Accelerated Mobile Pages (AMP) represent a framework by Google designed to construct web pages with the intention of improving loading speed and enriching the user experience.
Introduced by Google in 2016, Accelerated Mobile Pages (AMP) stand as a dynamic framework for crafting web pages.
While the primary objective of accelerated mobile pages remains the augmentation of loading speed and user experience, there exist several accompanying obstacles that have hindered the widespread adoption of AMPs as a webpage model.
It's worth noting that AMPs coexist alongside regular mobile pages, implying that content typically exists in two distinct versions. These two variants are subsequently linked using a specialized header tag. This tag is utilized by GoogleBot (and potentially other entities in the future) for indexing purposes.
For content creators, the appeal of accelerated mobile pages can be attributed to two core factors:
In an era where delayed page loading could translate to a permanent loss of potential new users, adopting AMPs elevates the status of your webpage significantly.
This is particularly pertinent for websites characterized by intricate and suboptimal code, or those that engage with other content as part of their platform.
Implementing AMPs demands minimal investment, while delivering a substantial enhancement to user experience, given that Google shoulders a substantial portion of the workload.
Conversely, if your website boasts a well-structured architecture and consistently delivers optimal performance, AMPs also play a pivotal role in amplifying visibility within Google search results.
Within the AMP Carousel, introduced shortly after the inception of the AMP framework, queries related to news are positioned prominently in the upper echelons of result pages. This preferential treatment can be attributed to the noteworthy endorsements of AMP by prominent entities, excluding Facebook and Apple.
Since their debut a few years ago, criticism from both the web development and publisher communities has been primarily directed at two key areas:
Due to its relatively constrained framework, AMPs do not facilitate direct clicks on a publisher's content from within the AMP itself. Instead, users are redirected back to Google search results, resulting in the potential diversion of a brand's traffic. This redirection also poses challenges in measuring website performance over the long term.
Another substantial concern with AMPs is their intricate monetization process. The aforementioned diversion of brand traffic culminates in diminished visitor numbers and subsequently reduced revenue streams. Unfortunately, as numerous publishers adopted AMPs to capitalize on their visibility within Google search, they swiftly encountered these repercussions.
These factors have contributed to the gradual uptake of AMPs compared to other emerging technologies. While the benefits for user experience and page visibility are evident, the existence of numerous challenges necessitates concerted efforts before AMPs can truly rise to prominence in the realm of mobile web development.
Ad stacking is a deceptive practice in mobile advertising where multiple ads are layered or 'stacked' in a single ad space. In this scenario, only the topmost ad is visible to the user. However, a click or an impression is falsely registered for every ad in the stack. This deceptive practice leads advertisers to unwittingly pay for multiple, non-genuine impressions and/or clicks, even though the user only sees one ad.
This fraudulent tactic is particularly prevalent in Cost Per Mille (CPM) campaigns, where advertisers pay per thousand impressions. Ad stacking significantly inflates impression counts, thereby defrauding advertisers. In click-based campaigns, ad stacking often overlaps with click spam, another fraudulent activity where fake clicks are generated.
Ad stacking is executed in various sophisticated ways. One common method involves a fraudulent publisher's script that, instead of serving a single ad, stacks multiple ads into one ad unit. To the user, only one ad is visible, while many others, set to near-zero opacity, are hidden behind it.
Another method involves using a static image as a placeholder that the user sees, while in the background, a video ad plays, continuously generating ad calls and thus impressions. Some fraudsters implement a rotating banner system, where invisible ads are continuously auctioned and swapped behind a visible ad. These ads, although never seen by the user, trigger the pixel count necessary for registering an ad impression, resulting in advertisers paying for these illegitimate impressions or clicks.
Ad stacking poses significant ethical and financial challenges in the digital advertising industry. It not only leads to financial losses for advertisers but also undermines the integrity of digital advertising metrics. Detecting and mitigating ad stacking is crucial for maintaining transparency and trust in digital advertising ecosystems.
An ad exchange is a real-time, online marketplace that enables advertisers and publishers to buy and sell advertising space and impressions. Advertisements such as display, video, and native ads can be bought and sold on ad exchanges, and can be displayed on both mobile and desktop platforms.
Ad exchanges typically operate programmatically, automating much of the advertising buying process. Ad networks and other entities can directly purchase ad impressions that appear on websites or apps marked as ad space. Advertisers can use demand side platforms (DSP) to connect to ad exchanges and use audience data to determine whether the ad space is relevant to their campaign. They can then purchase the space in real-time and bid on it instantly. These decisions can be made manually, or automatically using algorithms that analyze demographic and user data to find the best value for advertisers.
Agencies, brands, and games are among the entities that use ad exchanges. Brands and games often have in-house programmatic buying teams that work directly with DSPs. Demand-side platforms are programmatic platforms that help agencies and brands efficiently buy ad space, acting as the "bidders" in the auction. They use sophisticated algorithms to determine what to pay and when to bid for ad space that meets the requirements of a campaign. Supply-side platforms, on the other hand, are specialized networks that focus on aggregating digital inventory and are traditionally responsible for holding programmatic auctions.
The supply path optimization (SPO) is the main value driver of an ad exchange, which is an industry-wide effort to bring demand as close to supply as possible. SPO removes irrelevant nodes in the chain, such as SSPs, agencies or even DSPs in some cases. This leads to removing margins and increasing brand buying power, resulting in less "specialized" players. DSPs now take on more of the capabilities of SSPs, while SSPs build out DSP capabilities, and publishers, such as ironSource, often act as SSPs.
Ad exchanges offer several benefits for advertisers, such as the ability to choose the best ad placements for their campaigns, run cost-effective advertising through price settings and advanced bidding capabilities, get control over ad frequency to avoid overexposure, and avoid ad inventory if they don't want to be associated with a particular publisher.
Developers also benefit from ad exchanges by gaining control over ad placements and units, ensuring brand safety by getting transparency into ad fraud or offensive ads, setting minimum costs for ad space, and getting access to a large pool of agencies and advertisers looking to advertise in their app.
Ad networks and ad exchanges are different. Ad networks aggregate inventory from a range of publishers, while ad exchanges directly connect publishers and advertisers, allowing buyers to see the exact price for impressions.
As a mobile advertiser, you can leverage mobile attribution data to gain valuable insights into the effectiveness of your ad exchange purchases. This data provides you with indisputable and authoritative information that empowers you to allocate your budget more intelligently. By optimizing your app's performance, return on ad spend (ROAS), and customer lifetime value (LTV), you can make the most of your ad spend and achieve your marketing objectives.
When exploring your ad exchange options, keep in mind that there is a foolproof way to maximize your investment in digital advertising. By identifying your most valuable and least valuable digital investments, you can allocate your budget more effectively and achieve better results.
An ad exchange is like a pool of ad impressions, where publishers upload their ad impressions and advertisers select the impressions they want to purchase. It helps to streamline the process of buying and selling ad space, making it more efficient and transparent while maximizing profitability. Advertisers can effectively reach their desired target audience with the most relevant, data-driven context, and publishers can receive the best price for their ad space.
An ad exchange has the ability to analyze real-time data from various sources such as user mobile identifiers, device type, ad position, demographics, and purchasing behavior to determine whether or not to bid on the impression and at what price. This enables advertisers to easily purchase ads across multiple sites instead of negotiating directly with specific publishers. In an ever-evolving and complex advertising market, an ad exchange offers a more streamlined and transparent solution for buying and selling digital advertising.
Ad monetization is the process of generating revenue for mobile apps through advertising. While it may seem straightforward to insert ads into the app and collect revenue, maximizing ad monetization can be quite challenging for app businesses.
To calculate the total ad revenue, you need to combine the money earned from each source, which, when working on a large scale, will come from several ad networks that define views, clicks, and conversions in different ways. Furthermore, different creatives and placements provide varying revenue opportunities. Sophisticated mobile publishers also analyze their most valuable ad monetization users, their return on investment (ROI) of acquired users, and the lifetime value (LTV) of their users.
Since many app businesses also earn revenue through in-app purchases, these figures must be included to accurately determine the ROI of acquired users.
For many free or freemium apps, ad monetization is their primary source of revenue. One of the most common types of ad monetization is in-app advertising (IAA), which relies on ad networks to connect developers and app businesses with advertisers. Ad monetization enables apps to monetize all users, not just the small fraction who will pay. In addition, advertisers can target users with personalized ads on platforms like Android (and pre-iOS 14 iPhone and iPad) to earn revenue for the apps.
Since apps collect first-party data on an opt-in basis, they can provide valuable targeting parameters like gender and age, making it easier for advertisers to reach their ideal audience. While that level of targeting may not always be possible with iOS 14.5 and SKAdNetwork, contextual targeting can be used instead.
There are four main types of in-app ads: rewarded video ads, interstitials, offerwall ads, and native ads. Rewarded video ads provide users with a reward for watching a video ad, while interstitials are push ads placed within an app's interstices. Offerwalls are similar to rewarded videos and provide a reward for users completing a specific action. Native ads are in-app content that matches the form and function of the rest of the app.
To successfully monetize an app with advertising, app businesses must test and optimize each advertising option to determine the right mix for their audience. In many cases, native ads provide an optimal user experience as they fit seamlessly within the app experience. However, the other methods can also be effective for the right business. Many app businesses combine this revenue stream with other monetization methods to maximize their ROI.
Cost per mile (CPM) and cost per action (CPA) are two widely used bidding models for ad monetization. CPM is a bidding model where publishers pay for every thousand ad views, regardless of whether or not users interact with it. CPM works well for branding campaigns, as it increases brand awareness. High-traffic platforms are best suited for this type of bidding as it makes ad monetization easier.
On the other hand, with CPA, publishers only get paid when users click on the ad or install the advertised application. This model is commonly used for performance campaigns, where ad strategy is crucial for revenue. Publishers earn money based on user clicks or form fill-outs, but not for actual product sales. Accurate targeting can result in more user actions and higher income.
Effective cost per mile (eCPM) is a standard measurement system that publishers can use to assess the effectiveness of advertising. To calculate the eCPM while running a CPA ad, all paid CPAs need to be divided by 1000. For instance, if you pay $10 for every game installation, divide 10 by 1000 to get a cost of $0.01 for every impression. This number helps in calculating the general revenue from the running ad.
When running an app, publishers might experience low eCPM. Several reasons could contribute to this, including a lack of advertisers for traffic, slow website performance, or malfunctioning pages. Fixing these mistakes requires selecting an advanced Supply-Side Platform (SSP) that can optimize and check your ad.
Ad mediation works by routing ad requests to multiple ad networks, and then selecting the highest paying ad to display to the user. The ad mediation platform typically includes tools for optimizing ad performance and revenue, such as eCPM (effective cost per thousand impressions) optimization, ad network failover, and ad network waterfalls. When a user opens an app or website that is using ad mediation, the platform sends an ad request to multiple ad networks. Each ad network then returns an ad and the eCPM (effective cost per thousand impressions) they are willing to pay for it. The ad mediation platform then selects the highest paying ad to display to the user.
Additionally, ad mediation platforms can also use ad network failover, which allows it to route ad requests to backup ad networks in case the primary ad network is unavailable. They can also use ad network waterfalls which routes ad requests to multiple ad networks in a predefined order, based on the eCPM (effective cost per thousand impressions) offered by each network.
Ad mediation platforms also provide analytics and reporting features to help app and website publishers to track and optimize the performance of their ads, and make data-driven decisions about which ad networks to work with.
Ad mediation platforms can help app and website publishers in several ways:
Increased revenue: Ad mediation platforms can help boost ad income by optimizing ad performance, administering multiple ad networks, and furnishing access to a wider selection of ad inventory.
Simplified monetization: Ad mediation platforms can make the process of monetizing content easier by providing a single integration point for multiple ad networks.
Analytics and reporting: Ad mediation platforms provide analytics and reporting features that enable app and website publishers to track and enhance the performance of their advertisements. This can assist them in making decisions based on data about which ad networks to collaborate with.
In-app advertising: Ad mediation platforms can help app developers and mobile marketers to promote their apps through in-app advertising, which can augment app downloads and user engagement.
Header bidding and waterfall bidding: Ad mediation platforms can also bolster In-app header bidding and waterfall bidding which allows multiple ad networks to tender for ad inventory in real-time, before the ad request is sent to the primary ad server. This can increase rivalry for ad inventory and lead to higher ad revenue for publishers.
Ad Network failover: Ad mediation platform can also provide failover feature which allows it to send ad requests to reserve ad networks in case the primary ad network is inaccessible, which can ameliorate user experience and amplify the fill rate for ad inventory.
Ad networks are an important aspect of digital marketing. They connect advertisers with publishers, who then display the ads on their websites or apps. In this guide, we will explain what ad networks are, how they work, and why they are important.
An ad network is a platform that connects advertisers with publishers. Advertisers pay ad networks to display their ads on websites or apps that are part of the ad network's inventory. Ad networks provide a variety of targeting options, such as demographics, interests, and behavior, to help advertisers reach their target audience.
Ad networks work by collecting ad inventory from publishers and making it available to advertisers. Publishers can be websites or apps that have space available for ads. Advertisers can then bid on this inventory, and the highest bidder's ad will be displayed on the publisher's website or app.
Ad networks use a variety of targeting options to ensure that ads are displayed to the right audience. These targeting options include demographic information, interests, and behavior. Ad networks also use algorithms to optimize ad delivery and ensure that advertisers get the best possible return on their investment.
Ad networks are important for both advertisers and publishers. Advertisers can use ad networks to reach a large and targeted audience, while publishers can use ad networks to monetize their websites or apps. Ad networks also provide a way for advertisers to manage their ad campaigns and track their performance.
In conclusion, ad networks are a crucial component of digital marketing. They provide a platform for advertisers to reach their target audience and for publishers to monetize their websites or apps. By understanding how ad networks work, advertisers and publishers can make the most of this important tool.
Here is a diagram that illustrates the process of how ad networks work:
graph LR; A[Advertiser] --> B[Ad Network]; B --> C[Publisher]; C --> D[User];
The advertiser pays the ad network to display their ad on the publisher's website or app. The ad network uses targeting options to ensure that the ad is displayed to the right audience. The user then sees the ad and can click on it, which generates revenue for the publisher and provides a return on investment for the advertiser.
Ad podding is a digital advertising technique where multiple advertisements are grouped together and played sequentially during a single break. This strategy is predominantly utilized by Over-The-Top (OTT) platforms and online content publishers to enhance fill rates and advertising revenue.
Ad podding arranges a series of ads to play one after another during an ad break. This approach minimizes the need for multiple ad requests, streamlining ad delivery to the viewer. It's applicable to various types of ad placements such as pre-roll, mid-roll, and post-roll.
This method resembles traditional TV commercial breaks where ads are played back-to-back. The primary difference lies in its application, which is tailored for digital video content, especially for long-form Video on Demand (VOD) on OTT platforms.
Introduced by the Interactive Advertising Bureau (IAB) in VAST 3.0 in 2012, ad podding initially saw slow adoption. However, its potential was soon recognized by OTT service providers and content owners, leading to widespread implementation, notably by YouTube in 2018. Now, it's a common strategy used by established OTT platforms to optimize ad inventory and increase revenue from VOD and live streaming content.
An ad pod consists of several ad slots, each with a specific sequence for play. Publishers can auction each ad slot within a pod, often commanding higher prices for prime positions, like the first slot in the sequence.
To viewers, ad podding appears as a straightforward sequence of ads. However, the process involves sophisticated mechanisms, including the use of a video CMS for setting up ad tags (each representing a slot in the pod) and managing them through Client-Side Ad Insertion (CSAI) or Server-Side Ad Insertion (SSAI). Tags are assigned unique sequence numbers for orderly play, and various scenarios are managed to ensure seamless ad delivery.
Revenue Enhancement: By serving multiple ads in a single request, ad podding increases the ad fill rate and allows for strategic pricing of ad slots. This leads to maximized utilization of ad inventory and enhanced revenue generation.
Improved Viewer Experience: Ad podding reduces repetitive ad play and enhances viewer engagement through creative deduplication and control over ad quantity and duration. This results in a more enjoyable viewing experience with lower latency and faster loading times.
Ad podding solutions offer customization in terms of pod length, the number of ads per pod, and individual ad duration. Publishers can use ad analytics to fine-tune these parameters. The bidding process for ad pods and slots enables both publishers and advertisers to optimize their strategies, with publishers setting floor prices to boost revenue.
Ad podding may not be suitable for short-form content or when other monetization models like subscriptions or paywalls are in place. It's most effective for long-form VOD and live streams.
An ad tag is a piece of code that is inserted into a website or app to display advertisements. When someone visits the website or app, the ad tag tells the ad server to serve up an ad, which is then displayed on the website or app. Ad tags can be used to display a variety of different types of ads, including banners, pop-ups, and video ads.
Ad tags are used by advertisers to reach specific audiences and to track the performance of their ad campaigns. Digital marketing advertisers use ad tags to display ads on websites and apps that have a relevant audience in the hopes of getting those users to take some desired action, such as clicking on the ad, making a purchase, or signing up for a newsletter..
Advertisers might use ad tags to display a variety of different types of ads, including banner ads, video ads, and rich media ads. They might also use ad tags to target specific audiences, such as users who have previously shown an interest in similar products or services.
Ad tags are also used by website and app owners to monetize their content. By inserting ad tags into their websites or apps, they can display ads and earn revenue from those ads.
Overall, ad tags are used by advertisers and website and app owners as a way to reach specific audiences and to track the performance of ad campaigns. They are an important tool in the world of digital advertising.
Below is an example of a simple JavaScript ad tag that could be used to display a banner ad on a website:
<script type="text/javascript">
var ad_tag = '<!-- Beginning of tag -->\
<a href="http://www.bigabid.com">\
<img src="http://www.bigabid.com/banner.jpg" alt="Banner ad">\
</a>\
<!-- End of tag -->';
document.write(ad_tag);
</script>
This ad tag is written in JavaScript and is inserted into the HTML code of a website. When someone visits the website, the ad tag tells the ad server to serve up a banner ad, which is then displayed on the website. The banner ad is a clickable image that, when clicked, takes the user to the website specified in the ad tag.
There are several different types of ad tags that can be used to display ads on a website or app. Below are a few examples:
Ads.txt stands for Authorized Digital Sellers and is an initiative developed by the Interactive Advertising Bureau (IAB) to counter ad fraud, including domain theft, alleged domain hijacking, and illegitimate inventory arbitrage.
Ads.txt is a text file that publishers host on their web servers, listing companies authorized to sell their ad inventory. Advertisers and media buyers can use this information to validate sellers in a bid request, ensuring they don’t spend their ad budget on counterfeit ad inventory and unauthorized reselling.
The ads.txt file is located in the root directory of a publisher’s website (websitename.com/ads.txt). A publisher’s ads.txt file can be viewed by entering the URL into a browser. Google Ad Manager can also be used to verify if a seller has a valid ads.txt file.
No, ads.txt is not mandatory. However, publishers are increasingly adopting ads.txt to have more control over who can sell ads on their sites, preventing counterfeit inventory from entering the market.
As more advertisers use ads.txt to validate a publisher’s reliability while protecting themselves from spoofing attempts or fraudulent inventory, we can expect more publishers to implement ads.txt on their sites to build trust and drive ad sales.
Digital ad fraud cost advertisers worldwide $65 billion in 2021. Ads.txt helps verify sellers so brands can avoid spending their budget on counterfeit inventory, ensuring more money goes to legitimate publishers.
Ads.txt makes digital advertising more transparent by preventing unauthorized reselling in programmatic advertising. Buyers can verify which supply-side platforms (SSP) are authorized to sell a publisher’s inventory, down to the exact web spot. Advertisers and media buyers can also automate the process of screening dealers based on information in ads.txt files, minimizing the risks of doing business with unauthorized resellers.
Ads.txt helps brands protect their reputation by ensuring their ads only appear on trustworthy websites.
Ads.txt is an effective tool for combating ad fraud, specifically domain spoofing and inventory arbitrage. Domain spoofing occurs when a request originates from one site, but the ad is placed on another lower-quality website. With the help of ads.txt, advertisers can identify which Supply Side Platforms (SSPs) are authorized to sell what inventory, thereby avoiding fake impressions and improving inventory reporting accuracy.
Inventory arbitrage, while technically not ad fraud, is a dishonest practice that erodes trust in the industry. It involves a third party purchasing impressions and then repackaging and reselling them at a higher price. Ads.txt discourages inventory arbitrage by listing authorized resellers of a publisher’s inventory. This allows publishers to protect their reputation in the open market and maintain control over their partnerships.
Both domain spoofing and inventory arbitrage deceive programmatic advertising platforms, such as ad exchanges, into believing that high-quality inventory is being accessed, when in reality, the ads appear on dubious websites or are viewed through a covert application designed to generate fake impressions.
