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 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 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.
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.
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.
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%.
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.
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.
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 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.
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:
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.
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.
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.
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.
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:
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.
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.
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.
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.
SDK spoofing is a ploy employed by cybercriminals to secure confidential details from an application. They do this by manipulating the SDK of the app, which is a conglomeration of instruments and libraries that creators employ to construct the app. By transforming the SDK, the attacker can make the app act as though it's from an alternate source, such as a contrasting app or platform. This can be utilized to circumvent security measures and obtain access to user data, including login credentials and individual information. Furthermore, once the attacker has access to the app, they can use it to disseminate malware or to initiate other cyberattacks. To thwart SDK spoofing, developers should utilize protective measures such as code signing and encryption to guarantee that the app's code and data are preserved, and to authenticate the legitimacy of the app.
One way to avert SDK spoofing is by utilizing code signing. This is a procedure where the application's code is digitally endorsed by the developer to affirm its legitimacy. This assists with guaranteeing that the app has not been meddled with, and that it's coming from a trusted source. Creators ought to likewise utilize encryption to secure the app's information, this will forestall unapproved access to client information and other delicate data.
Another approach to forestall SDK spoofing is by utilizing a portable security app that can examine the app for malware or other security dangers. This will assist with recognizing whether the app has been hacked or undermined and can alarm the client to take care of business. Moreover, app engineers ought to screen their SDKs consistently and ensure that they are utilizing the most recent form of the SDK. This will help to guarantee that any security imperfections have been fixed, and that the SDK isn't being utilized maliciously.
A supply-side platform (SSP) is a powerful adtech solution that enables digital media owners and publishers to manage and distribute their ad inventories in an efficient and effective manner. Acting as a key component in the real-time bidding (RTB) process of programmatic advertising, SSPs provide publishers with the ability to optimize yield by connecting their inventory to multiple ad exchanges and demand-side platforms (DSPs) simultaneously. This allows for maximum revenue generation by exposing impressions to a wide range of potential buyers. In this context, SSPs are commonly referred to as yield-optimization platforms.
The commercials that Google serves you are the consequence of a intricate and variable procedure that involves both the advertiser and the publisher. Google exploits user conduct data to match the correct ads to the correct users, all in real-time.
Supply-side platforms (SSPs) carry out a vital role in helping the relationship between publishers and advertisers. They grant publishers to gain access to a more extensive crowd of potential purchasers by forwarding accessible ad impressions to multiple sources. This raises the possibilities of vending the publisher's stock at the highest achievable cost.
SSPs permit advertisers to purchase ad impressions across a large range of publisher sites, targeting exact users based on their online demeanor and central identifiers. This is attained through the real-time investigation of accessible impressions proffered by the publisher's SSP. Consequently, ads are exactly placed and served to the pertinent audience.
Increased Revenue: SSPs provide publishers with the capability to link their inventory to multiple ad exchanges and DSPs simultaneously, thereby expanding the range of potential buyers. This intensifies the rivalry for ad inventory, leading to higher ad prices and improved earnings for the publisher.
Optimized Yield: SSPs empower publishers to attain maximum returns by connecting their inventory to multiple ad exchanges and DSPs to optimize yield. This offers opportunities to gain revenue through exposing impressions to a wide range of potential buyers.
Improved targeting and audience segmentation: SSPs give advertisers the ability to purchase ad impressions across a wide selection of publisher sites, targeting specific users on the basis of their online behavior and key identifiers. This leads to more appropriate ads being served to the correct audience, thereby enhancing the performance of the campaign.
Furthermore, with the use of data management platform (DMP) which is often integrated with SSPs, publishers gain access to more data and insights to segment audiences and effectively target them.
Supply-side platforms (SSPs) facilitate the process of selling advertising inventory by connecting publishers to a wide range of ad buyers on various ad exchanges and networks and demand-side platforms (DSPs). They simplify the process of working with multiple ad networks or ad exchanges by