Apart from the benefits of cutting fraud, enhancing transparency, and building trust, ads.txt is a user-friendly tool. Publishers can implement an ads.txt file with minimal technical expertise, create and upload one in just a few minutes, and easily maintain it by adding or removing sellers at any time to keep information up-to-date.
The ads.txt process is secure since only website owners can upload and update the file. As a result, publishers can maintain control over their partnerships, prevent unauthorized reselling, and safeguard their reputation.
Ads.txt is a vital tool in the programmatic advertising ecosystem, serving as a public record of authorized digital ad sellers. Publishers upload the ads.txt file onto their website, confirming domain ownership and verifying partner accounts, such as ad exchanges and SSPs, eligible to sell their ad inventory. Programmatic platforms can also integrate ads.txt files to confirm which publishers' inventory they are authorized to sell.
The IAB Tech Lab recently released a crawler that can efficiently pull ads.txt files from publisher websites, enabling media buyers and advertisers to validate a large amount of inventory information quickly, compile a list of authorized sellers, and streamline the verification process. When an advertiser receives a bid request from the publisher's site, it can check the publisher's account ID against the ads.txt file to ensure that the publisher and inventory are legitimate. If the advertiser can't validate the publisher's account, it may choose not to bid on the inventory to safeguard its budget.
For example, each domain publishes ads.txt on their web server and lists exchanges and advertisers that are authorized to sell their inventory, including the publisher's seller account IDs within each of those advertisers.
To use ads.txt with AdSense, publishers can sign in to their AdSense account and follow the instructions provided. To use ads.txt with Wordpress, publishers need to install and activate the Ads.txt Manager plugin, configure the plugin settings, and add lines to declare each authorized platform or reseller. For instance, publishers can add the following line to declare Google Adsense as authorized: google.com, pub-0000000000000000, DIRECT, f08c47fec0942fa0. It's crucial to keep ads.txt files current to ensure their accuracy and effectiveness and prevent scammers from exploiting unaudited ads.txt files using the 404bot.
App-ads.txt is a text file integrated by app developers/publishers into their developer websites, serving as a comprehensive list of authorized vendors permitted to sell their inventory. This innovative tool addresses various challenges of programmatic advertising, such as opacity and security concerns.
Officially referred to as "Authorized Digital Sellers for Mobile Apps," App-ads.txt provides app developers and publishers with the means to grant specific ad networks and supply-side platforms (SSPs) permission to sell their inventory. It is essentially a text document that enlists legitimate ad tech vendors who possess the authorization to distribute the publisher’s digital ad inventory.
Publishers can upload the app-ads.txt to their developer website, while developers can analyze the file by crawling the developer website. This process enables developers to evaluate bid requests from ad networks and SSPs listed within the file.
The Interactive Advertising Bureau (IAB) Tech Lab introduced app-ads.txt in November 2018 as an extension of the ads.txt file to the realm of mobile in-app and OTT advertising. This release aimed to enhance transparency between buyers and sellers within programmatic advertising. Major app stores, such as Google Play and the Apple App Store, have embraced app-ads.txt due to its advantages for both publishers and advertisers.
Mobile app developers can either upload the app-ads.txt file to their developer website or utilize online hosting platforms specialized for app-ads.txt. Subsequently, they integrate the developer website URL into each app store listing.
Buyers and advertisers can navigate individual app stores to identify the developer website associated with a particular app. Armed with this URL, advertisers can explore the developer website to pinpoint vendors authorized to sell the publisher’s ad inventory. This process aids advertisers in making informed decisions when it comes to accepting or rejecting bid requests from ad networks or SSPs.
1. Enhanced Security App-ads.txt acts as a formidable defense against ad fraud. By mitigating direct interactions between buyers, sellers, and ad tech vendors, this tool combats issues like unauthorized reselling, counterfeit inventory, and domain spoofing. Consequently, the programmatic advertising ecosystem becomes more secure, safeguarding publishers’ revenue and advertisers' investments.
2. Amplified Transparency and Trust App-ads.txt fosters a transparent ad buying and bidding process. Advertisers can validate claims made by ad tech vendors by visiting the publisher's developer website to ascertain their inventory access. Furthermore, this tool instills confidence and reliability in modern advertisers, as publishers providing the file earn their trust.
3. Ensured Brand Safety By adhering to app-ads.txt, advertisers guarantee that their campaigns run on legitimate and relevant ads, preserving their brand reputation. Publishers, in turn, prevent unauthorized access to their inventory and bolster their credibility.
4. Increased Revenue Efficient implementation of app-ads.txt eradicates concerns about revenue loss due to counterfeited apps or fabricated inventory. Publishers can access every dollar spent by advertisers on their inventory. Similarly, advertisers benefit from reduced risks associated with misleading ad tech vendors, ultimately enhancing their campaign performance and return on ad spend.
The app-ads.txt file comprises entries for various ad networks and SSPs, each encompassing ad system domains, publisher account IDs, account types/relationships, and certificate authority IDs. For instance, a record might look like this:
Google.com, pub-00000000000000000000, DIRECT, f08c47fec0942fa0 Publishers follow a series of steps to implement app-ads.txt effectively, which involve providing their website URL in app store listings, obtaining app-ads.txt records from vendors, compiling them into a file, and uploading it to their root domain.
Key Insights App-ads.txt, introduced by the IAB, elevates transparency and security in programmatic advertising. By extending the benefits of ads.txt to the mobile in-app and OTT advertising spheres, it ensures authorized sales, curbing ad fraud and bolstering trust. Advertisers can make informed decisions, publishers preserve brand integrity, and both parties reap the rewards of a secure and transparent ecosystem.
Implementing App-ads.txt The implementation of app-ads.txt requires careful execution to maximize its benefits for both advertisers and publishers. This process involves several key steps:
Website Integration: Developers must provide their website URL within each app store listing. This step is essential for linking the app to its corresponding developer website.
Vendor Collaboration: Publishers need to reach out to various vendors, including ad networks and SSPs, to request their app-ads.txt records. These records should follow the format of ad system domain, publisher ID, account type/relationship, and certificate authority ID.
Record Compilation: Using a text editor, such as Notepad, publishers compile the received app-ads.txt records into a single file. Each record is typically listed on a new line.
File Naming and Hosting: The compiled file is then saved with the name "app-ads.txt" and uploaded to the publisher's root domain. The URL would resemble something like "https://example.com/app-ads.txt".
In the dynamic landscape of digital businesses, app retention rate stands out as a pivotal metric that determines the success and sustainability of any mobile application. App retention rate, often referred to as user retention rate, specifically measures the percentage of customers who continue to engage with and use a mobile app over a defined period. This metric is particularly significant for subscription-based services, offering crucial insights into customer satisfaction, product appeal, and revenue stability. This comprehensive guide will delve deep into the nuances of app retention rate, its importance, calculation methods, and strategies to enhance it.
Understanding the app retention rate is essential for businesses for several reasons:
Revenue Stability: App retention rate directly influences a company's revenue stream. Satisfied, retained customers are more likely to continue their subscriptions, ensuring a stable income for the business.
Customer Satisfaction: High app retention rates are indicative of satisfied users. When users find value in an application, they are more likely to stay engaged, leading to positive reviews and word-of-mouth referrals.
Cost-Efficiency: Acquiring new customers can be significantly more expensive than retaining existing ones. High app retention rates reduce the need for aggressive user acquisition strategies, saving valuable marketing resources.
Product Improvement: By analyzing patterns within the app retention rate, businesses can identify specific features or stages where users tend to drop off. This valuable feedback aids in refining the user experience and enhancing the app's overall appeal.
Calculating the app retention rate involves comparing the number of users at the beginning of a specific timeframe with the number of users at the end of that period. The formula is as follows:
App Retention Rate=(Initial Users−Churned UsersInitial Users)×100%App Retention Rate=(Initial UsersInitial Users−Churned Users)×100%
Here, "Initial Users" represent the number of users at the start of the period, and "Churned Users" represent the users who ceased using the app during the same period.
Enhancing app retention rate requires a strategic approach and a deep understanding of user behavior. Here are some effective strategies to boost app retention rates:
Personalized User Experience: Tailoring the app experience based on user preferences and behavior fosters a sense of belonging and encourages prolonged engagement.
Regular Updates: Continuous improvement and the addition of new features keep the app fresh and exciting for users, reducing the likelihood of them switching to competitors.
Effective Onboarding: A seamless and intuitive onboarding process ensures that users quickly understand the app's value proposition, reducing early churn rates.
Push Notifications: Thoughtful and relevant push notifications can re-engage users, reminding them of the app's benefits and encouraging return visits.
Incentives and Rewards: Implementing loyalty programs, discounts, or exclusive content for long-term users creates incentives for continued app usage.
Feedback Loops: Actively seek feedback from users, addressing their concerns, suggestions, and grievances promptly. Users who feel heard are more likely to stay loyal.
Community Building: Encourage users to interact with each other within the app, fostering a sense of community. Social engagement often leads to higher retention rates.
Cohort analysis is a powerful tool for understanding app retention rates in specific user segments. By dividing users into cohorts based on criteria such as acquisition date or user behavior, businesses can gain valuable insights into user retention patterns.
Acquisition Cohorts: Dividing users based on acquisition date helps in understanding the retention rate of users acquired during specific marketing campaigns or timeframes. This information is invaluable for refining marketing strategies.
Behavioral Cohorts: Grouping users based on specific interactions within the app provides insights into which features or activities lead to higher retention rates. This data can inform app redesigns and marketing efforts.
In the competitive world of mobile applications, understanding and optimizing app retention rate is paramount to long-term success. By adopting data-driven strategies, staying responsive to user feedback, and continuously refining the user experience, businesses can not only retain existing users but also attract new ones through positive word-of-mouth and app store reviews. As the digital landscape evolves, businesses that prioritize user satisfaction and retention will undoubtedly emerge as leaders, shaping the future of the app economy.
The concept of app stickiness revolves around the degree to which users interact with an app on a regular basis. The higher the level of user engagement, the stronger the app's stickiness factor.
App stickiness describes an app's ability to retain a dedicated user base, minimize user attrition, and maintain high levels of engagement. This is achieved by offering a distinctive and exceptional user experience that differentiates the app from its competitors.
Central to stickiness is the app's value and relevance in users' lives. Ensuring an app's stickiness implies that users are motivated to revisit it frequently, opening up more opportunities for revenue generation through in-app purchases or advertising.
A practical gauge of app stickiness involves the DAU/MAU ratio. By aligning the count of daily active users (DAU) with that of monthly active users (MAU), this ratio gauges the proportion of monthly users who engage with the app within a 24-hour timeframe.
The formula serves a dual purpose: predicting traction and potential revenue over time, while also indicating how valued the app is among users based on their frequency of return.
Here's how the app stickiness ratio is computed:
Consider a scenario where a total of 2,000 users are active on a daily basis (DAU), and the app accumulates 8,000 monthly active users (MAU) in August. The stickiness ratio for that month would be 25%.
To boost an app's stickiness, certain common strategies employed by sticky apps can be adopted:
Identify Weak Points: Begin and conclude your app's improvement journey with user needs and expectations. Involve your target audience in development decisions by seeking input on issues, broken experiences, and desired features.
Optimize Onboarding Experience: The initial onboarding experience is crucial. It's akin to a first meeting, where your app's value and problem-solving capabilities must be swiftly demonstrated. A seamless, clear, and engaging onboarding process can enhance stickiness, user lifetime value (LTV), and retention rates.
Implement Personalization: Allowing users to personalize their app experience can significantly impact engagement and loyalty. Personalization not only adds relevance but also strengthens emotional ties to the app.
Analyze Success Patterns: Identify successful app experiences and dissect their elements. Recognize your "power users" – those with high LTV and engagement – and understand their usage patterns to replicate what keeps them engaged.
Foster Brand Loyalty: Deliver unique and practical value to users to establish a loyal connection. Personalized, non-salesy content builds trust and enhances stickiness.
Meaningful User Interaction: Engagement efforts should extend beyond the app. Leverage various communication channels for timely, personalized push notifications, informative emails, and valuable educational content.
Continuously Monitor Stickiness: App stickiness is an indicator of long-term health. Regularly measuring your app's stickiness and evaluating competitors' can shape your development strategy.
In conclusion, a sticky app maintains a devoted user base due to its exceptional user experience. Utilize the DAU/MAU ratio for insights and improvement. Enhance stickiness by addressing weak points, perfecting onboarding, personalizing experiences, analyzing power users, fostering brand loyalty, engaging users effectively, and consistently tracking engagement levels.
App store analytics play a pivotal role in providing app owners with invaluable insights into the performance and reception of their applications.
App store analytics encompass a spectrum of data insights crucial for gauging an app's success. This data can be categorized into two primary tiers: basic insights readily available through app stores and more intricate analytics provided by third-party tools.
Basic app store analytics serve as an entry point, furnishing beginners with essential key performance indicators (KPIs) including download metrics, rankings, device demographics, geographical distribution, and revenue streams.
Third-party analytic tools, such as App Annie and Sensor Tower, offer a deeper dive into app performance. They enable app owners to conduct comparative analyses against competitors, employing various slicing and filtering mechanisms for enhanced granularity.
Key metrics measurable through advanced app store analytics encompass revenue streams, download rates, update frequencies, rankings, user reviews, and keyword-specific rankings.
Advanced app store analytics further facilitate comprehensive competitive analyses and vertical assessments. They provide in-depth reports and aggregated data tailored to individual app stores, making them indispensable for owners managing multiple applications or diverse app versions.
App store analytics encompass a spectrum of data insights crucial for gauging an app's success. This data can be categorized into two primary tiers: basic insights readily available through app stores and more intricate analytics provided by third-party tools.
Basic Insights:
Basic app store analytics serve as an entry point, furnishing beginners with essential key performance indicators (KPIs) including download metrics, rankings, device demographics, geographical distribution, and revenue streams.
Third-Party Tools:
Third-party analytic tools, such as App Annie and Sensor Tower, offer a deeper dive into app performance. They enable app owners to conduct comparative analyses against competitors, employing various slicing and filtering mechanisms for enhanced granularity.
Key Metrics:
Key metrics measurable through advanced app store analytics encompass revenue streams, download rates, update frequencies, rankings, user reviews, and keyword-specific rankings.
Advanced Insights:
Advanced app store analytics further facilitate comprehensive competitive analyses and vertical assessments. They provide in-depth reports and aggregated data tailored to individual app stores, making them indispensable for owners managing multiple applications or diverse app versions.
Optimizing Performance:
App store analytics empower app owners to optimize their applications for better performance. By tracking KPIs such as user engagement, retention rates, and revenue generation, developers can make informed decisions regarding updates, feature enhancements, and marketing strategies.
Competitive Analysis:
Thorough competitor analysis is made possible through app store analytics. By benchmarking their apps against industry rivals, developers can identify strengths, weaknesses, and market trends, thus refining their own offerings for greater competitiveness and market penetration.
User Feedback Integration:
App store analytics allow for seamless integration of user feedback into app development cycles. By monitoring user reviews, ratings, and sentiment analysis, developers can address user concerns, improve user experience, and cultivate positive app perceptions.
App Tracking Transparency (ATT), succinctly referred to as ATT, stands as Apple's proactive privacy paradigm that mandates every iOS app to solicit user permission for data sharing. This is orchestrated through a pop-up interface, proffering users the choice to either authorize or decline data tracking.
What exactly constitutes App Tracking Transparency (ATT)? Emerging subsequent to the iOS 14 launch (and enforced post iOS 14.5), the ATT privacy framework was introduced across the gamut of Apple devices. Its principal aim was to curtail the extent to which app developers could disseminate user data to external entities. This initiative has yielded consequential ramifications for the mobile advertising sector.
Preceding ATT, all iPhone users were involuntarily enrolled in data tracking, unless they had proactively opted out via the Limit Ad Tracking feature. In such instances, developers and marketers could access user-specific data and attribution through a distinct iOS advertising identifier labeled as IDFA.
Under the ATT umbrella, app users are compelled to actively opt into data tracking via a pop-up displayed by the app. Owing to the majority of users opting out, this has posed a considerable challenge for advertisers, publishers, and app developers alike, rendering the task of targeting precise demographics and refining campaigns based on high-performance user data significantly more onerous. Further elaboration on this will follow.
How does the interface of App Tracking Transparency manifest? The crux of ATT lies in the in-app pop-up, colloquially known as the ATT prompt. This interface queries users if they wish to "permit the app to track your activity across other companies' apps and websites." Users possess the liberty to abstain or assent, with the default setting being opt-out. While the wording of this prompt remains immutable, there exist strategies to augment opt-in rates, which we shall delve into later.
Not a Mandatory Fixture for Apps Apps aren't under obligation to showcase the prompt since it functions on an opt-in basis. Should developers opt out of this display, they forego the collection of user-specific data. The ATT prompt serves as an avenue for apps to accumulate user-centric data, which in turn can augment performance and furnish insights for benchmarking, extrapolation, and more. Consequently, a substantial majority of apps (nearly 70%) opt to feature the prompt.
Prior to Apple's resolute focus on privacy, app developers and publishers reveled in unrestricted access to copious amounts of data. Apple operated on the Limited Ad Tracking (LAT) model, allowing users to renounce personalized advertising.
Though available to most users, a significant majority (around 70%) refrained from exercising the option to opt out of tracking. This facilitated publishers and advertisers in trading and disseminating user data among media companies, apps, and advertisers. This ecosystem engendered finely targeted ad campaigns based on behavior, demographics, and interests. Alas, optimization thrived at the expense of privacy.
Transitioning to ATT and Its Implications
Although the shift from iOS' opt-out model to an opt-in model did diminish tracking rates, global adoption of ATT still stands at a commendable 46%, albeit this figure pertains only to users who encountered the prompt. For advertisers, the crux of the challenge rests in the scarcity of IDFA attribution.
Navigating the Unfamiliar Data Terrain User-specific data and attribution have been pivotal for optimizing ad campaigns. Conversely, the dearth of data has proven detrimental for advertisers and publishers accustomed to working with granular user-level data, now rendered incapable of orchestrating finely targeted campaigns akin to the past.
In this context, it remains pivotal to acknowledge the ongoing industry transition. As we acclimate to the domain of aggregate-level data insights, the primary objective, innovation persists, and measurement is anticipated to be largely sustained. (Refer here for further insights.)
The initial challenge arises from a substantial user segment that eludes tracking. Users who previously opted out of personalized advertising (LAT users) are automatically classified as 'denied' to advertisers today, constituting over 30% of global iOS devices.
Moreover, 14% of Apple users employ restricted devices designated for minors, unknown age demographics, or educational purposes. Additional limitations on tracking could emanate from certain corporate-owned devices.
Certain app developers express apprehension regarding the intentionally unwelcoming verbiage ("allow app to track your activity across other companies' apps and websites"), which could potentially exacerbate user churn and impede user experience.
A noteworthy stumbling block involves dual consent when advertising across different apps. Users are required to provide consent twice for user data exchange between two distinct entities, effectively sealing the attribution loop. Consent must emanate both from the advertiser and the publisher. This dual opt-in dynamic significantly contributes to the diminished IDFA attribution rates, despite relatively high ATT opt-in rates.
Having delineated the prevailing challenges presented by ATT, let's embark on a journey through methodologies to elevate opt-in rates.
The keystone catalyst for elevated ATT opt-in rates is trust. An app with an established presence or a brand with inherent credibility garners greater user trust, rendering users more amenable to entrust their data. For nascent apps, cultivating a secure and trustworthy user experience is imperative.
Experimentation with diverse pre-prompt messaging emerges as a plausible approach. This involves an antecedent popup preceding the ATT prompt. Tailoring the messaging to accentuate the advantages of personalized advertising for users is pivotal. Conciseness, sincerity, and clarity should remain focal.
No universal panacea exists for determining the optimal prompt display timing. The decision hinges on user behavior and the value proposition your app extends. Identifying the funnel stage that aligns with your objectives is paramount.
Early Funnel: Inception of app usage, preliminary app session, early stage completion, initial app revisits Displaying the ATT prompt during the early funnel phase offers access to a substantial audience. This strategy gains efficacy when accompanied by high opt-in rates, substantiated by data indicating negligible churn or attrition due to ATT. However, this approach could be perceived as intrusive by new users.
Mid Funnel: Account establishment, app's inaugural value juncture, meaningful engagement Positioning ATT prompts within the mid funnel hinges on user actions within the app. This could coincide with a pivotal "AHA!" moment when users discern the app's intrinsic value. Timing the ATT prompt during this felicitous phase could capitalize on the positive user experience.
Lower Funnel: In-app purchases and beyond While this targets a narrower audience, engaging users in the lower funnel entails reaching a highly-trusted cohort. These individuals have already recognized the app's utility, evident through their purchases.
The optimal choice hinges on your objective, audience profile, and their familiarity with your brand or app. A singular best practice remains elusive, with trade-offs between audience scope, timely campaign optimization, and distinctive opt-in rates.
Android's Terrain: A Glimpse Unpacking Android's Trajectory Apple's proclamation was promptly followed by Google's announcement in June 2021. This announcement heralded heightened privacy measures for all Android devices, a policy slated for Android 12 and beyond.
Parallel to Apple's precedent LAT model, Google's update empowers users to disengage from personalized advertising.
With Google poised to sunset cookies in 2023, conjecture mounts that Google might eventually curtail user-level data exchange via its GAID (Google Advertising ID), akin to iOS' IDFA. Nonetheless, the constraints are anticipated to be comparatively less stringent than Apple's stipulations.
Now that we've unraveled the nuances and significance of ATT, let's delve into its implications for users. For iPhone, iPad, and tvOS users, evading ad tracking necessitates no action on their part.
Apple has enriched their informational guide "Day in the Life of Your Data," elucidating the benefits of ATT for ordinary users.
On your iPhone, access Settings, followed by Privacy. A conspicuous orange icon denoting Tracking will appear. Clicking on it reveals a toggle labeled "Allow Apps to Request to Track."
The master toggle dictates universal app tracking settings. Alternatively, users can independently designate tracking permissions for specific apps.
Should users alter their preference, it's seamless to inhibit the ATT prompt from appearing on iOS or iPadOS devices. The process mirrors activation, with the difference being the selection of the tracking toggle under Settings, Privacy, and Tracking. Simply toggling tracking on or off orchestrates the desired changes.
Crux of the Matter In a nutshell: App Tracking Transparency (ATT) necessitates iOS 14.5+ apps to secure user consent through a pop-up before sharing their data. Pre-ATT era enabled app developers and publishers to access copious user-level data. Although ATT opt-in rates are commendable, dual consent requisites and user experience hurdles pose challenges for attribution and campaign measurement. Strategically selecting the prompt display timing hinges on the app's value proposition and user behavior. Parallel to Apple, Google's Android is adopting augmented privacy measures, permitting users to opt out of data tracking. Armed with the insight into ATT's implications and mechanics, users can navigate their privacy preferences with greater clarity and control.
ARPDAU, or average revenue per daily active user, serves as a crucial key performance indicator (KPI) that evaluates app monetization for each active user on a daily basis. This metric is valuable for detecting the impact of app changes on its user monetization potential.
ARPDAU employs earnings derived from in-app purchases (IAP), subscriptions, and advertisements to compute the daily revenue per active user of the application.
Whether your app undergoes updates, launches new campaigns, or introduces promotional events, ARPDAU provides a high-level overview of the app's revenue trends in response to these modifications.
ARPDAU functions as a metric enabling app developers and marketers to gain real-time insights into their app's monetization capabilities, delving into the finest details.
For instance, imagine your app offers special benefits to its paid users. You decide to decrease the price of the paid version to attract more subscriptions. Utilizing ARPDAU, you can assess whether this price reduction effectively increases paid subscriptions and overall revenue.
This metric benefits app developers because:
Regular Analysis for Maximizing Monetization Efforts Frequent evaluation guarantees optimal utilization of monetization strategies.
Visibility and Balance in Advertising Strategy ARPDAU provides evidence of a balanced and visible advertising strategy.
While many app developers use ARPDAU to compare monetization trends over time, they are aware that ARPDAU is a short-term metric and possesses some limitations. This is primarily due to the fact that lifetime value (LTV) serves as a superior metric for long-term monetization performance evaluation.
So, what challenges or constraints are associated with using this metric?
Consider an app that is a two-player game. Player one is active from 8 am to 11 am daily, spending $1 each day. Player two is active from 10 pm to 1 am and also spends $1 daily. In this scenario, the average revenue per day amounts to $2, resulting in an ARPDAU of $1.
Now, what happens if player two skips a day? Given that they cross the midnight/day boundary, how does this impact their ARPDAU?
This example highlights a significant flaw in the calculation, underscoring why LTV serves as a more accurate metric.
ARPDAU solely focuses on daily active users, limiting data and overall comprehension of the effectiveness of the monetization strategy over an extended period.
The formula for calculating ARPDAU is straightforward:
ARPDAU = Total daily revenue / Total active daily users
For instance, if a popular social media app boasts 11 million daily users, generating $5 million in daily revenue, the calculation appears as follows:
ARPDAU = $5,000,000 / 11,000,000 = 45 cents
In this case, the ARPDAU amounts to 45 cents, implying that, on average, each active user spends 45 cents daily on the app.
Enhancing the average daily revenue per user is a feasible endeavor, with actionable strategies to implement that yield immediate effects on user spending.
Elevating Ad Engagement with Offerwalls Encouraging users to engage by offering rewards, such as in-game currency or extra lives, through techniques like offerwalls can yield positive outcomes. Displaying enticing ads increases the likelihood of improved revenue.
Captivating Users with IAPs, Special Offers, or Exclusive Deals By offering specific user segments IAPs, exclusive deals, or special offers, you can stimulate clicks and purchases, thereby augmenting revenue.
ARPU, or average revenue per user, calculates the mean revenue generated per user within a specific timeframe. This metric is preferable when targeting new users across various channels.
Essentially, ARPU per channel or platform provides insights into the platforms or channels with the highest potential for generating app income.
Key Insights
Despite its lengthy acronym, ARPDAU is easily comprehensible as a digital marketing metric.
Key points about ARPDAU:
ARPPU, or average revenue per paying user, measures how much money a user spends on a product or service in the gaming and software industries. It is calculated by dividing the total revenue generated by the number of paying users. This metric can be helpful for businesses because it allows them to understand how much money they are generating per user, and can be used to identify trends and make decisions about pricing and marketing strategies.
To calculate ARPPU, you need to know two things: the total revenue generated by a product or service and the number of paying users. The formula for ARPPU is simple:
ARPPU = Total Revenue / Number of Paying Users
For example, if a game generates $100,000 in revenue and has 1,000 paying users, the ARPPU would be $100. This means that, on average, each paying user spends $100 on the game.
In the mobile app industry, ARPPU is often used as a key performance indicator (KPI) to measure the effectiveness of marketing campaigns and the overall success of an app. By tracking ARPPU over time, app developers and marketers can identify trends and make decisions about how to improve the app's monetization strategy.
For example, if an app has a high ARPPU, it may indicate that it is popular among a certain demographic or that its pricing strategy is effective. In this case, the app's developers and marketers may want to focus on targeting the same demographic and maintaining the current pricing strategy. On the other hand, if the ARPPU is low, it may indicate that the app is not appealing to users or that the pricing strategy is ineffective. In this case, the app's developers and marketers may want to consider making changes to the app's features or pricing to increase revenue.
Overall, ARPPU is a valuable metric for app developers and marketers because it provides insight into how much money users are spending on an app, and can be used to make data-driven decisions about how to improve the app's performance and generate more revenue.
To improve ARPPU, you need to focus on two things: increasing the amount of money that users are spending on your product or service and increasing the number of users who are paying for it. Here are some strategies you can use to do this:
Overall, improving ARPPU involves a combination of strategies that focus on increasing the amount of money that users are spending on your product, and increasing the number of users who are paying for it. By implementing the strategies above and tracking your ARPPU over time, you can make data-driven decisions that will help you improve your product's performance and generate more revenue.
The metric ARPU, short for Average Revenue Per User, is a ratio calculated by dividing a business's total revenue for a specific time frame by the average number of users during that same period.
ARPU (Average Revenue Per User) is calculated by dividing the aggregate earnings produced by a company for a certain interval by the mean figure of users for that same duration. The equation for ARPU is:
ARPU = Total Revenue ÷ Average Number of Users
For example, if a subscription-based business made $100,000 from customers in January, their ARPU for that month would be determined by dividing the total revenue by the average number of users:
ARPU = $100,000 ÷ 1000 = $100
Therefore, the ARPU in this example is $100. Monitoring ARPU over a time period can help companies in gauging the financial potential of their product or service and take steps to increase revenue.
ARPU is a vital statistic for businesses as it supplies an understanding of the average income earned from each user within a certain period of time. This knowledge is invaluable to marketers, product supervisors, and executives.
For marketers, ARPU can guide their decision-making by displaying the revenue garnered from both their highest and lowest value customers. With this information, they can optimize their marketing tactics according to which campaigns are performing well and which are not succeeding. By using ARPU as a measurement, marketers can evaluate their marketing channels and campaigns and make decisions to augment their return on investment (ROI).
In relation to mobile user acquisition, ARPU is a complementary metric to cost of media metrics such as cost per install (CPI) or cost per action (CPA). Comparing ARPU with these metrics can help identify a marketing budget's return on ad spend (ROAS) and ascertain if the marketing dollars are being spent judiciously.
There are several approaches to improving ARPU. Among the most effective are:
Modifying Pricing Plans
Businesses offering subscription-based services can improve ARPU by revising price plans. This could involve introducing superior features to incentivize users to purchase higher-priced plans, or offering a reduced rate for annual payments.
Emphasizing Retention
Focusing on retaining valuable users can significantly raise ARPU. Retention is typically less expensive than acquiring new customers. Analyze user behavior to find trends in churn and launch targeted campaigns to re-engage them. One effective way to retain users is by offering loyalty plans, such as discounted rates for an e-commerce business or complimentary in-game bonuses for a gaming app.
Optimizing User Acquisition Campaigns
Measuring ARPU related to user acquisition strategies can highlight which channels, creatives, or campaigns are yielding the most valuable users. In the mobile space, you can also compare the value of different advertising networks. By focusing on strategies that bring in the highest ARPU, you can increase investment and gain a greater return for your business. Conversely, if a campaign is delivering a low ARPU, you can reallocate your resources to other areas.
Average Revenue Per User, or ARPU, is a key indicator that organizations use to measure success. Fundamentally, it is a measure of how much revenue a business is generating on a per-user basis. ARPU is determined by taking the total income of a company and dividing it by the number of users. It is an essential metric for any business that has a direct connection with its clients, such as a SaaS company or a mobile app. It can also be utilized by subscription-based businesses to measure the revenue generated per subscriber. ARPU assists businesses to comprehend how much money they make from each customer and help make data-driven decisions about product development, marketing strategy, and customer acquisition.
ARPU is a crucial metric for any business with a direct relationship with its customers, such as a software as a service (SaaS) company or a mobile app. It can also be used by subscription-based businesses, such as streaming services or gyms, to measure the revenue generated per subscriber.
ARPU is important because it allows companies to discern the amount of money they are earning from each individual customer. This data can be utilized to make key decisions regarding product advancement, marketing plans, and customer procurement. For instance, if a company realizes that its ARPU is low, it may have to concentrate on obtaining more lucrative customers or creating new products and services to produce more income per user.
Calculating ARPU is relatively straightforward. The formula is:
ARPU = Total Revenue / Number of Users
For example, let's say a company has a total revenue of $100,000 and has 10,000 users. To calculate the ARPU, you would divide the total revenue by the number of users:
ARPU = $100,000 / 10,000 = $10
In this example, the company's ARPU is $10 per user.
It's also important to note that you can calculate ARPU for a specific time period, such as monthly or annually. To do this, you would use the same formula but with the revenue and user numbers for that specific time period.
For example, if a company has a monthly revenue of $25,000 and has 2,500 users, the monthly ARPU would be:
ARPU = $25,000 / 2,500 = $10
In this example, the company's monthly ARPU is $10 per user.
ARPU and LTV (lifetime value) are often used in tandem but they are not interchangeable. LTV represents the aggregate income earned from a client throughout their lifespan. Conversely, ARPU refers to the revenue obtained per user within a short time frame. It is essential to remember that while LTV is a long-term metric, ARPU is a short-term one.
Improving your ARPU can be done in multiple manners, such as selling more to current customers, obtaining premium clients, or forming novel products and services that will bring in more income per user. In addition, businesses can put emphasis on augmenting the life span of their customers through loyalty plans or other upkeep techniques.
Overall, ARPU is an essential statistic for any corporation that desires to interpret its expansion and make decisions based on data. It is basic for any entrepreneur or financier to recognize and monitor ARPU to make well-informed decisions about the destiny of their business.
App Store Optimization (ASO) is a critical process that involves optimizing and enhancing an app's visibility in an app store. It can be considered as the mobile app version of Search Engine Optimization (SEO).
The main goal of ASO is to improve your app's ranking and visibility in the app store. Each app store provides guidance on ASO best practices, as well as tools such as advertising, which can help improve an app's ranking and visibility.
Although the exact algorithm used by app stores is unknown, several factors that drive ASO have been identified. Ongoing optimization can significantly enhance your ranking, increase organic installs, and ultimately drive more traffic to your app store page, leading to more free installs.
As with web SEO, the strategic placement of keywords is crucial to being easily discovered in the app stores. Other contributing factors include the app's title, description, and use of images and videos. When crafting your app's description, it is essential to consider how your target users might describe your app and what they use it for. Additionally, highlighting your competitive advantage can set your app apart from others.
Analyzing your competitors' keywords and choosing less popular, yet still descriptive, ones can give you an edge. Running A/B testing, localizing your listing for different countries, and measuring every possible ASO KPI related to your app's visibility can all improve your page metrics.
Finally, it's crucial to pay attention to your competitors' progress in the app stores and how they achieve their results. Since most app downloads are still organic, a well-executed ASO strategy can benefit your brand visibility and business metrics significantly.
Mobile attribution is a process that connects app installs to marketing efforts. It is a critical tool for marketers as it enables them to link their actions to results. Mobile app marketers rely heavily on attribution insights to measure and optimize their user acquisition campaigns and marketing performance. Additionally, attribution helps marketers understand how in-app events affect their efforts.
When it comes to investing in marketing budget, it's important to choose the right attribution provider. Attribution providers are classified into two categories: biased and unbiased. Biased attribution providers include data selling or buying and selling of mobile ad media in their business model, which creates potential conflicts of interest and partial business practices.
On the other hand, unbiased attribution providers focus solely on attribution as their core business. They ensure impartiality and independence as a reliable third party, measuring and reporting campaign performance, and resolving any reporting discrepancies on both the buy and sell sides of mobile advertising.
Providing attribution data as a service relies heavily on building trust with customers and partners. The trust that an attribution provider builds with its clients and partners is the foundation of its business. When this trust is broken, the attribution provider's products and services can no longer be seen as reliable. Recovering from such a problem is extremely difficult for an attribution provider.
Attribution modeling involves a framework for assessing which touchpoints receive acknowledgment, and to what extent, during the journey towards a conversion.
Attribution modeling within the mobile ecosystem pertains to utilizing various approaches to identify the origins of non-organic installations.
Attribution modeling provides advertisers with a system to attribute and quantify the impact of diverse marketing strategies across different channels. This subsequently informs decisions about budget allocation and overall strategies for mobile marketing.
As attribution modeling revolves around assigning value to specific advertising actions performed by users within a defined time frame, advertisers can more accurately pinpoint which channels are most effective in alignment with the company's objectives.
In essence, attribution modeling serves as a navigational guide for both advertisers and advertising networks. It aids in gauging the user journey and generating revenue from user interactions with advertisements.
Given that users often interact with multiple ads on various channels, there are several attribution modeling types:
Last Touch Attribution This is the prevailing standard for attribution modeling. It occurs when an installation or re-engagement is linked to the last interaction, or touchpoint, in the user's journey within the attribution window. The advertising network responsible for the last touch receives the credit and payment.
For instance, if the cost per install in the user journey costs $2, the media source that was the last touch—network C—receives full credit.
Multi-Touch Attribution Also referred to as fractional attribution, this approach identifies multiple touchpoints throughout a user's journey that contribute to a conversion, whether it's an installation, purchase, or another designated in-app action.
Multi-touch attribution can occur within a single channel (across one device), span multiple channels (across devices like mobile, desktop, or TV), or even encompass offline interactions.
In multi-touch attribution, networks A and B in the given example would be credited as assisting in the installation, while network C would receive credit for the installation.
This form of attribution assigns weighted credit to media sources that indirectly contributed to the conversion. Although it's not widely used currently, it's considered a potential future alternative due to its detailed analysis and crediting process.
The idea is that all media sources involved in the user's journey prior to the last touch will receive a portion of the payment.
Other Attribution Models The attribution ecosystem determines the specific attribution modeling method used for measurement and payment. Besides multi-touch attribution, other models like U-shaped/position-based attribution and W-shaped attribution adhere to similar principles.
Attribution modeling enables advertisers to ascertain how to attribute and quantify the performance and value of their media sources in marketing endeavors.
Without a robust attribution model, advertisers lack a comprehensive understanding of user acquisition and revenue generation. This includes detailed insights into specific media sources, user interactions, ad traffic, user quality (retention and lifetime value), and long-term return on advertising spend (ROAS) and return on investment (ROI), among other metrics.
In relation to multi-touch attribution modeling, the multiple touchpoints leading to installations provide a more comprehensive insight into how and why a user converted. This information significantly influences decisions regarding future budget distribution.
Attribution modeling not only supports advertisers' marketing efforts but also ensures accurate and equitable crediting and payment for installations on the network side. An impartial third-party attribution provider is vital for establishing a reliable, transaction-based attribution reporting system. This mechanism assigns both credit and accountability to networks as warranted.
In summary, attribution modeling serves as the framework within which attribution for mobile installations occurs. It maintains equilibrium and dynamism within the mobile ecosystem for both advertisers and media sources in the long run.
A banner ad, also known as a web banner, is a form of online advertising that is embedded on a web page. It was among the first advertisements ever published on the internet. Banner ads typically feature visuals and images rather than marketing copy, and when clicked, redirect to an external website.
Banner ads come in various types such as square ads, leaderboard ads, skyscraper ads, or traditional rectangular banners that appear horizontally at the top or bottom of a web page. They can be sold by individual websites or placed on websites through an advertising network.
A traditional, horizontal banner ad's common dimensions are 468 x 60 pixels. Banner ads are often purchased based on the number of impressions on a CPM (cost per mille or cost per thousand impressions) basis.
When a user views a website, a pop-up ad appears or "pops up" on the screen, often overlaying the existing content. In contrast, a banner ad lives within the content and is meant to be seen as a part of the website rather than an interruption. Pop-up ads are considered more effective, but they are also polarizing and can lead to a terrible user experience.
Standard banner ads, such as leaderboard ads, are placed above navigation bars or within the main content on a web page. Banner ads are typically created as images that are 468 x 60 pixels in size.
Banner ads contain calls to action (CTA) that entice users to click and view or obtain something else. They inform, notify the user of new products, grab their attention, or simply increase brand awareness in a more passive manne
Churn rate is a metric used to gauge the percentage of users who have disengaged from an app, either by ceasing to use it or uninstalling it. This term is interchangeable with "abandonment rate" and is the opposite of "app retention rate."
Calculating the churn rate of an app is typically done on a daily, weekly, or monthly basis. To determine the churn rate, subtract the number of active users at the end of a given time period from the number of active users at the beginning of the same time period. Divide the resulting figure by the number of active users at the beginning of the time period.
Churn Rate = (Active Users at the Beginning – Active Users at the End) / Active Users at the Beginning
For example, if an app had 5,000 active users at the beginning of a 90-day period and 3,500 active users at the end of the same time period, the churn rate would be calculated as follows:
Churn Rate = (5000 - 3500) / 5000 = 0.3 or 30%
Therefore, the churn rate for the app in this example would be 30%.
Churn rate, often referred to as the rate of attrition, signifies the speed at which customers cease utilizing a product or service. In the realm of mobile applications, it specifically denotes the pace at which users disengage from an app, be it through uninstallation, subscription cancellation, or passive neglect. This disengagement can stem from user dissatisfaction, migration to competitors, or financial constraints. High churn rates pose significant threats to a business's profitability and impede its growth. Hence, prioritizing efforts to minimize churn and enhance user retention becomes imperative for sustainable expansion and financial prosperity.
Churn rate quantifies the percentage of users lost within a defined timeframe, whereas retention rate gauges the portion of existing customers who persist in using an app. For example, if a mobile app commences a month with 1,000 users and loses 200 by month-end, the churn rate for that period stands at 20%, while the retention rate equals 80%. These metrics are pivotal in evaluating customer satisfaction and business viability, often serving as key performance indicators (KPIs) for app companies.
Understanding churn rate is instrumental in assessing customer satisfaction and business health. It offers insights into user departure reasons, enabling businesses to gauge their app's stickiness. Moreover, churn rate analysis provides valuable data for calculating customer lifetime value (LTV) and determining the budget for customer acquisition. The LTV-to-customer acquisition cost (CAC) ratio serves as a barometer for spending efficiency, with a balanced ratio indicating profitability. Monitoring churn is crucial for multiple reasons, including cost-effective customer retention, ensuring product-market fit, and maximizing customer lifetime value to justify higher user acquisition expenditures.
Regularly measuring an app's churn rate facilitates the tracking and enhancement of user satisfaction and engagement. Calculations can be performed monthly or annually, offering valuable insights into month-to-month and year-over-year growth trends. Monthly churn rate quantifies user loss within a month, exemplified by a scenario where an app starts with 10,000 users and ends with 8,500, resulting in a 15% monthly churn rate. Conversely, annual churn rate analyzes yearly user loss, exemplified by a reduction from 50,500 to 45,000 users, yielding a 10.89% annual churn rate.
While attaining a zero churn rate is ideal, it is practically unachievable. On average, apps lose 77% of their daily active users within the first three days post-installation. An acceptable annual churn rate typically falls between 4% and 7%, yet industry-specific benchmarks vary. Therefore, defining a "good" churn rate necessitates alignment with specific goals and adherence to industry standards.
Understanding why users churn requires meticulous examination of data patterns, especially focusing on retention trends during the initial days, weeks, and months. Identifying spikes in uninstall data aids in pinpointing specific disengagement points, such as app bugs, limited core features access, or inadequate onboarding processes. Evaluating communication strategies, including customer support interactions and timely, relevant messaging, contributes to re-engagement efforts and retention.
In summary, understanding and mitigating churn rate are indispensable endeavors for app businesses seeking sustainable growth and enhanced profitability. By adopting strategic measures, including cohort analysis, optimized onboarding, personalization, re-engagement efforts, deep linking, and data-driven issue resolution, businesses can effectively combat churn, ensuring enduring user satisfaction and prolonged app engagement.
Click flooding, categorized as mobile ad fraud, occurs when networks deliberately generate a substantial volume of deceitful clicks. This strategy aims to attribute the last click just before an app installation takes place. By doing so, these networks seek to claim full credit for the conversion.
Click flooding, sometimes referred to as click spamming, constitutes a variant of click fraud. This deceptive practice involves malicious entities orchestrating the dissemination of a large number of counterfeit click reports. Their goal is to secure credit for the concluding click prior to app installations. This not only allows them to reap rewards from advertisers but also wreaks havoc on marketing budgets.
How exactly does click flooding operate? Fraudulent actors manipulate click attribution by fabricating clicks on behalf of users who did not initiate them. This enables them to lay claim to fabricated clicks, positioning themselves for unwarranted gains.
A fraudulent application might execute these counterfeit clicks while the user is actively engaged with it or even when it's running in the background (such as battery-saving apps or launchers). These apps might even convert impressions into clicks, offering up deceptive engagement metrics, all without the user's awareness or intention.
Countering click flooding fraud is achievable through anti-fraud solutions designed to automatically thwart traffic stemming from sources involved in click flooding.
In order to identify the origins behind a click flooding assault, these solutions scrutinize traffic patterns characterized by prolonged Click-To-Install Time (CTIT) distributions, diminished click-to-install conversion ratios, and/or elevated rates of multi-touch contribution (note: this necessitates access to multi-touch attribution data).
A valuable yardstick when assessing one's data is that approximately 75% of installations materialize within the first hour following a click, with around 94% of installs taking place within 24 hours of the initial click.
It's important to note that video ads and larger applications often exhibit extended CTIT durations.
Click injection is a sophisticated form of click-spamming. By publishing (or having access to) an Android app that listens to “install broadcasts,” fraudsters detect when other apps are downloaded and trigger clicks before an install completes. The fraudster then receives credit for installs as a consequence. If fraud prevention tools are inadequate, individuals who commit click injection fraud can take advantage of a low-quality app to take control of a device at a strategic moment (and with the necessary data) in order to produce a false advertisement click that looks genuine, causing payouts based on cost per install (CPI).
The click injection technique is often employed by fraudulent actors through the use of "junk apps" installed on a user's device. These apps lay dormant until an installation broadcast activates them, allowing them to take control of the user's device and generate false clicks that steal credit for organic or non-organic installs generated by other networks.
Aside from the financial damage caused by draining advertising budgets, click injection can have serious implications for advertisers' future targeting and segmentation of traffic. It can distort the planning and distribution of ad spend by highlighting fraudulent sources ahead of legitimate ones.
Click to Install Time, abbreviated as CTIT, refers to the time interval between a user clicking on an advertisement and the subsequent opening of an application. This metric holds significant value as it plays a crucial role in identifying and thwarting instances of mobile ad fraud perpetrated by malicious entities seeking to fabricate final clicks.
CTIT constitutes a form of distribution modeling utilized in the identification of two distinct forms of ad fraud: install hijacking and click flooding.
In the case of install hijacking, CTIT analysis is primarily focused on detecting patterns where a substantial number of installations occur within the initial 3 to 10 seconds after the initial click.
Conversely, when dealing with click flooding, CTIT analysis aims to uncover an almost uniform distribution pattern occurring on a larger scale. This pattern is observed between the 2nd and 24th hours, as well as between the 2nd and 7th days following the installation.
CTIT holds fundamental importance in the detection and tracing of fraud. It stands as a primary and crucial metric to assess when scrutinizing potential fraudulent endeavors within your web traffic.
Click spamming, also known as click flooding, is a form of mobile ad fraud wherein networks generate a substantial volume of fake clicks in an attempt to receive credit for the last click before a conversion, such as an app installation. This malicious activity aims to deceive advertisers into paying for fraudulent clicks, thereby causing significant financial losses.
To carry out click spamming, fraudsters send a massive amount of clicks to a Mobile Measurement Partner (MMP). The high volume of clicks increases the probability of misattribution by the MMP, resulting in the fraudsters receiving payouts for their illegitimate activities.
Apart from robbing advertisers of their marketing budgets, click spamming also has the potential to distort or skew the advertiser's marketing data. This can cause marketers to increase their budgets for these networks, even though they are not generating any real clicks, users, or conversions. Hence, it's critical to implement robust fraud prevention measures to mitigate the risk of click spamming and other fraudulent activities in the mobile ad ecosystem.
Click spamming is a fraudulent practice that involves generating fake clicks in various ways, such as using a fraudulent app that executes clicks in the background of a user's mobile device without their knowledge or consent. By claiming credit for these fraudulent clicks, the fraudsters aim to deceive advertisers and steal their marketing budgets.
A user downloads a fraudulent app, which may appear to be a legitimate utility app, game, or other type of mobile app. The app has code that runs in the background, generating spam clicks on ads without the user's knowledge. The clicks are then attributed to the developer of the fraudulent app, who can then receive payment for the clicks.
This example highlights the negative consequences of click spamming, such as reduced battery life for the user and distorted marketing data for advertisers. Moreover, click spamming techniques are becoming increasingly sophisticated, with fraudsters targeting specific users who are more likely to engage with the fraudulent ads.
To combat click spamming, advertisers and Mobile Measurement Partners (MMPs) are implementing advanced fraud prevention techniques to ensure that their advertising budgets are spent on legitimate users and driving real conversions. As click spamming continues to pose a growing threat, it's crucial for businesses to remain vigilant and adopt robust fraud prevention strategies to protect their marketing investments.
Detecting and stopping click spam requires careful monitoring and analysis of data. One approach is to analyze traffic and conversions, as fraudulent activity often leads to sudden spikes in clicks without corresponding conversions. Suspicious sources, such as mobile apps or websites, should be isolated and removed, and further investigation should be conducted to determine the cause of the problem.
Another method involves analyzing publisher analytics to identify patterns and click distributions that indicate the presence of fake clicks. Unusual patterns can be detected and avoided in the future to prevent similar fraudulent activity from occurring again.
Validating apps before using them in advertising campaigns is also recommended, as this can help detect and prevent malicious code from infiltrating an advertiser's network. However, the validation process can be time-consuming, and not all developers are willing to share their code.
Taking a proactive approach to fighting click fraud involves investing in anti-fraud solutions that use sophisticated algorithms to detect and block fraudulent activity before it causes significant damage to an advertiser's budget. Solutions that offer in-depth traffic and click analytics should be considered, as they can help identify suspicious activity and prevent it from occurring in the first place.
Finally, manually selecting ad networks and placements can be time-consuming, so investing in a technology stack that streamlines the process can be beneficial. This can help minimize the workload and automate tasks associated with managing advertising campaigns, ultimately leading to a more efficient and effective approach to preventing click spam.
Cohort analysis is a method used to analyze the behavior of a particular group of customers over time. In this approach, cohorts are created as unchanging groups, where no new customers join a cohort once it's formed, and customers cannot move from one cohort to another.
The most common type of cohort is the group of customers who became part of the business in a specific time frame, such as the second week of January or the fourth quarter of the year. Also known as "static pool analysis," cohort analysis tracks the behavior of these specific, fixed customer groups over time, as they move along the customer lifecycle curve.
Cohort analysis is useful for identifying trends within customer behavior that may be hidden when looking at more general analytics data. For example, overall analytics data may show an increasing number of monthly purchases, which seems like a positive sign for the business. However, cohort analysis may reveal that the higher overall percentage is due to many first-time buyers, while cohorts of older customers are actually returning to make purchases much less frequently than in the past. Therefore, following the behavior of particular cohorts over time provides a more accurate view of business performance.
When a company experiences a "bad month," it's essential to understand if the unexpected performance drop was due to a market-wide factor or a specific problem that might be identified and adjusted. For example, if most new customers in a particular month spent much less than the customers acquired in previous months, it would be wise to examine any changes in acquisition strategy and identify under-performers.
Sophisticated cohort analysis involves tracking the longer-term impact of a particular marketing action on a group of customers who were treated with that marketing action. By creating a cohort of this customer group and tracking its behavior over time, the marketer can achieve much deeper insight into the long-term effects of a particular marketing action.
Another advanced use of cohort analysis is combining cohort analysis with behavior-based customer micro-segmentation. This involves defining a cohort of customers who exhibit certain behaviors, such as high spending on a particular product over a specific period, and spotting trends among this specific group of customers to gain insights into customer acquisition, uplift marketing, and customer retention.
Conversion rate refers to the percentage of viewers who take a desired action, such as registering for an event, making a purchase, or clicking a link. This metric is used to measure the effectiveness of a campaign or content. The higher the conversion rate, the more successful the campaign is considered. Average conversion rates vary by industry, but typically range in the low single digits, such as 2% for app downloads leading to a purchase. A small change in the conversion rate can have a significant impact.
Conversion rate is crucial because it indicates how effective a page or content is in achieving its marketing objectives. While metrics like page views or impressions are informative, they do not provide insight into whether the content is achieving its purpose of driving users to act. For example, a good click-through rate (CTR) for a digital ad campaign may indicate that the ad is grabbing people's attention, but if few people download the app, the campaign is not performing as it should. By tracking conversion rates, marketers can identify weaknesses in their marketing funnel and improve their landing pages or promotional offers.
Conversion rates can be calculated by dividing the number of conversions by the number of interactions with the content and multiplying by 100 to get a percentage. Many analytics platforms, such as Google Analytics, automatically calculate conversion rates once goals are set up. For example, a landing page with 1,000 views and 25 resulting purchases would have a conversion rate of 2.5% (25/1,000 = 0.025 or 2.5%).
When it comes to online buying decisions, users are faced with a complex array of factors to consider. From product compatibility to pricing and company trustworthiness, there are multiple objections that must be overcome in a very limited time window. In order to increase conversion rates, it is essential to not only showcase the value of your product or service through messaging and imagery, but also to create a seamless user experience with a strong call to action.
There are several factors that can impact conversion rates, including page load time, page design, differentiation, pricing and offer, and the call to action message. Ensuring that your website or landing page loads quickly is essential, as research shows that slow loading times can lead to user disinterest and a lack of trust. A well-designed website with a mobile-friendly interface and optimized content can also improve conversion rates, and differentiation strategies can help your offering stand out among competitors.
Pricing and offers are also important factors to consider, as users may be turned off by high prices or unappealing promotions. By benchmarking your pricing against competitors and offering special deals or limited-time promotions, you can create a sense of urgency and incentivize users to convert.
Perhaps most important of all is the call to action message. Effective messaging should prompt users to take action and provide a clear next step. It is important to choose the right words to convey the value of taking action, and to test different CTA messages to find what works best. By following these best practices and continually testing and refining your approach, you can improve your conversion rates and achieve greater success in your online marketing efforts.
Cost per click (CPC) is a widely used term in paid advertising, where advertisers pay publishers for each click on an ad. Also known as pay per click (PPC), CPC is a key metric that helps advertisers determine the cost of displaying ads to users on search engines, social media platforms, and other publishers.
CPC plays a significant role in determining bidding strategies and conversion bidding types, helping businesses maximize clicks relative to their budget size and target keywords. Various types of ads, including text, rich-media, and social media ads, use CPC as a factor in calculating total paid advertising campaign costs. However, certain ad types are only displayed on specific networks, such as Google Search Network (ads at the top of Google's search engine result pages) and Display Network (Google-owned or partnered sites like YouTube and Gmail).
Calculating CPC involves dividing the cost of a paid advertising campaign by the number of clicks received. Popular online advertising tools like Google AdWords often show CPC for target keywords. Other related metrics include average cost per click and maximum cost per click, with the latter referring to the highest amount an advertiser is willing to pay for a click.
Manual CPC bidding is an approach where advertisers set the maximum CPC for each ad by hand, while enhanced CPC is an automated conversion bidding strategy in Google AdWords used to maximize ad conversions for certain types of ads on Google's Search and Display Network.
CPC has several advantages, including its ability to help businesses drive traffic to their sites or stores, improve paid advertising campaigns, determine the most effective ad types, and choose manual or automated bidding strategies based on their understanding of their business, audience, and paid advertising strategies.
In summary, CPC is a crucial metric for businesses looking to run effective online advertising campaigns. By understanding how CPC works and how to calculate it, businesses can optimize their advertising campaigns to generate more clicks and conversions, leading to increased revenue and growth.
Cost per acquisition (CPA), alternatively referred to as cost per conversion, is a growth marketing key performance indicator (KPI) that quantifies the cumulative expenditure incurred by a user undertaking a task that results in a conversion. The conversion may involve various actions, including purchases, clicks, sign-ups, form submissions, or app downloads.
The formula for computing CPA is the total advertising cost divided by the total number of conversions as follows:
CPA = Total Advertising Cost/Total Number of Conversions
For example, let's assume that you have run an advertising campaign on Facebook, Twitter, and Google to promote your e-commerce business for a week. If the total advertising cost for the campaign was $1000, and there were about 50 conversions, the CPA would be $20 ($1000/50).
CPA is a critical KPI for every business, as it provides a business perspective to measure the success of your campaign. However, many marketers tend to concentrate on traffic and sales acquisition and overlook cost optimization. Focusing on cost optimization by reducing the cost per acquisition can increase your return on investment (ROI) within a relatively short period.
Cost per acquisition is an important metric that is utilized in various paid marketing activities, including Pay-per-click (PPC), Affiliate marketing, Display advertising, Social media advertising, and Content marketing.
Cost per action (CPA) is a performance-based pricing model that enables marketers to pay media sources a fixed rate based on a predetermined action. Unlike cost per install (CPI), which relies on attributed user installs to achieve campaign conversion, CPA can be selected from various in-app events, including registration, app launch, item purchase, and other actions.
The value of CPA is simply the price an advertiser pays a media source for each pre-specified action (e.g., purchase, registration, etc.) driven by that source. To obtain a comprehensive overview of a particular ad network's performance, you can calculate the effective cost per action (eCPA) by dividing the total cost incurred from that network by the total number of specified actions based on a pre-selected time range.
This metric includes all campaign CPAs you want to measure, giving you an overall view of advertising costs over time on the media source level.
Tracking cost per acquisition (CPA) is a crucial aspect of digital-first businesses, and it can be done using several methods, including:
By leveraging UTM parameters, digital-first businesses can generate link codes for social media or affiliate marketing, which help to track CPA more effectively. Exporting PPC campaign data from AdWords and using promotional codes to build custom links for internal campaigns can also provide valuable insights into CPA. An effective CRM system can streamline the tracking process, making it easier to monitor CPA accurately. Additionally, including a form field on lead forms can help identify the lead source, reducing lead attribution gaps and providing a clearer picture of CPA.
The CPI, or Cost per Install, refers to a pre-agreed upon price that an advertiser will pay to a publisher for every user who installs their app directly as a result of an advertisement served by the publisher. It is important to note that this term is sometimes confused with eCPI, or effective CPI, which is the actual cost per install that an advertiser incurs as they receive installs in real-time or after the campaign has concluded.
For instance, suppose an advertiser allocates a marketing budget of $10,000 to a publisher, which results in 5,000 installs. In this scenario, the eCPI for this campaign would be $2. Occasionally, media outlets will optimize their campaigns based on an eCPI objective. This means that before the campaign begins, the advertiser and publisher agree to a target eCPI and work towards optimizing the campaign to achieve this objective.
However, it is important to note that self-reporting networks, such as Facebook, Google, and Snap, charge advertisers based on CPM or cost per mille, which refers to the price per 1,000 impressions viewed. These networks optimize their campaigns towards eCPI or the advertiser's maximum bid levels, while charging advertisers based on CPM. Consequently, their eCPI may differ from the eCPI calculated by the attribution provider since an SRN charges based on engagement, regardless of whether it was attributed for the last touch or not.
To calculate the Cost Per Install (CPI) for your mobile app, you need to divide your total ad spend for a specific time period by the number of new installs generated during that same period. The resulting figure is your CPI.
For instance, if you invested $500 in ads for your app and generated 200 new installs during the campaign, your CPI would be $2.50.
Formula: CPI = Ad spend / Number of new installs
Example: CPI = $500 / 200 = $2.50
Therefore, your Cost Per Install for this campaign would be $2.50.
The calculation of Cost Per Install (CPI) is affected by various factors that determine the price an advertiser pays to a publisher for every new install resulting from an ad. Here's a detailed explanation of these factors:
Country or Region: The geographical location of the user plays a crucial role in determining the CPI. The socio-economic standards of a region can influence the price an advertiser pays for a CPI, with more affluent countries resulting in higher value users and therefore, higher CPIs. As an example, the average CPI in North America is $5.30 compared to LATAM, where it is $0.30.
Channel: Different channels offer varying services and popularity and thus, different CPI costs. Social media channels like Facebook and Twitter, which cater to larger audiences, can charge higher CPIs, though they need to balance audience size with the CPI. On the other hand, niche channels with targeted audiences can demand higher CPIs despite their smaller scale.
App Vertical/Genre: CPI can vary significantly across verticals and genres within the same vertical. For instance, hyper-casual games usually have a CPI of $1 or less, whereas mid-core and hardcore games can have up to five times that CPI.
Cost of Ad Unit: The CPI cost can also depend on the ad unit's value, with more prominent ad inventory commanding higher prices than remnant inventory that advertisers cannot sell.
Android vs. Apple: The difference between iOS and Android platforms is another significant factor affecting CPI. For the same reasons as geolocation, iOS users tend to spend more than Android users on average. Regions with a higher GDP, such as North America, Japan, and Europe, tend to have more iOS users, while Android has a greater presence in regions such as LATAM, India, and Southeast Asia. For example, the average CPI for Android is $1.20, while that for iOS is triple at $3.60. Within different gaming genres, there is also a significant CPI gap. For example, puzzle games in Japan have a CPI of $1.77 on Android compared to $3.69 on iOS. Action games range from $2.01 on Android to $3.96 on iOS, while educational games are $1.09 on Android and $3.04 on iOS.
Lead generation is a critical marketing metric for your sales and marketing teams. To acquire new leads, you need to conduct various marketing efforts, such as display advertising and webinars. Tracking not only the number of quality leads but also the cost of acquiring potential customers is essential. This is where the cost per lead (CPL) comes in.
CPL is the average cost for each new lead generated in your ad campaign. It is a lead metric that measures the cost-effectiveness of your marketing campaigns, ensuring that generating leads is worth the ad spend. Similar to CPM and CPC, it is also an online advertising pricing model where the advertiser pays for a sign-up from a potential customer instead of a view or click.
It is crucial not to confuse CPL with other similar acronyms in digital marketing such as Cost Per Acquisition (CPA), Cost Per Click (CPC), Cost Per Thousand (CPM), and Customer Relationship Management (CRM).
Calculating the CPL is relatively simple. You divide the total ad spend for a given period by the number of leads generated for the same period. The formula for calculating CPL is as follows:
Total ad spend / number of leads = cost per lead
While a good cost per lead varies across different industries, it should be equal to or less than your gross profit per sale. For instance, if a sale gives you a total amount of $100 after deducting total costs and expenses, your cost per lead should be $100 or lower. On the other hand, a bad cost per lead is when each lead costs more than your average gross profit per sale.
To improve your lead generation and lower your cost per lead, consider the following best practices:
The cost per mille (CPM) is a widely used pricing model in the advertising industry, wherein advertisers pay a fixed amount for every 1,000 impressions of their ads. The term "mille" is derived from the Latin word for 1,000. This model is commonly utilized by advertisers to determine the cost-effectiveness of their campaigns and is an important metric in programmatic advertising.
In the realm of programmatic advertising, digital ad inventory can be procured and sold automatically. CPM is most advantageous for larger publishers, as advertisers pay a predetermined fee based on the number of impressions an ad placement generates, usually monthly or quarterly.
To determine the CPM for an app’s ad campaign, essential data such as the total cost of the campaign and the total number of ad impressions generated must be available. To compute the CPM, divide the total campaign cost by the number of impressions and then multiply the result by 1,000, which produces the CPM rate.
To illustrate a CPM calculation, consider a hypothetical example where an ad campaign costs $800 and generates 10,000 impressions. In this scenario, the CPM for the ad would be $80.
($800 / 10,000 impressions) * 1,000 = $80
Calculating CPM is a crucial aspect of digital advertising as it allows advertisers to compare the costs of various ad campaigns across different platforms and publishers. By analyzing CPM rates, advertisers can make informed decisions about their ad placements and maximize the efficiency of their ad spend.
CPM, or cost per mille, is a pricing model used in marketing and advertising for campaigns that aim to increase brand exposure and awareness. With CPM campaigns, advertisers pay for ad impressions to maximize the number of people who see their ads. This is in contrast to performance marketing campaigns that require payment only upon completion of specific actions.
For instance, in a cost-per-completed-view pricing model, advertisers don't pay until a video ad is watched in its entirety, while in a cost-per-engagement model, an action beyond the initial impression is required. CPM campaigns are ideal for creating and elevating brand awareness before a more conversion-oriented campaign, even though they're challenging to measure in terms of performance.
Although CPM campaigns don't demand user engagement with the ad, their effectiveness can be determined by evaluating CTR (click-through rate), which is the ratio of clicks an ad receives compared to overall impressions. Therefore, marketers can get a general understanding of how well their ad resonated with users.
In the world of programmatic advertising, CPM is a pricing model used by advertisers, while eCPM serves as a revenue indicator for app developers. While both metrics relate to the cost of 1,000 impressions, CPM exclusively refers to the amount an advertiser will pay for 1,000 ad impressions. Typically, CPM is used in the context of brand awareness campaigns that don't have specific performance goals. Advertisers pay for a specific number of impressions, and the focus is on generating exposure.
In contrast, eCPM, also known as effective CPM, measures the revenue an app developer generates from displaying 1,000 ad impressions to their users. This metric takes into account both ad impressions and their associated earnings. Earnings can come from clicks, ad views, or other types of engagement with the ad. For app developers, eCPM is a key indicator of how well they are monetizing their app's user base.
A device ID is a distinct and anonymous identifier consisting of a combination of alphanumeric characters linked to a solitary mobile device, such as a smartphone, tablet, or wearable device like a smartwatch.
The device ID is devoid of any personally identifiable details, like names, emails, addresses, or credit card numbers. It can be accessed by any app installed on the device, enabling marketers and developers to track users' in-app activities and campaign interactions without accessing personal information.
However, recent times have witnessed a significant shift towards consumer privacy, favoring aggregated data over individual-level data. A pivotal change was Apple's introduction of AppTrackingTransparency (ATT), compelling app proprietors to obtain user consent for accessing their device IDs – more details to follow.
Two primary types of device IDs exist: Apple employs the Identifier for Advertisers (IDFA), while Android utilizes the Google Advertiser ID (GAID). Both function similarly, associating user actions with ad campaigns, installations, and in-app engagements.
The Apple IDFA is presented in uppercase, comprising eight digits, a dash, and three sets of four digits. An example:
GAID employs the same structure but employs lowercase letters:
Notably, post iOS 14.5, access to the IDFA depends on user consent. Further details will be discussed later.
Device IDs are primarily employed by app marketers to assess pre-install engagement, installations, and post-install in-app activities. They are crucial for attributing marketing endeavors and charting user journeys. Matching device IDs with user interactions is a dependable method of attribution due to its deterministic nature.
Deterministic attribution hinges on the device ID to recognize users across multiple channels, ensuring precise measurement of user behavior. Moreover, device IDs facilitate personalized user experiences, delivering relevant ads and services based on user behavior and preferences. These IDs aid in refining audience segmentation by grouping users according to device type, usage patterns, and more.
Lastly, device IDs empower app owners to comprehend user engagement levels by aggregating in-app event data. This insight assists in identifying patterns of user engagement, such as drop-offs, conversions, and loyalty development.
The device ID is retrievable by any installed app upon its initial launch. Subsequently, it is employed for attribution purposes, associating installs with previous activities. Consider attributing an app install:
A user clicks on an ad for an app, leading them to the relevant app store (Google Play or Apple App Store) for downloading. Following installation and the first launch, the app's Attribution Software Development Kit (SDK) activates, recording the install. The SDK then searches its database for matching click or view IDs.
If a match is found within the attribution window, the ad is credited for facilitating the app's installation.
Discovering your device ID is straightforward, whether you own an Android or Apple device. For Android, input "##3455##" into the keypad to access the GTalk service monitor and view your device ID.
Since iOS 14's launch, Apple mandates that apps seek user consent to access their IDFA, aligning with their efforts towards enhanced consumer privacy. Before IDFA, Apple utilized a Unique Device Identifier (UDID), which couldn't be reset, leading to privacy concerns and its eventual replacement in 2012.
In 2016, Apple introduced Limited Ad Tracking (LAT), enabling users to opt-out of tracking. Under Apple's AppTrackingTransparency (ATT) framework introduced in June 2020, app owners must obtain user permission to access their IDFA.
Amid growing privacy concerns, the future of measurement shifts towards aggregated data. One significant change is moving from user-level to aggregated data, eliminating individual measurement while focusing on trends. For iOS users opting into ATT, the device ID's usage remains unchanged.
For opt-out users, alternative methods like SKAdNetwork, machine learning, predictive analytics, incrementality testing, and web-to-app flows emerge.
Device IDs have been pivotal for measurement and optimization in the mobile landscape. The rise of privacy-centric updates has slightly diminished their role, though they remain crucial tools for marketers' success.
Device IDs facilitate precise user-action matching, and they offer audience segmentation for campaign enhancement. Under the ATT framework, access to the IDFA necessitates user permission. In its absence, SKAdNetwork, supplemented by other solutions, becomes essential for marketers' needs.
Deferred deep linking offers brands the opportunity to engage potential customers through mobile promotions, guiding non-users to specific content pages within an app.
Deferred deep linking represents a marketing strategy leveraging external digital avenues to reach potential users and seamlessly direct them to content and promotions housed within an app. With deep linking, users are spared the need to navigate through the app in search of desired content. Instead, upon downloading and opening the app, users are directly led to the precise content page linked to the provided URL. This technique is widely regarded as the optimal means of transitioning non-users from an advertisement to an app.
The operational mechanism of deferred deep linking involves using both mobile and non-mobile channels to guide non-users toward app installation and subsequently to a specific location within the app. The process unfolds as follows:
This process significantly enhances user experience and engagement, simplifying app interactions for non-users. It eliminates the need for users to navigate through the app store, download the app, and then search within the app for the desired content. Instead, deferred deep linking efficiently guides users from app download within the store directly to the targeted content.
There are several compelling reasons for businesses to prioritize channeling customers to their app rather than their website, and deferred deep linking serves as the most effective and secure means to achieve this objective.
Deferred deep links stand as a potent tool for both re-engagement and acquisition, serving as a versatile solution for various marketing needs.
While conventional deep links have primarily targeted existing app users, deferred deep links offer a broader spectrum of applications:
Deferred deep linking provides avenues for acquiring engaged users, rekindling relationships, and recapturing lost opportunities.
The integration of deferred deep linking broadens mobile app marketing horizons, enabling expansion into various physical and digital channels, including SMS, email, social media, QR codes, and even desktop-to-app interactions. This technique uniquely enables safe utilization of external digital avenues to channel users to specific conversion-oriented pages.
To optimize marketing campaigns, adhering to these best practices is crucial:
It's essential to distinguish between deferred deep linking and traditional deep linking. The latter is aimed at re-engaging existing app users, while deferred deep linking focuses on enticing non-users to download the app. Deep links lead current users to specific pages within the app, while deferred deep linking directs non-users to download the app and subsequently takes them to the desired location.
Brands strategically employ deferred deep linking for several purposes, enhancing customer experience and expanding their user base. Examples include:
In sum, deferred deep linking is a potent strategy fostering customer engagement, acquisition, and retention through targeted and streamlined interactions within mobile apps.
Dormant users refer to individuals who were previously active within your application but have ceased engaging with it.
The duration of inactivity and the specific actions considered as activity vary from one application to another. For instance, a banking app might outline a dormant user as follows:
Conversely, a food-tracking app that encourages daily food logging might characterize a dormant user like this:
For many businesses, regaining dormant users is more cost-effective than acquiring new ones. Even more economical is proactively engaging users who are on the verge of becoming dormant. Therefore, comprehending the identity of dormant users, the reasons for their dormancy, and potential interventions is crucial.
The initial step to identify dormant users entails establishing criteria for dormancy, including the duration of inactivity and the specific activities not performed by the user. These criteria will differ across industries and applications.
To determine what constitutes dormancy for your app, consider these questions:
While analyzing app data, you might discover that users who go on to subscribe typically log in for at least five days during their seven-day free trial. Consequently, you could categorize a dormant user as someone with just one day left in their trial but who has logged in on four days or fewer. Subsequently, you could initiate a re-engagement campaign targeting these dormant users, encouraging them to subscribe on the seventh day.
After identifying dormant users, the subsequent step involves deciphering the reasons behind their disengagement. Potential factors could include confusion about app functionality, boredom with content, or simply forgetting about the app.
Examining data for patterns among formerly active users who became dormant is essential. Reviewing prior communications with dormant users and exploring any correlations between increased dormancy and industry-related events could provide insights.
Once the causes of dormancy are known, a targeted approach can be devised to address the issue, with regular assessments to adapt to changing patterns.
By the seventh day post-download, less than 7% of users remain engaged with an app. To enhance user retention, several key principles can be applied:
In addition to general prevention tactics, various strategies can be employed to re-engage dormant users:
Key Insights
A DMP, or Data Management Platform, refers to a software system utilized in the fields of marketing and advertising for the purpose of constructing profiles of anonymous individuals, aggregating and preserving information about each individual, and facilitating the sharing of such data with advertising networks.
DMPs are employed to manage, store, and scrutinize data relating to advertisement campaigns and target audiences. A DMP can be linked to a Demand Side Platform (DSP) or Supply Side Platform (SSP) to enable the procurement of advertisements via ad networks. The DMP ingests anonymous identifiers of customers, cross-references these against external lists, creates a lookalike model with summarized data, selects similar anonymous individuals from third-party lists, and transmits such lists to advertising systems.
In essence, a DMP serves as a platform for audience data management. It is indispensable for audience segmentation, the development of lookalike audiences, and the optimization of paid media expenditures. It does not store first-party data and primarily utilizes third-party data in the form of cookie IDs and user behavior patterns.
A Data Management Platform (DMP) is a technology solution that facilitates the collection, organization, and activation of first-party, second-party, and third-party data from various online, offline, and mobile sources. The purpose of a DMP is to build detailed customer profiles that drive targeted advertising and personalization initiatives. These anonymized customer profiles are then made available to other tools such as ad exchanges, demand-side platforms (DSPs), and supply-side platforms (SSPs) to improve targeting, personalization, and content customization.
DMPs are critical to digital marketing as they allow organizations to gain a deeper understanding of their customers. As more customer data is created and collected, DMPs provide a robust solution for managing this data effectively and turning it into insights that drive outcomes. DMPs can handle different types of data including first-party data collected from website visits, CRM systems, social media, subscriptions, mobile, and apps; second-party data obtained from a mutually beneficial relationship with another company; and third-party data from websites and social media platforms that is used to reach a wider audience.
The focus on first-party data has increased in recent times, and DMPs are capable of effectively collecting and managing this type of data, typically pulling first-party data from CRM software or company-owned channels and connecting to third-party data brokers or corporate partners for third-party data. However, some industries, such as consumer packaged goods, have a scarcity of first-party data, and DMPs must augment their platforms with innovative technologies, such as identity graphs, to build vast data lakes that can be segmented and activated.
Being data-driven is not enough in the era of digital marketing, and the focus must be on quality data-driven initiatives. A sophisticated DMP allows organizations to safely analyze and refine their datasets, ensuring only the most accurate data is used in marketing efforts. A DMP works by analyzing both first-party and third-party demographic, contextual, and behavioral data to build targeted audience segments. The data collected by a DMP is organized to build an anonymized profile of each customer, which is then shared with digital advertising platforms and in-house marketing channels to serve targeted ads or content.
If you're a marketer just starting out with digital advertising and audience segmentation, a DMP can be a useful tool for you. It allows for the creation of look-alike audiences based on key data points, such as individuals who live in Cleveland and own a Play Station 5.
However, many marketers have a wider focus that goes beyond digital ads, making it beneficial to integrate a DMP with other marketing technology tools. This allows for a comprehensive view of the customer journey, enabling the identification of customers like John Doe, who lives in Cleveland, owns a PS5, is researching iPADs, and recently purchased a smartwatch from your online store.
While a DMP can be a great starting point for becoming a data-driven marketer, it's best used as part of a larger marketing ecosystem. On the other hand, a Customer Data Platform (CDP) is designed for all types of customer data and creates a 360-degree view of named, individual customers.
DMPs focus on anonymized audience data, while CDPs gather data from various sources, including first-party data and personally-identifiable information (PII). DMPs store data for a maximum of 90 days, while CDPs retain data long-term to build robust customer profiles.
In conclusion, DMPs are suitable for short-term audience segmentation tasks, while a CDP is necessary for a comprehensive understanding of individual customers and intelligent orchestration of their journey. Most CDPs can be integrated with any DMP, using the DMP identifier to enhance the customer profiles in the CDP.
Emulated devices, alternatively referred to as device emulators, encompass virtual replications of physical devices. These simulations mirror the operational behaviors, functionalities, and appearances of genuine devices. The primary purpose of emulated devices is to provide developers with a platform to examine software applications, systems, and debug efforts within a digital environment prior to their official deployment. Despite their advantageous features, emulated devices can be misused by malicious individuals to disrupt legitimate paid advertising campaigns.
Emulated devices denote mobile operating systems functioning on non-mobile apparatuses. Developers commonly employ device emulators on their laptops to assess their products across diverse devices or operating systems, obviating the necessity to procure and configure various physical devices. For instance, a developer aiming to evaluate their application's performance on different iterations of iOS, such as 9, 10, and 11, spanning devices like the iPhone 7, 8, and X, can achieve this without the need to acquire and set up each individual device. Another notable attribute of these emulated devices lies in their capacity to be automated for programmatically executed tasks, eliminating the requirement for human intervention.
Emulated devices can be exploited within the realm of mobile advertising. Fraudulent actors utilize scripted emulators to repetitively engage with paid ad campaigns, install applications, and execute conversions, all with the intention of siphoning off advertising budgets. This nefarious manipulation involves employing emulated devices to carry out actions that emulate genuine user behaviors, ultimately diverting marketers' allocated ad expenditure into the pockets of these malicious entities.
The fraudulent application of emulated devices often transcends their basic simulation concept. Perpetrators go a step further by meticulously programming these emulators to interact with advertising campaigns, install apps, and undertake in-app activities, thereby fabricating fictitious user engagements. These contrived actions, distinctively fraudulent due to their origin from non-human sources, deplete marketing budgets while channeling marketers' financial investments directly into the coffers of fraudsters.
Fake installs refer to the practice of artificially inflating the number of downloads for a mobile application by fraudulent methods, for example, creating fake accounts or utilizing bots to download the app multiple times automatically. This tactic is a violation of the guidelines set forth by app stores such as the App Store and Google Play and is considered unethical in the software development industry.
It is important to understand that fake installs not only undermines the integrity of app store rankings and recommendations but also misrepresent the true popularity of an app to potential users. This can ultimately lead to a poor user experience and wasted time and resources for those who may download an app based on its inflated metrics.
Fake installs typically work by using fake accounts or automated bots to inflate the number of downloads for a mobile application artificially. This can be done in a few different ways;
It's important to mention that install fraud is prohibited and violates the terms of service set by app stores such as Google Play and Apple's App Store. Developers engaging in this practice risk having their apps removed from the app store and even being banned from the platform.
Detection of fake installs is an essential part of maintaining the integrity of mobile app stores and making sure that users have access to accurate information about app popularity and usage. There are a number of different methods that can be used to detect fake installs, including:
Google Advertising ID, commonly known as GAID, Android ID, or Android Advertising ID (AAID), serves as a unique identifier for devices. This identifier plays a pivotal role in allowing app developers and marketers to gauge campaign performance and user actions across various media sources, all while safeguarding user privacy.
Debuting in 2014, GAID is akin to Apple's Unique Device Identifier (UDID). It offers advertisers the means to monitor ad views, app engagement, and conversions. A noteworthy feature is that users can reset their GAID, simultaneously allowing their device identifier to remain visible to media vendors and mobile measurement partners (MMPs).
GAID has historically held a crucial position within the realm of mobile advertising. It empowers advertisers to scrutinize app traffic and attribute sources of media, encompassing app installations and in-app purchases. This empowers marketers to evaluate campaign efficacy, refine strategies in real-time, and categorize their audience for personalized outreach, thereby optimizing their impact.
Furthermore, GAID aids marketers in fine-tuning campaigns for enhanced advertising efficiency. For instance, it enables the engagement of user clusters exhibiting specific in-app activities or expressing interest in particular products or services.
Though the steps may vary slightly based on your device and operating system, the following is a general guide to finding your GAID:
It is important to note that selecting the "Reset advertising ID" option generates a new GAID number but does not erase user-level data linked to the previous GAID.
As of now, when an Android user opts out of personalized ads, their GAID remains accessible to app developers for limited purposes such as analytics and fraud prevention. However, Google's announcement on June 3, 2021, revealed a significant change. By the close of 2021, Android 12 OS users who choose to opt out of personalized ads will render their GAID inaccessible to app developers and marketers. Instead, the GAID will appear as a sequence of zeros.
This move mirrors Apple's ATT framework, which was implemented in April 2021. Unlike the ATT framework, Google's approach doesn't mandate users to opt-in to GAID usage nor does it prohibit app developers from utilizing alternative device identifiers if a user opts out. This is contingent on users accepting the app's privacy policy and the app adhering to Google's Developer Distribution Agreement regarding data handling.
In place of GAID, Google introduced the Privacy Sandbox initiative. Unveiled with a 2-year timeline, this endeavor aims to develop privacy-protective technologies for online users. Operating as a collaborative effort across multiple platforms (including web and Android apps), the proposed solutions aim to curtail user tracking while offering safer alternatives to existing technologies.
Key Takeaways
In summary, GAID, or Google Advertising ID, is a distinctive device identifier empowering app developers and marketers to evaluate campaign success and user interactions across media outlets, all while upholding user data privacy. GAID's role in the mobile advertising domain has been substantial, allowing advertisers to dissect app traffic, gauge media attribution, optimize campaign effectiveness, and target audiences more effectively. The evolution of GAID, particularly its unavailability for opted-out users by the end of 2021, aligns with Google's response to user privacy concerns. Nonetheless, Google remains committed to furnishing substitutes for GAID that support analytical insights and fraud prevention.
Gross rating point (GRP) is a key performance indicator that has long been used in traditional advertising to gauge the effectiveness of an advertising campaign. GRP is a measure of impressions as a percentage of a target audience, multiplied by the frequency with which that audience sees the ad. GRP is a valuable tool for measuring the impact of traditional ad campaigns where precise measurement is difficult.
Rating points are widely used in media planning and buying. Since the 1950s, GRPs have been the primary metric for TV advertising buys, with advertisers typically paying publishers based on the ratings points they receive for a particular ad. Although GRP is primarily used in traditional media campaigns, it is also important for digital and mobile marketers to compare and coordinate linear TV and digital advertising campaigns.
To calculate GRP, we must multiply the percentage of a target demographic that is reached by an ad (known as reach) by the number of times that ad is shown in a given campaign (known as frequency). For example, if a campaign has an average of 4 impressions by 1,000,000 viewers, out of a total addressable population of 50,000,000 people, the GRP will be calculated as follows: (1,000,000/50,000,000) x 4 = 8.
The base population used in calculating GRP is typically the largest measured population with reasonable access to the media source. To estimate the total population of a given audience, advertisers may look at estimates of past performance of a chosen channel from market research and measurement groups (such as Nielsen for linear TV).
TRP and GRP measure the same thing but with different levels of specificity. While GRPs show how much of the total population your campaign can reach, TRPs look at the campaign’s performance for a specified target audience within the total population. While GRPs equal one percent of the total audience exposed to an ad, TRPs equal one percent of a given target demographic’s exposure.
When setting GRP goals, advertisers must consider how much of the market they want to reach and how many times they need to reach their audience to achieve their objectives. GRPs are calculated slightly differently based on the medium, and GRP goals vary across verticals. In general, advertisers should aim to reach between 50-90% of their target market and assume it will take at least three exposures for a viewer to act on an offer. New products require more frequency than established products, and complex products or products with a lot of competition will require more frequency.
GRP is an important metric for measuring the effectiveness of advertising campaigns. Although GRP has primarily been used as a metric for linear TV, it also serves as a bridge between traditional and digital media for ad buyers. Advertisers adopt GRP as a way to compare campaign performance across media formats. Large advertisers have been using apps such as Facebook and YouTube, which have partnered with Nielsen’s Digital Ad Ratings, to compare their ad performance versus traditional TV ads using GRPs, giving them more comprehensive campaign performance measurement capabilities.
A hybrid app is a versatile mobile application designed to be downloaded and installed on multiple mobile platforms, including Android and iOS. What sets hybrid apps apart is their ability to utilize the same codebase for all operating systems. Essentially, hybrid apps combine elements of both native apps (platform-specific applications) and web apps (applications accessed via web browsers), offering a unique approach to mobile app development.
Hybrid apps are constructed using widely-used front-end development technologies, such as HTML5, JavaScript, and CSS. This approach empowers developers to write code for a mobile app once, while still catering to the diverse landscape of multiple platforms. When users download a hybrid app from an app store and install it locally, its native shell seamlessly connects to the mobile platform's capabilities through an embedded browser.
To gain a deeper understanding, it's crucial to distinguish hybrid apps from native apps, web apps, and cross-platform apps:
Native Apps: Native apps are painstakingly tailored for a specific platform, be it iOS or Android. Developers employ platform-specific languages like Swift or Java to create them. The advantage of native apps lies in their ability to offer unparalleled performance and complete access to device functionalities. However, this comes at the cost of separate development efforts for each platform, often resulting in budgetary constraints for many businesses.
Web Apps: Web apps closely resemble mobile apps but differ in that they exclusively run within web browsers. These apps are constructed using web technologies like HTML5 and JavaScript. Web apps are renowned for their cost-effectiveness and versatility, as they can be accessed on a multitude of devices. However, they often exhibit slower performance and less intuitive user interfaces.
Cross-Platform Apps: Cross-platform apps, like hybrid apps, enable code sharing across multiple platforms. Developers can use various programming languages, and these apps offer a user experience closely mirroring native apps. They're known for cost-efficiency and ease of maintenance, depending on the chosen framework, such as React Native or Flutter.
Selecting the most suitable app type for your business involves a careful evaluation of various factors:
Target Audience: Consider the intended users of your app. If it's designed for consumers, native or hybrid apps are typically preferred. Conversely, if the app is intended for internal use within a specific organization, web apps might be more appropriate.
Platform: Determine which platforms you aim to support. Native apps are exclusive to a particular operating system, while web apps are accessible through web browsers on any device. For greater flexibility, hybrid and cross-platform apps offer broader compatibility.
Functionality: Carefully assess the features and functionalities your app requires. Native apps boast direct access to device-specific capabilities, whereas web apps may have limitations in this regard.
Development Resources: Examine the available development resources, including skills and budget. Native apps demand specialized expertise and often require larger development teams. Hybrid and cross-platform apps, on the other hand, can be developed with intermediate levels of resources.
Maintenance: Consider the ongoing maintenance needs of your app. Native apps necessitate updates for each platform separately, while web apps can be updated centrally. Hybrid and cross-platform apps fall somewhere in between in terms of maintenance requirements.
Cost: Evaluate your budget constraints. Native app development tends to be more expensive due to the need for specialized skills and separate development efforts for multiple platforms. Hybrid and cross-platform apps offer flexibility with varying development costs.
Hybrid apps offer a unique set of advantages:
Greater Reach: Hybrid apps can run on both Android and iOS platforms, significantly expanding the potential audience for your app. Users don't have to wait for platform-specific versions to be released.
Scalability: Hybrid apps excel in scalability. They allow for the efficient inclusion and deployment of new features across all platforms simultaneously, reducing friction for users.
Cost Efficiency: Development of hybrid apps is cost-effective because they require only a single codebase. This leads to shorter development times and lower costs compared to creating separate native apps.
Low Maintenance: When it comes to maintenance, hybrid apps shine. A single patch or bug fix can address issues across all platforms and devices. In contrast, native apps require updates for each platform individually.
Device Access: Hybrid apps can access device features much like native apps. This capability contributes to better performance and an enhanced user experience compared to web apps.
However, hybrid apps also come with their share of disadvantages:
Slow Performance: While hybrid app development languages have evolved, they may still lag behind native apps. This is because hybrid apps load within a browser-like component known as a webview, which is only as efficient as the webview responsible for rendering the user interface and running JavaScript code.
Test Complexity: Testing hybrid apps can be complex. While much of the code is shared, some components may still be native, adding complexity to the testing process.
UI/UX Challenges: Achieving a consistent user interface (UI) and user experience (UX) in hybrid apps largely depends on the skills and expertise of developers. If not executed well, the flexibility of hybrid app development can lead to inconsistencies. Additionally, poor internet connectivity can result in an inconsistent UX if developers are not well-versed in progressive web design. Furthermore, developers may need to write native code to adhere to Android and iOS interaction guidelines or to access platform-specific APIs (Application Programming Interfaces).
Hybrid app development is a suitable choice in various scenarios:
Multiple Platform Compatibility: If your app needs to be available on multiple platforms without extensive development efforts, hybrid apps are a logical choice. Thanks to their single codebase, they are platform-independent.
Limited Coding Knowledge: Hybrid apps are an attractive option if you lack expertise in complex programming languages. Their development simplifies the process of hiring a hybrid app developer.
Basic Features: For apps that do not require advanced native features, hybrid apps suffice. Additionally, they are well-suited for integrating basic features that require ongoing iteration, such as split testing, tweaking, and notifications. The presence of webviews within hybrid apps streamlines this process.
Avoiding API Development: If you have budget constraints but still desire some elements of native apps, hybrid apps provide an economical solution. They eliminate the need for extensive API development, which can often incur significant costs.
In addition to these situations, consider hybrid apps when:
Numerous well-known hybrid apps demonstrate their adaptability and effectiveness:
Gmail: Gmail is one of the most widely-used email platforms globally. Its hybrid app seamlessly blends HTML5, webviews, and native code to deliver a cross-platform experience, offering a plethora of functionalities and features that enhance user experience.
Instagram: Originally a native app, Instagram transitioned into a hybrid app when it became part of the Facebook ecosystem. This transformation allowed Instagram to offer offline data storage and rich media capabilities while maintaining a unified user experience.
Amazon App Store: The Amazon App Store, used for browsing and purchasing products from Amazon's vast online marketplace, is a prime example of a high-performing hybrid app. It employs HTML5 and web technologies for a user-friendly interface and leverages native code for features like push notifications and camera access.
Twitter: Twitter, a prominent social media platform, made the switch to hybrid app development to address performance issues and bugs that frequently plagued its native app. This transition significantly improved the app's speed and smoothness, changing perceptions of hybrid app development in the process.
Uber: Uber offers users an intuitive and user-friendly interface, elegantly designed for seamless navigation. The app's foundation rests on a single codebase that uses Base, a web React UI framework, to create a webview on users' phones. This approach ensures a consistent experience across various operating systems.
Key Takeaways
In summary, hybrid apps merge elements of both native and web apps, harnessing the power of HTML, CSS, and JavaScript to deliver cross-platform functionality. Their cost-effectiveness, simplified maintenance, and device access make them an attractive choice for businesses operating under budget constraints and those seeking to target diverse user bases. Notable examples like Gmail, Instagram, and Twitter underscore their effectiveness and adaptability in the market.
In-app advertising refers to the monetization approach frequently used by app developers, where they receive compensation for displaying ads on their app. This practice is facilitated by mobile app advertising networks that serve as intermediaries between advertisers and developers.
The process involves the app sending a request to the network for an advertisement, which the network responds to by using complex algorithms to identify and deliver the most lucrative ad to the user in real-time.
App developers can integrate a diverse range of mobile ad formats into their app to enhance monetization, such as video ad units, mobile app display ads, and native mobile app ads.
In the realm of mobile app development, in-app advertising represents a crucial revenue stream. Through strategically placed advertisements delivered within a mobile application, developers can monetize their offerings. To enable this, a mobile ad network serves as an intermediary platform that facilitates the connection between developers and advertisers.
The mobile advertising landscape has undergone significant changes over the years. With an increasing number of mobile apps competing for user attention, developers need to remain current with the latest ad formats and techniques to remain profitable. Programmatic ad buying and real-time bidding have emerged as game-changing technologies that enable developers to automate the management of ads, resulting in more efficient operations.
Furthermore, advertisers have innovated their ad formats to improve integration with the mobile user experience. The latest ads can showcase key app features and incentivize users to upgrade, while gaming apps can offer free levels before requiring payment to unlock the full game. This intuitive approach ensures that ads blend seamlessly with the mobile user experience, enhancing user engagement and retention.
Integrating mobile app ads seamlessly into your game loop can enhance the user experience, and offering users rewards in exchange for watching or engaging with ads at specific points in the app lifecycle can create a positive association with the ads.
Furthermore, ad units that are designed to work in conjunction with your in-app economy can drive in-app purchases, with users who engage with rewarded ads being up to 6 times more likely to make an in-app purchase.
In-app ads that are carefully tailored to your game and integrated in the right places with appropriate capping and pacing can also boost user engagement, improve retention, and increase the lifetime value of users.
In the context of in-app advertising, the process of delivering ads within mobile applications involves a series of steps. Initially, when an ad is requested, it undergoes mediation through a specialized solution. This mediation solution acts as a bridge between the ad request and the ad network.
Next, the ad request is transmitted to the ad network, where it is subjected to a complex process of analysis to identify the most lucrative ad option for that particular user. Through advanced algorithms and real-time data analysis, the ad network determines the optimal ad to present to the user, based on factors such as user demographics, interests, and previous behaviors.
Crucially, ad networks have the ability to promote apps to a vast number of users and attract high-quality traffic by selectively targeting those users who are most likely to engage with the advertisement. This targeted approach ensures that advertisers can maximize their return on investment while delivering valuable content to users.
Just as any technological field experiences moments of calm before waves of innovation, the realm of in-app advertising had its tranquil period known as the waterfall era. During this phase, app publishers prioritized demand sources based on historical data about estimated cost per thousand impressions (eCPM). For a considerable time, the waterfall approach dominated in-app advertising. However, modern advertisers and publishers have recognized a more optimal solution: in-app bidding.
In-app bidding represents an advanced advertising strategy where mobile publishers auction their ad inventory, leading to a simultaneous bidding competition among all their advertisers. This heightened competition results in significantly higher prices (CPMs) earned by the publisher compared to alternative methods.
In-app bidding isn't an entirely new concept; it's essentially the application-oriented version of header bidding, a desktop technology in operation since 2015. Header bidding enabled web publishers to partner with third-party header bidding providers and insert code into a web page's header. This code allowed these providers to access multiple ad exchanges, which could then bid at the same time on a publisher's inventory. Consequently, header bidding ensured publishers secured the best price for each impression, with the highest bidder among advertisers winning the impression.
Until recently, mobile apps lacked a bidding mechanism, even though our predominantly mobile-centric world required a model that optimizes CPMs for publishers, similar to header bidding's impact on the web. Technical barriers and the absence of distinct headers within apps hindered the widespread adoption of such a solution on mobile platforms. However, these technical challenges have now faded away, marking the beginning of the in-app bidding era.
Despite its programmatic nature, in-app bidding differs from conventional programmatic methods, often known as the "waterfall." In the waterfall approach, publishers' mediation platforms prioritize ad networks based on past rates for a particular impression. In contrast, in-app bidding enables publishers to present their inventory in auctions beyond the waterfall framework. This allows various ad networks to bid simultaneously on the publishers' inventory, ultimately securing the highest achievable CPM.
In-app bidding effectively reduces fragmentation among demand sources by allowing all advertisers to bid on ad impressions from multiple sources at the same time. Conversely, this means that several advertisers gain insight into a given publisher's inventory, thus increasing yield for the publishers. Essentially, this democratized advertising solution benefits both advertisers, who access more publisher inventory, and publishers, who make their inventory available to a wider range of advertisers.
In-app bidding transforms the competition for impressions by enabling advertisers to compete in real-time, free from a hierarchical structure of demand partners. This empowers publishers to consistently achieve the highest CPMs for their inventory. By eliminating the constraints of averages and allowing individualized assessment of each bid, the highest bidder consistently wins the impression. This increased competition among advertisers further boosts CPMs, providing significant advantages to the mobile gaming industry.
In a nutshell, everyone involved in the mobile gaming industry stands to benefit from the emergence of in-app bidding. Publishers experience increased revenue, advertisers access a broader inventory range for better cost attribution, and consumers encounter more relevant ads.
For publishers, in-app bidding offers three main advantages:
Increased Demand: The mechanism increases the demand for ad inventory as multiple demand sources bid on each impression, driving CPM and Average Revenue Per Daily Active User (ARPDAU) higher.
Total Transparency: All demand sources are treated equally, fostering a fair bidding environment for each source.
Enhanced Efficiency: In-app bidding eliminates manual management and continuous optimization, operating fully automated. Every demand source has an equal chance to bid, consistently yielding maximum revenue. This automated buying process expedites the shift from historical to real-time pricing.
In essence, in-app bidding emerges as a transformative force, reshaping the landscape of in-app advertising and providing multifaceted benefits for all stakeholders.
The install referrer is a unique, non-identifiable string exclusive to Android platforms, designed specifically for monitoring the performance of installation-based advertising campaigns. This digital identifier plays a crucial role in the app marketing ecosystem by allowing marketers to pinpoint the source of app installations with remarkable accuracy.
An install referrer is composed of multiple pieces of information that collectively provide a detailed account of an app's installation process. Exclusive to Android devices, this tool empowers marketers to track and attribute ad engagements directly through the Android application marketplace. Recognized for its precision, the install referrer serves as a cornerstone in the realm of deterministic attribution techniques.
Common Platforms and Integration
While commonly associated with the Google Play Install Referrer API, the concept of install referrers extends to other marketplaces such as Xiaomi, Samsung, and Huawei. These platforms have developed their own versions of install referrer tracking, broadening the scope for marketers to assess ad performance across a diverse range of Android app stores.
Operational Mechanism
The functionality of an install referrer is triggered when a user engages with an advertisement to download an app. This action appends specific parameters to the download URL, which are then conveyed to the respective app store (Google Play Store or an alternative, depending on the device's setup) at the time of click. Following the app's installation, these parameters are relayed to the attribution provider via an Application Programming Interface (API), enabling precise tracking of the install source.
Supported App Stores
Precision: Install referrers are heralded for their deterministic nature, offering unparalleled accuracy in measuring user engagement with ads. This method ensures the highest possible fidelity in mobile attribution.
Reliability: By embedding a custom attribution ID within the referrer string, install referrers obviate the need for external network configurations, thereby simplifying the attribution process.
While primarily associated with Android devices, the concept of install referrers is part of a broader strategy that encompasses cross-platform attribution modeling. This approach allows marketers to maintain a cohesive attribution framework across different operating systems, ensuring consistent tracking and analysis of user acquisition efforts. The adaptability of install referrer technology facilitates seamless integration with various analytics and marketing platforms, enhancing the ecosystem for mobile app marketers.
Despite its advantages, navigating the install referrer landscape can present challenges, such as discrepancies in data reporting standards across different app stores and potential privacy regulations impacting data collection practices. Marketers must stay abreast of evolving privacy standards and technological advancements to leverage install referrers effectively. Continuous engagement with attribution and analytics providers ensures that marketers can adapt to these challenges, employing best practices for data accuracy and privacy compliance.
The future of install referrers looks toward further innovation in attribution technologies, with a focus on enhancing privacy while maintaining accuracy. As the digital ecosystem evolves with new privacy regulations and technological shifts, the methodologies behind install referrers will also adapt. Emerging technologies, such as machine learning and artificial intelligence, are expected to play a significant role in refining the attribution process, offering predictive analytics and deeper insights into user behavior.
The install referrer remains a pivotal element in the mobile marketing and attribution landscape, providing a robust framework for tracking ad performance and optimizing marketing strategies. As the industry continues to evolve, the role of install referrers will adapt, ensuring marketers have the tools necessary to navigate the complex digital advertising ecosystem effectively. Embracing these changes and challenges will be key to leveraging the full potential of install referrers in driving app growth and user acquisition.
Incentivized traffic refers to the flow of visitors who are motivated by rewards such as cash, gift cards, discounts, whitepapers, free eBooks, or game tokens, to visit specific websites. This type of traffic is particularly beneficial as it drives more visitors to websites, leading to increased customer engagement and valuable customer data.
However, incentivized traffic can pose a significant risk when third-party promoters compensate individuals for visiting your site without your knowledge or consent. Compared to unincentivized traffic, incentivized traffic is widely considered to be of lower quality, as visitors tend to perform only the minimum required action to obtain the reward.
Rewards can take various forms, such as cash, points, or other types of incentives. While incentivized clicks and traffic are the most commonly used incentive-based actions, incentivized registrations are also prevalent.
Incentivized traffic presents a convenient means of boosting website traffic, thereby increasing the potential for acquiring new customers. This strategy revolves around providing incentives to prospects to encourage them to explore your offerings. By clicking on a link, customers can access various rewards, such as a discount, whitepaper, upgraded product, eBook, or gift. Incentivized traffic campaigns aim to focus on driving customer interest, motivating them to engage with your content, and ultimately performing the desired action to obtain the reward.
Beyond achieving a rapid customer acquisition rate, incentivized traffic also offers additional benefits. For instance, it can increase customer engagement, leading to a higher number of downloads and acquisitions, thereby positively impacting your search engine ranking. Consequently, more users will be exposed to your website.
For example, a mobile app with a high number of installs and increasing popularity on Google Play or App Store can benefit from increased visibility over time. Companies can expect to see positive results from incentivized traffic campaigns in as little as three days.
There are several types of incentives that companies can offer to enhance customer acquisition and drive traffic to their websites. Rewards are a powerful motivator and can be customized to suit the desired action. Content locking is a popular technique used by many websites that require users to answer a series of questions before accessing the content. Similarly, gaming sites and apps often use offer walls to reward gamers with game tokens for referring friends or sharing information about their accounts on social media. Additionally, they attract traffic by providing cash rewards, discounts, access to materials, gifts, and other incentives.
Incentives are particularly popular in mobile gaming as they incentivize users to play more frequently and progress through the game. Companies can offer their users exclusive bonuses as rewards.
A Google Android instant app refers to a compact software application that allows users to sample a segment of a native Android app without the need for installation on their device.
Instant apps function as native containers, possessing access to a device's hardware and behaving like conventional local apps. A distinctive trait is that these apps do not occupy storage space on the device.
To engage with instant apps, Android users must initially activate the feature by navigating to Settings, then Google, and finally toggling on the instant app option.
Android users can encounter instant apps through various channels. One method involves conducting a Google search for a specific app. If an instant app variant is offered, users can tap the "Try it Now" button to initiate the instant app. Additionally, instant apps can be found on the Google Play store, where Google has integrated them into Google Play Instant.
The concept of instant apps was unveiled by Google during its annual I/O developer conference in May 2016. Subsequently, the following year's conference marked the expansion of instant app availability to all Android developers. Google introduced support for developing instant apps with the release of Android Studio 3.0 in October 2017.
In the context of application, instant apps hold significant potential for e-commerce enterprises and game developers. Notably, The New York Times introduced an instant app version for its crossword game, enhancing its shareability and discoverability. Game developers benefit from instant apps by enabling users to engage with specific game levels before committing to the full app. Similarly, e-commerce organizations can leverage instant apps to encourage users to download the complete application, thereby boosting adoption rates. Another application involves swiftly accessing one-time apps, such as quickly initiating a parking payment via an instant app scan.
A primary advantage of instant apps is their enhanced discoverability, alleviating the challenge of app visibility within crowded app stores. Users find it easier to engage with instant apps, potentially reducing negative reviews stemming from dissatisfaction. However, instant apps also pose security concerns due to their modular structure, which could increase potential vulnerabilities. These apps exhibit limitations such as the inability to utilize background services, notifications, access external storage, or obtain device identifiers.
From a developer's standpoint, creating instant apps is relatively straightforward and doesn't necessitate additional expertise. Developers can opt to build instant apps from scratch or transform existing apps into instant versions, facilitated through Android Studio. Despite the simplicity, challenges emerge during instant app development, particularly when converting a traditional app into an instant one. This process demands modularization of the app's code components. While some developers are accustomed to modularization, it can be arduous and time-consuming for others. Meeting the 4 MB size requirement for app modules can necessitate developers to refactor the app, or in certain cases, developers may struggle to meet this size constraint.
A lookback window refers to the specified timeframe following an advertisement's click or view, during which an app installation can be credited to that advertisement. This window is pivotal in determining the effectiveness of an ad in prompting a user to download an app.
Advertisers and attribution providers utilize lookback windows to assess whether a particular ad click or view was instrumental in a user's decision to install an app. This process is vital for understanding and optimizing ad performance.
Standard 7-Day Window: By default, if a user engages with an ad from Network X and installs the corresponding app within seven days—without interacting with another ad in the interim—Network X is attributed with the installation. Advertisers can often adjust this period to align with their specific attribution requirements.
24-Hour Probabilistic Modeling: In scenarios lacking a device ID (e.g., Google Advertising ID or Apple’s IDFA), probabilistic modeling steps in. This approach, which estimates the likelihood of an ad leading to an install within a 24-hour frame, relies on statistical probabilities and, while not infallible, boasts high accuracy within its limited timeframe.
Self-reporting networks differ from their counterparts by not directly reporting each interaction. Instead, they establish a data-sharing agreement with attribution partners. Upon detecting an install, the attribution provider syncs the device ID with the network using a specialized API. This process enables the network to report any ad clicks or views associated with that device ID within the established lookback window.
Lifetime Value (LTV) is a significant metric used to estimate the average revenue generated by a single app user or customer throughout their entire lifespan, whether as a free or paying user or customer. This metric is closely related to Customer Lifetime Value (CLTV), and it helps companies make informed decisions on how much they can spend to acquire a new user or customer.
LTV plays a crucial role in enabling companies to understand potential profitability, scale their marketing budgets, forecast revenue, and more. The calculation of LTV varies based on whether the app is a paid app, an ad-supported app, or a subscription business. Nevertheless, it is an essential tool for analyzing the ROI of marketing efforts.
Growth marketers can calculate the payback time for upfront advertising or marketing costs and the expected profit from each customer over their lifetime by knowing their user or customer lifetime value and their user or customer acquisition cost (CAC). However, as each user or customer will differ based on their level of engagement, retention rate, and ultimate value to the company, this metric is an estimate that fluctuates over time.
When it comes to customer valuation metrics, two terms that are often used interchangeably are LTV and CLV. However, there are some subtle differences between the two.
Typically, CLV is used to measure the total value that an individual customer brings to a business over the course of their entire relationship with the company. On the other hand, LTV is a metric that provides an estimate of the average value of a business's entire customer base, including both paying and non-paying users or customers. In other words, while CLV is focused on the value of a single customer, LTV looks at the bigger picture and considers the average value of all customers.
The importance of LTV cannot be overstated, especially in the free-to-install app economy. When combined with the average revenue per user, LTV becomes a critical metric for determining the potential revenue or value of your users.
Here are some of the key reasons why measuring LTV is so important:
Improve your strategies: If you don't measure LTV, you can't improve it. Once you begin measuring LTV and breaking down its various components, you can employ more targeted strategies around pricing, advertising, and user retention. This helps you achieve your goals of improving your user experience and increasing profit.
Better user acquisition decisions: By knowing what to expect in terms of average earnings per user, you can increase or decrease your spending on user acquisition to maximize profitability and continue attracting the right audience.
Improve forecasting: LTV predictions can help you make forward-looking decisions around ad spend. LTV forecasting minimizes the risk of underspending and missing out on potential business, or overspending and wasting your money in the wrong places.
Boost customer loyalty and retention: When you consistently provide value to your customers, in the form of a great, intuitive app, outstanding customer support, or an excellent loyalty program, customer loyalty and retention tend to soar. Focusing your efforts on users with higher LTV will enable you to drive retention, resulting in lower churn rates, more referrals, and positive reviews.
Drive recurring purchases: LTV allows you to measure web visits or app usage per year or over your users’ lifetime. You can then use that data to implement strategies that increase repeat business.
Charge up profitability: Higher LTV leads to bigger profits. By keeping users for longer stretches of time and building a model that encourages them to spend more, you should see the benefit show up on your bottom line.
Calculating LTV requires a methodology for aggregating and calculating ad monetization for an app with ad revenue. For an app that monetizes largely based on in-app purchases, IAP revenue is generally much easier to obtain good information on immediately. In a subscription-based business, LTV can be calculated by dividing the average amount a customer spends each month or the average monthly recurring revenue (MRR) by the churn rate.
For non-subscription businesses such as eCommerce, LTV refers to the average total revenue from a typical customer, including all their repeat purchases and upsells over a given time period. This can be calculated as the Average Order Value multiplied by the Purchase Frequency multiplied by the estimated customer lifespan. Estimating the customer lifespan in eCommerce can be challenging as customers may end up making a repeat purchase two or more years in the future. In such cases, LTV can be estimated based on specific time frames, such as the monthly or annual LTV of a customer.
LTV is critical for several main use cases, including budgeting marketing expenses, estimating the time to recoup marketing investment, understanding customer acquisition costs and profitability, and forecasting revenue. It costs less to keep existing customers than it does to acquire new ones, so increasing the value of existing customers is an excellent way to drive growth. Each of these use cases is centered around resource allocation, profitability, and having an accurate view of the company’s ROI on a per-customer basis.
Mobile Attribution
Mobile attribution is the process of identifying which marketing campaigns, media partners, and channels are responsible for driving specific app installs. It links app installations to the corresponding marketing efforts, enabling marketers to understand the effectiveness of their strategies.
Mobile attribution is crucial for marketers as it allows them to connect their marketing actions directly to outcomes. By leveraging attribution insights, mobile app marketers can measure and optimize their user acquisition campaigns and overall marketing performance. Additionally, understanding how in-app events, occurring post-installation, influence overall strategy is essential for refining marketing efforts and maximizing return on investment (ROI).
Attribution insights guide marketers on where to invest their marketing budget effectively. These insights are provided by attribution providers, which fall into two categories: biased and unbiased.
Biased Attribution Providers: These providers may have a conflict of interest because their business model includes activities like selling data or engaging in the buying and selling of mobile ad media. This arrangement can potentially affect their impartiality and business practices.
Unbiased Attribution Providers: These providers concentrate solely on attribution, ensuring their impartiality and independence. They are regarded as trustworthy third parties that can accurately measure and report on campaign performance. Unbiased providers play a crucial role in resolving reporting discrepancies between buyers and sellers in mobile advertising.
The relationship between an attribution provider and its clients is heavily reliant on trust. This foundation of trust is vital for the provider's data and services to be considered reliable. If this trust is compromised, it becomes exceedingly difficult for the provider to regain its credibility and reliability in the market.
Mobile attribution relies on a variety of technologies to accurately track and attribute app installs and in-app activities to the right marketing sources. These include:
While mobile attribution is a powerful tool for marketers, it faces several challenges:
Privacy Regulations and Device Restrictions: With increasing privacy concerns and regulations (such as GDPR in Europe and CCPA in California), along with changes in device-level permissions (e.g., Apple's App Tracking Transparency framework), the ability to track and attribute user actions is becoming more complex. These changes necessitate ongoing adaptation and innovation in attribution methods.
The future of mobile attribution lies in the development of more sophisticated and privacy-conscious tracking methods. Marketers and attribution providers will need to balance the demand for detailed, actionable insights with the need to respect user privacy. Emerging technologies, such as machine learning algorithms, can help in identifying patterns and improving the accuracy of attribution models in a privacy-centric world.
Effective mobile attribution is not just about tracking; it's about integrating these insights into broader marketing strategies. This involves:
Mobile interstitials are full-screen advertisements that appear at certain points within a user's interaction on a website, app, or game. These ads emerge at natural pauses, such as transitioning between game levels or while navigating between articles on a news site.
Mobile interstitials are immersive, full-page ads that integrate into the user experience. Users can either engage with these ads by clicking through to their destination or dismiss them to continue with their current activity. These ads can be static images or dynamic media, like videos.
In the context of mobile websites, interstitials appear as full-page ads that display between page views. They provide a significant revenue stream for publishers by allowing effective monetization of web pages.
Web interstitials are triggered by user-initiated navigation. However, they are less common on websites featuring continuous scrolling or single-page applications (SPAs), where page navigation is minimal.
Similar to their web counterparts, mobile app interstitials are full-page ads displayed during transitions within an app. They are particularly effective for brand advertising due to their large size and engaging nature. These ads often appear at natural intervals, such as at the start of an app, during game level transitions, or before a video.
Mobile interstitials are effective in countering issues like banner blindness in mobile environments. Their prominent placement ensures higher visibility, which can lead to increased user engagement and improved click-through rates (CTR). Recognized for their effectiveness, these ads are highly sought after by both brand and performance advertisers.
When implementing mobile interstitials, it's crucial to consider their placement and frequency:
Ad Placement: The timing and location of your ad are critical. Employ A/B testing to identify optimal moments for ad placement, such as at the end of a level in a game or between articles on a news site.
Ad Frequency: The frequency of your ads is equally important. Ads should enhance, not detract from, the user experience. Ensure ads are served at a balanced pace and only after user interaction with your app or site.
In 2017, Google introduced penalties for intrusive interstitials on mobile websites to regulate the user experience. Publishers and advertisers must comply with these guidelines to avoid penalties. Intrusive practices include:
Google penalizes sites with intrusive ads, potentially lowering their search rankings. However, these rules exclude interstitials required for legal reasons, like age verification or cookie usage.
Key Takeaways Mobile interstitials are impactful advertising tools that fit naturally within a user's journey on apps and websites. They are favored for their size and engagement potential. Proper placement and frequency are vital for effectiveness, and adherence to Google's anti-intrusive advertising policies is essential. By following best practices, mobile interstitials can significantly enhance user engagement without compromising the user experience.
Monthly Active Users (MAU) is a metric used to measure the number of unique users who engage with a particular website, app, or platform within a month. It is an important indicator of user engagement and the growth of a business.
MAU is calculated by calculating the number of unique users who have interacted with a particular platform in a month. A unique user is someone who has logged in to the platform, performed an action, or interacted with other users within a specified time frame.
Monthly Active Users (MAU) is a crucial metric for businesses as it helps in measuring user engagement and growth. It helps businesses determine their website or app's popularity among users and identify user behavior patterns.
Additionally, MAU can provide insights into the effectiveness of marketing campaigns, the impact of new features or updates, and the success of user acquisition strategies. It is also useful in identifying user retention rates, as it helps identify users returning to the platform and engaging with the business regularly.
Calculating Monthly Active Users (MAU) is a simple process that involves identifying the unique users who interacted with your platform in a month. You can calculate MAU by counting the number of unique users who have engaged with your platform, such as logging in or performing an action, within a month.
MAU can also be calculated by dividing the total number of unique users who engaged with your platform in a month by the total number of days in that month and multiplying it by the number of days the platform was active.
MAU = Number of Unique Users who interacted with the particular website, app, or platform in a month
Increasing Monthly Active Users (MAU) is critical to growing your business. You need to focus on user engagement and retention strategies to increase MAU. Here are some effective ways to increase MAU:
Enhance User Experience: Improving user experience by providing a simple and intuitive interface can lead to higher user engagement and retention.
Provide Valuable Content: Users engage with platforms that provide them with valuable and relevant content. By creating content that addresses the needs of your target audience, you can increase user engagement.
Personalized User Experience: Providing personalized experiences by recommending content or products based on user behavior can increase user engagement.
Implement User Acquisition Strategies: Use various channels such as social media, email marketing, and search engine optimization to acquire new users.
Gamify User Experience: Gamification is an effective way to engage users and increase user retention. By adding gamification elements to your platform, you can create a fun and engaging user experience.
Monthly Active Users (MAU) is a crucial metric for businesses as it helps in measuring user engagement and growth. It provides valuable insights into user behavior patterns and helps businesses to identify user retention rates. Businesses can increase MAU and grow their business by focusing on user engagement and retention strategies.
Monetized Daily Active Users (mDAU) refers to the number of unique users who access and interact with an app in a given day. The definition of an active interaction varies depending on the industry and the app, but it often includes actions like account log-in, transferring, adding items to a cart, or software usage. The active user is identified through unique identifiers such as email, user ID, cookies, or a combination of these. DAU is a crucial metric for businesses as a high number of daily active users suggests that the app is sticky and successful.
Measuring mDAU is crucial as it serves as a barometer of success. The higher the level of user engagement with an app, the more frequently they will access it. Engaged users are more valuable compared to unengaged ones, and therefore, measuring DAU helps app developers assess the earning potential of their app.
Calculating Monetized Daily Active Users (mDAUs) may appear straightforward, but it can be intricate based on the definition of active user engagement. Here's a technical overview of the process:
Example:
Result: The DAU count is 2 (User 1 and User 3 are considered active users).
Note that the definition of an active user heavily depends on the company's long-term growth objectives and business model. Different business goals result in different active user criteria.
To increase mDAU, you need a multi-channel engagement strategy that includes email, push notifications, in-app notifications, and re-engagement campaigns. Here's how to use each channel effectively:
A mobile measurement partner, often referred to as an MMP, plays a pivotal role in the world of app marketing. This entity is instrumental in helping apps gauge the effectiveness of their campaigns across various advertising marketing channels, media sources, and ad networks. This article delves into the significance of MMPs, their operational mechanisms, the advantages of partnering with them, and the recent developments in the realm of privacy that affect their functioning.
A mobile measurement partner can be thought of as a platform provider that spe8cializes in attributing, collecting, and organizing data related to mobile apps. Its primary function is to deliver a standardized assessment of campaign performance metrics. To draw an analogy from the world of sports, an MMP serves as an unbiased referee, arbitrating on attribution matters.
Within the tech stack of mobile marketers, an MMP holds a mission-critical position. It stands alone as the entity capable of providing an impartial view of the entire consumer journey. Consequently, MMPs empower marketers to discern which media sources truly deserve recognition for driving conversions. This, in turn, facilitates informed decisions regarding budget allocation. In essence, an MMP emerges as a trusted ally for marketers, aiding them in their quest for campaign success.
In its most fundamental form, an MMP aligns campaign engagements with app installations and post-install in-app actions. This is achieved through a combination of methods, including device IDs for user-level attribution, probabilistic modeling, and SKAdNetwork in iOS 14 for deterministic aggregated-level attribution. Employing a software development kit (SDK), a piece of code designed to collect measurement and attribution data, the app collaborates with the MMP to connect ad engagements with app installs and in-app events. These events may include progressing through game levels, completing registrations in a FinTech app, making purchases, and more.
Working with an MMP can yield a multitude of advantages for app marketers:
Improved App Performance, ROAS, and LTV: An MMP equips marketers with accurate and detailed data, enabling precise budget allocation and optimization of app performance, Return on Ad Spend (ROAS), and Lifetime Value (LTV).
Scalability in Marketing Efforts: As marketers expand their campaigns across various ad networks, MMPs provide the necessary tools to seamlessly explore new networks.
Effective Budget Utilization: An impartial MMP connects the dots within campaigns, helping pinpoint where credit is truly due. This ensures optimal budget allocation to high-performing campaigns.
Time and Resource Savings: Leveraging a universal SDK simplifies the measurement and attribution process across multiple ad networks. This results in significant time savings and eliminates the need to analyze numerous dashboards and spreadsheets.
Single Dashboard View: MMPs consolidate both raw and aggregated data from paid media sources and organic activities into a single dashboard. This provides marketers with a comprehensive overview of their campaign performance.
In short, any marketer with an app stands to benefit from partnering with an MMP. Mobile marketing is characterized by an abundance of data, making it a data-rich channel. However, evaluating campaign performance can be overwhelming due to the sheer volume of media data that requires analysis. Those who choose not to employ an MMP may find themselves expending significant resources and manpower attempting to decipher a multitude of dashboards and spreadsheets. Such manual analysis can lead to errors and missed opportunities for optimizing LTV and ROAS.
When entrusted to a reputable, unbiased provider, mobile attribution can precisely identify the value of specific channels, media sources, publishers, campaigns, and even creative elements. This allows continuous optimization of app performance and strategic growth.
Achieving a great Return on Ad Spend (ROAS) in a mobile campaign necessitates the thorough analysis and attribution of every consumer interaction. This is why the majority of marketers turn to MMPs to enhance their offerings to advertisers, maximize LTV, and gain clarity on where to allocate their budgets effectively.
Mobile attribution hinges on Software Development Kits (SDKs). However, managing multiple SDKs can pose challenges for mobile marketers, particularly when dealing with numerous ad networks. As marketers scale their efforts, the time, cost, and manual labor associated with adding new SDKs for each network can drain technical resources and adversely affect app performance.
MMPs address this issue by offering a universal SDK. This cost-effective solution connects advertisers to a vast mobile ecosystem of thousands of ad networks, eliminating the need for expensive, time-consuming development efforts.
When choosing a mission-critical solution like an MMP, there are four key factors to consider:
Security and Privacy: Ensure that your chosen MMP places paramount importance on security and privacy. Vigilant due diligence processes should be in place to protect your customers' data. Confirm that your attribution partner remains independent and unbiased.
Fraud Prevention: Opt for an established MMP with a significant market share, capable of leveraging extensive market intelligence to combat fraud. Choose an MMP committed to maintaining a fraud-free ecosystem and partnering only with ad networks equally committed to fraud prevention.
Data Accuracy and Comprehensive Features: Deep granularity and multi-touch attribution are essential for accurate analysis. Advanced capabilities like deep-linking, real-time data, omni-channel LTV, and cross-channel attribution provide invaluable insights.
Experience: MMPs with a solid reputation, a loyal customer base, and the resources to support your growing needs are crucial. Mobile attribution technology is complex, and a seasoned, trustworthy MMP can make all the difference.
Privacy has taken center stage in mobile app marketing, particularly on iOS. The introduction of the AppTrackingTransparency (ATT) framework has altered attribution models and campaign optimization. This framework requires users to opt-in before third-party advertisers can collect their user-level data. However, existing capabilities still apply to the larger Android platform and to opted-in iOS users.
In response to privacy concerns, the industry has embraced innovative solutions like incrementality measurement, predictive modeling, and web-to-app flows. These measures ensure that accurate measurability remains a core component of mobile marketing.
Key Takeaways
In summary, here are the top five reasons for incorporating an MMP into your marketing strategy:
Data-Driven Decision-Making: MMPs provide authoritative data for informed decisions, enhancing performance, ROAS, and LTV.
Universal SDK: A universal SDK simplifies the process, saving time and resources by connecting all ad networks.
Cost Efficiency: Avoid paying double or triple for attribution by relying on an impartial MMP to attribute credit accurately.
Single Dashboard View: Consolidate data from various sources into a single dashboard for a holistic view of your user funnel.
Expertise and Support: The complex mobile landscape requires the resources and expertise of MMPs to meet your marketing insights needs.
In conclusion, partnering with a reliable, unbiased MMP with a secure and scalable attribution platform can streamline your operations, allowing you to focus on strategic marketing and building a stronger, higher LTV user base. Mobile measurement partners are indeed indispensable allies in the ever-evolving world of mobile app marketing.
Non-organic installs, or NOIs, occur when a user proactively downloads and installs an application after encountering a marketing campaign. This phenomenon transpires as a result of user acquisition strategies that leverage paid and owned media.
The term "paid media" denotes a marketing approach that necessitates monetary expenditure, whereas "owned media" refers to promotional efforts over channels a business possesses and does not require payment. Both of these campaign types strive to guide potential users toward downloading an application by presenting them with ads and inciting them to click on them.
The primary dissimilarity between NOIs and organic installs is that, in the case of the latter, the user does not undergo any advertising influence. Instead, they actively seek out a particular application and independently initiate the installation process.
Non-organic installs are a crucial element of mobile app marketing and paid user acquisition, as they provide businesses with guaranteed scale and quality for their app. While organic installs are desirable for their high-quality users, who download the app without any influence from mobile marketing campaigns, non-organic installs are equally important to acquire a larger user base.
Organic installs lack attribution to a specific media source, making it challenging to determine what led a user to download the app. On the other hand, non-organic installs are linked to the media source that promoted the app installation, enabling businesses to measure their effectiveness accurately.
Non-organic installs generated by DSPs and marketing partners through advertising activities have become critical to every paid user acquisition strategy. They offer high scalability for apps and high-quality users that generate in-app revenues, making them an attractive proposition for advertisers. As a result, businesses are increasing their budget allocation towards a well-structured and effective paid user acquisition strategy that includes non-organic installs.
The rising trend in non-organic installs has led to the development of new strategies and activities by advertisers aimed at increasing non-organic traffic and installs and enhancing the overall quality of such traffic. This trend reflects the growing importance of non-organic installs and their potential to drive growth and revenue for businesses in the highly competitive world of mobile app marketing.
Non-organic installs refer to app downloads that directly result from marketing activities driven by paid user acquisition campaigns. In contrast to organic installs, which occur when users are not directly influenced by ads, non-organic installs are linked to the media source responsible for prompting the installation, providing valuable attribution for DSPs and other media sources. Additionally, non-organic installs deliver high-quality traffic for advertisers and their apps, making them a crucial component of any successful mobile app marketing strategy.
A private marketplace is a platform for buying and selling mobile app inventory that is only available to a select group of pre-approved buyers and sellers. These marketplaces are typically invitation-only and offer a more exclusive and high-quality inventory than open marketplaces. Private marketplaces can offer a variety of benefits to both buyers and sellers, such as increased transparency, improved targeting, and better control over pricing and inventory. Overall, it can be a great way for both parties to connect and engage mutually.
Here are 5 ways a private marketplace is different from the open market:
To summarize, a private marketplace can offer a more exclusive and efficient way to buy and sell mobile app inventory, while open marketplaces can be more widely available and less specific.
In the mobile app industry, private marketplaces are becoming more an more popular for various reasons. One of the primary reasons is the demand for quality inventory… brands and agencies are becoming more selective about where they place their ads. Private marketplaces offer a more exclusive and high-quality inventory than open marketplaces, which can be more valuable to advertisers.
Another reason is the improved targeting capabilities that private marketplaces offer. With more precise targeting, brands and agencies can reach their desired audience more effectively. Additionally, private marketplaces provide greater transparency which allows brands and agencies to make more informed decisions about where to place their ads.
Brand safety is also becoming a concern for many companies and private marketplaces offer a safer environment as they are only available to a select group of pre-approved buyers and sellers, reducing the risk of fraud and ad placements on inappropriate sites.
Private marketplaces also give brands and agencies better control over pricing, which can lead to more efficient and profitable advertising campaigns. On top of that, private marketplaces allow brands and agencies to establish direct relationships with publishers, which can be beneficial for both parties.
Programmatic guaranteed is a unique type of advertising deal that fosters direct interactions between publishers (sellers) and advertisers (buyers). In this arrangement, publishers commit to providing a fixed number of impressions, while advertisers commit to a pre-negotiated price for acquiring them.
Programmatic guaranteed represents a one-on-one deal structure that allows advertisers to purchase ad inventory directly from publishers. This entails the publisher delivering a pre-agreed volume of impressions, and in return, the advertiser pays a predetermined price. Once the deal is settled, the publisher reserves inventory exclusively for the advertiser in question.
Programmatic advertising has been around for decades, but programmatic guaranteed is a relatively recent innovation, emerging in 2015. It came into being as a response to the frustrations of advertisers and publishers with the limitations of traditional programmatic deals, such as real-time bidding and private auctions.
Both advertisers and publishers have the ability to initiate programmatic guaranteed deals. Publishers can utilize platforms like Google Ad Manager to create proposals, which they then send to suitable advertisers. Interested advertisers can review the inventory and assess the proposal before making their final decision.
Conversely, advertisers can analyze various publishers and reach out to them for negotiations on programmatic guaranteed deals. Once the deal is sealed and the publisher agrees to reserve inventory for that particular advertiser, a unique deal ID is assigned, enabling the advertiser to display their ads.
Programmatic guaranteed leaves no room for ambiguity as every facet of a campaign is set in stone. This includes details like impressions, placement, prices, and dates. To facilitate this process, the advertiser-side agency must synchronize its data management platform (DMP) with the publisher's DMP, enabling them to thoroughly analyze inventory and audience data for campaign planning.
Amplified Revenue: Publishers can charge premium prices for inventory, avoiding the limitations of open auctions and gaining more control over pricing.
Improved Brand Safety: Direct communication with advertisers allows publishers to closely monitor campaigns and ensure brand alignment.
Enhanced User Engagement: Programmatic guaranteed enables the display of relevant ads, enhancing audience engagement and preserving the user experience.
Better Control: Enhanced transparency allows advertisers to plan campaigns with precise details on impressions and costs, reducing hidden expenses.
Improved Targeting: Advertisers can assess publishers' inventory and audience to target their ideal demographic effectively.
Enhanced Security: Direct communication reduces ad fraud risk, increasing the authenticity of impressions and clicks.
Increased Efficiency: Automation streamlines processes, freeing advertisers to focus on creating compelling campaigns.
Better ROI: Precise campaign planning and control lead to improved ad performance and a higher return on ad spending.
While programmatic guaranteed offers significant advantages, it also presents challenges:
For Publishers: It requires a substantial investment and technical knowledge, making it less accessible for new or small publishers.
For Advertisers: The cost can be higher, and there's no benchmark to assess price fairness. Advertisers must carefully evaluate publishers for reliability.
Both programmatic guaranteed and private marketplace involve automated advertising but differ in structure. Programmatic guaranteed relies on direct agreements, while PMP is an invitation-only auction with no fixed impression commitments.
Programmatic direct includes preferred deals, which offer preferential access to premium inventory at a pre-negotiated CPM without fixed commitments. Programmatic guaranteed, in contrast, locks in impressions and CPM.
Advertisers should weigh the benefits and limitations of programmatic guaranteed carefully, assessing whether it aligns with their specific requirements and budget. It's particularly suitable for large advertisers seeking precise ad placements.
Key Takeaways
Programmatic guaranteed streamlines ad purchasing with fixed impressions and prices, benefiting both publishers and advertisers. Cautious evaluation of publishers is essential to make the most of these deals.
Programmatic TV represents a revolutionary way of automating the purchase of ad slots. It's a data-driven, technology-driven approach to acquiring and delivering ads within television content. This encompasses:
Programmatic TV diverges from the industry norm, where advertisers traditionally rely on show ratings for ad placement. Instead, it employs audience data for highly targeted advertising, reaching specific consumer segments, like iPhone-owning men with a $40,000 income.
For marketers, the focus shifts from where the ad appears (e.g., X Games or The X Factor) to ensuring the ideal audience is engaged.
Programmatic advertising refers to the use of software for purchasing digital ads, streamlining a process that traditionally involved requests for quotes, tenders, proposals, and negotiations. Algorithmic software is the driving force behind programmatic ad buying, facilitating the buying and selling of online ad space.
This model bridges the gap between publishers (those with ad inventory) and advertisers (individuals or companies seeking ad space). It revolutionizes TV service scaling, ad buying, and delivery, enabling advertisers to swiftly find their target audience and deliver personalized ads.
Advertisers and marketers can automate in-depth consumer analysis and tailor ad content, creating a more effective advertising model. Meanwhile, publishers and distributors can deliver TV programs aligned with viewer preferences, such as gender and age.
There are four primary programmatic ad buying models, each with distinct approaches and ideal use cases:
Real-time Bidding: Ad spots are open for public bidding in real-time over the internet. The highest bidder secures the spot but pays only slightly more than the second-highest bidder.
Preferred Deals: Advertisers choose ad spots before real-time bidding auctions or private marketplaces, providing a preview of available ad space. A fixed price, known as spot buying, is agreed upon before the spot is claimed.
Private Marketplace: An invitation-only marketplace where publishers offer premium ad spots to select advertisers. This approach is commonly used by high-reach publications and websites, ensuring transparency in ad placement.
Programmatic Guaranteed: Advertisers and publishers negotiate ad spot terms directly, bypassing the bidding process. Advertisers can specify pricing, audience, and ad frequency, offering maximum control but at a higher cost.
The global programmatic ad spending market is poised to grow significantly, with an estimated increase of about $314 billion during 2022-2026, averaging around 26% per year. This growth is fueled by the distinctive advantages offered by programmatic TV in comparison to traditional media buying:
1. Enhanced Reach: Programmatic TV facilitates connections with local broadcasts, especially in smaller markets, enabling access to a broader audience. Publishers can attract national brands, commanding higher prices for ad inventory.
2. Richer Data: Programmatic TV harnesses diverse data sources, enabling the delivery of more personalized and relevant ads. Multiple data sets, including publicly available social data and set-top box viewership, collaborate to define brand behavior and optimize ad campaign performance.
3. Reduced Errors: Automation eliminates errors that often plague manual, complex processes in media buying. Advertisers and ad spot sellers benefit from streamlined workflows, with automated availability requests, proposals, and orders. This empowers advertisers to reserve ad spots across multiple local stations through a single dashboard while modernizing and streamlining inventory management.
Purchase fraud, primarily observed in the mobile application ecosystem, is a multifaceted type of fraud encompassing several deceptive practices. These malpractices range from using stolen credit card details to orchestrating complex schemes that exploit return policy loopholes, and even extending to the generation of entirely fake purchase events. This form of fraud poses significant challenges to businesses as it directly impacts their financial integrity and consumer trust.
In the context of mobile marketing, purchase fraud mirrors the fraudulent activities prevalent in traditional brick-and-mortar stores and online shopping platforms. However, it uniquely targets the financial transactions that occur within mobile applications. The term specifically refers to unauthorized or manipulative activities related to in-app purchases and events.
This segment of the marketing funnel is particularly vulnerable as fraudsters exploit it to reap unearned rewards. They do so by taking advantage of long-term value (LTV) based promotions or rewards that are designed to incentivize legitimate in-app events. By manipulating these systems, fraudsters can illicitly gain substantial financial benefits, which poses a significant risk to the integrity of mobile marketing strategies and can lead to considerable financial losses for businesses.
Purchase fraud can manifest in a variety of sophisticated forms, some of the most common being:
Credit Card Fraud: This involves the unauthorized use of stolen or counterfeit credit card information to make purchases. Fraudsters may acquire these details through various means, including phishing attacks, data breaches, or skimming devices.
Exploitation of Return Policies: Some fraudsters meticulously study a company's return and refund policies to find loopholes that can be exploited for financial gain. This often involves returning purchased items under false pretenses or manipulating the system to receive refunds for items never purchased.
Falsification of Purchase Events: In a more technologically advanced approach, fraudsters may create false scenarios or use bots to simulate purchase events within apps. These simulated events are designed to trick systems into believing that legitimate transactions have occurred, thereby triggering unwarranted rewards or payouts.
Understanding and addressing these varied forms of purchase fraud is crucial for businesses, especially those operating within the mobile app space, to safeguard their revenue and maintain consumer trust.
Queries per second (QPS) is a metric used in online systems to measure the number of requests for information that a server receives per second. It is a critical parameter in evaluating the performance and capacity of information retrieval systems, such as search engines and databases. In the context of digital advertising, a high QPS measure is desirable as it indicates that the ad network or exchange has sufficient capacity to handle a high volume of requests.
Calculating QPS is a straightforward process. If you already know the number of queries that your system processes per second, you can use the following formula to determine your QPS rate:
QPS = (number of queries per second) x 60 seconds per minute x 60 minutes per hour x 24 hours per day x 30.41 average days per month.
By applying this formula, you can determine your system's average monthly query total. It is essential to monitor QPS regularly to ensure that your information retrieval system can handle the expected level of traffic without affecting performance or causing system failures.
In the bustling realm of programmatic advertising, Real-time Bidding (RTB) emerges as a game-changing process. In essence, RTB is the method through which advertisers seamlessly bid for ad impressions auctioned by publishers, all within the split-second timeframe it takes for a webpage to load. This instantaneous auction process lies at the heart of the programmatic advertising landscape, transforming how digital ad space is bought and sold on a per-impression basis.
RTB functions as a fundamental pillar of programmatic advertising, shaping the way digital ad space is acquired in real time. Imagine the moments between levels in a mobile game when an ad appears — that's the precise instance where an RTB auction unfolds. Advertisers eagerly compete, vying for the chance to present their ads to users. In this swift process, the highest bidder emerges victorious, ensuring a seamless and uninterrupted user experience.
It's easy to confuse RTB with programmatic advertising, but they are not interchangeable terms. While programmatic advertising encompasses the entire digital ecosystem facilitating real-time ad buying and selling, RTB specifically pertains to the rapid bidding process. Moreover, it's important to note that programmatic advertising can operate independently of RTB. Alternative strategies, such as private marketplaces and programmatic guaranteed deals, offer avenues for advertisers and publishers to negotiate directly, bypassing the open bidding approach of RTB.
To understand how RTB works, one must delve into the intricacies of its key players:
Upon a user's arrival at a webpage or app, the SSP triggers a bid request to the ad exchange, signaling the availability of ad inventory. This bid request carries user data and publisher requirements. Subsequently, an auction ensues, with DSPs swiftly evaluating bid requests and determining the optimal bids. The highest bid secures the advertising spot, leading to the instant display of the chosen ad to the user, all behind the scenes.
RTB offers a plethora of advantages, making it a preferred choice for many in the digital advertising sphere:
However, RTB is not without its challenges:
Header bidding, also known as advanced bidding or pre-bidding, represents a subset of RTB, adding a layer of complexity to the process. Unlike traditional RTB, where ad exchanges conduct individual auctions sequentially, header bidding enables multiple ad exchanges to bid simultaneously. This approach maximizes exposure for publishers, resulting in higher yields, improved fill rates, and augmented revenue streams.
In the realm of programmatic advertising, two distinct approaches coexist: RTB and programmatic buying. While RTB relies on the auction model, programmatic buying involves direct sales between publishers and advertisers. Negotiated for a fixed price and duration, programmatic buying is prevalent in premium markets, offering meticulous control over ad placement and context.
As the smartphone market burgeons, a mobile-first approach to advertising becomes imperative. Mobile RTB allows advertisers to bid for space on mobile sites and apps, ensuring targeted and relevant ad placements. In parallel to web-based header bidding, mobile apps utilize in-app bidding, enabling app publishers to secure competitive rates for their ad space while delivering tailored ads to users.
Looking ahead, the future of RTB is poised for significant transformations, driven by emerging trends and challenges:
In essence, RTB stands as the cornerstone of programmatic advertising, offering advertisers the opportunity to bid for ad impressions in real time. Its precise audience targeting and real-time campaign optimization benefit both advertisers and publishers, fostering efficiency and revenue growth. However, challenges related to human oversight, content relevance, and evolving privacy regulations persist, demanding innovative solutions.
Header bidding, programmatic buying, and mobile RTB provide nuanced approaches tailored to diverse advertising needs, ensuring flexibility within the programmatic ecosystem. As the digital landscape evolves, RTB's future lies in embracing technological advancements such as AI and machine learning while overcoming privacy challenges. These innovations will shape the trajectory of RTB, ensuring its continued relevance in the ever-evolving realm of digital advertising. With each click, the world of RTB unfolds, offering a glimpse into the future of programmatic advertising.
In conclusion, Real-time Bidding (RTB) remains a driving force in the ever-evolving landscape of digital advertising. Its instantaneous nature, coupled with precise audience targeting, reshapes how advertisers and publishers engage with their audiences. While challenges such as privacy concerns and content relevance persist, innovations in AI, machine learning, and privacy-preserving technologies are paving the way for a more sophisticated and efficient RTB ecosystem.
As we navigate the digital frontier, RTB's ability to adapt and incorporate cutting-edge technologies will define its future. With each bid placed and each ad displayed, the world of RTB continues to evolve, offering endless possibilities for advertisers, publishers, and users alike. In this dynamic environment, staying at the forefront of technological advancements and embracing innovative solutions will be key to unlocking the full potential of Real-time Bidding in the years to come.
Reattribution refers to the attribution of this reinstall to a specific retargeting campaign therefore a specific traffic source.
Reattribution refers to the attribution of a re-install or opening event to a user who has been inactive on an app for an extended period. A reattribution signal denotes that the user has returned to the app as a result of a retargeting campaign.
It is common for users to permanently discontinue the use of an app by either uninstalling it or not opening it again. However, these users represent potential targets for re-engagement campaigns, given their prior indication of interest in the app. Reattribution distinguishes these re-engaged users from new users who are unfamiliar with the app.
Reattribution pertains to the process of attributing app installs to users who had previously installed the app, but subsequently removed it. The reattribution signal arises when a user, who had deleted the app but remained inactive for a certain duration, is targeted with an advertisement and then reinstalls the app. This new installation or usage phase can be credited to a different ad partner than the initial installation.
Following the removal of an app, there exists a reattribution window that denotes a specific time frame during which the user can reinstall the app. Typically, a user is considered inactive after seven days of uninstallation, and the reattribution window exists for another seven days. However, these time frames are adjustable according to the requirements of the app.
The primary reason for reattribution is that there was already an established attribution source. For instance, if a user initially installed an app through a particular advertising campaign but subsequently deleted it, and then reinstalls the app within the reattribution window via another retargeting campaign, the current installation must be attributed to the secondary source that caused the user to return to the app.
Reattribution provides app marketers with valuable insights into the campaign or creative that prompted a user's return to the app. This data can be used to fine-tune re-engagement campaigns and effectively recapture users who have been inactive for an extended period.
In addition to its usefulness for re-engagement efforts, reattribution plays a crucial role in evaluating the efficacy of retargeting campaigns for marketers. By tracking user activity against industry benchmarks and ideal user journeys, marketers can determine when users are not as active as expected and launch targeted retargeting campaigns to re-engage them.
A reattribution window is a specified duration during which the reinstallation of an app occurs.
This section introduces the concept of the re-attribution window, a crucial period where app reinstalls are not considered new installs, but rather as a continuation of previous engagements.
Definition and Duration: The re-attribution window commences upon a user's initial app install and persists for a predetermined period, typically set by the advertiser. The default duration is 90 days, but this can vary from 1 to 24 months based on advertiser preference.
Configuration and Impact: Found in the App Settings, the duration of this window can be customized. Notably, any alterations to this setting will influence future reinstalls, without retroactive changes to previous install data.
Examples of Adjusting the Window:
Use Cases of the Re-attribution Window:
Reattribution windows function as a predetermined period during which a conversion event cannot be regarded as a new install. Reattribution occurs when a user, who had previously uninstalled an app, reinstalls the app after engaging with a re-engagement campaign.
This re-installation event falls under the re-attribution window, which commences when the user downloads the app and can be customized to suit the advertiser's requirements.
Re-installs that occur within this window do not trigger new install postbacks, which are responsible for attributing credit to an ad network when a user clicks or views an ad before downloading. Instead, the installation is not attributed, and no install postback is transmitted to any media source. Furthermore, all subsequent in-app events are counted as organic.
Retargeting is a digital marketing strategy aimed at re-engaging potential customers who have previously interacted with your website or mobile app but did not complete a purchase. This technique is crucial in nudging these visitors towards completing a sale, effectively reducing the number of abandoned shopping carts.
At its core, retargeting works by placing cookies on your website visitors' browsers. These cookies track the users' online journey and trigger your ads to reappear on various platforms they visit thereafter. This constant visibility keeps your brand and products at the forefront of potential customers' minds, increasing the likelihood of them returning to complete a purchase.
Retargeting isn't limited to just one type of media. It can take various forms, including display ads on websites, personalized email campaigns, SMS notifications, and even social media ads. This multi-channel approach ensures a broader reach and reinforces the message across different touchpoints.
One of the key reasons retargeting is effective lies in its precision and personalization. Retargeting campaigns can deliver highly relevant ads by segmenting your audience based on their behavior or interests. This targeted approach often results in higher conversion rates as the ads resonate more with the needs and interests of potential customers.
The audience for retargeting is unique because they've already shown interest in your products or services by visiting your website. This prior interaction makes them more likely to engage with your ads, as opposed to someone who is encountering your brand for the first time.
While often used interchangeably, remarketing and retargeting have subtle differences. Remarketing primarily involves re-engaging customers through emails and is broader in scope, while retargeting specifically focuses on serving ads to potential customers based on their previous online behavior, primarily using cookies.
Behavioral targeting in retargeting focuses on the actions users have taken, such as visiting a specific product page. Contextual targeting, on the other hand, places ads based on the content relevance of the websites the user is visiting. Both strategies can be effective in retargeting, depending on the campaign goals.
Effective retargeting involves segmenting your audience and tailoring ads to each segment's interests and behaviors. The timing of these ads is also crucial, with different products requiring different retargeting time windows. For instance, travel-related products might need immediate retargeting, while luxury goods can have a longer retargeting timeframe.
In today's digital landscape, privacy concerns are paramount. It's essential to be transparent about the use of cookies and retargeting practices, ensuring compliance with data privacy laws like GDPR and CCPA. Customers should have clear options to opt out of tracking if they choose.
Retargeting should not exist in isolation but rather be integrated into your broader digital marketing strategy. This integration ensures a cohesive and consistent message across all marketing channels, enhancing the overall impact of your marketing efforts.
To gauge the effectiveness of your retargeting campaigns, track metrics such as click-through rates, conversion rates, and return on ad spend. Regularly analyzing these metrics helps in fine-tuning your strategies for better performance.
As technology evolves, so does the landscape of retargeting. Emerging trends include the use of artificial intelligence to predict customer behavior more accurately and the increasing importance of mobile retargeting as mobile commerce continues to grow.
Retargeting is a powerful tool in the arsenal of digital marketers, playing a critical role in converting potential customers into buyers. By understanding and implementing retargeting effectively, businesses can significantly enhance their online sales and overall marketing effectiveness.
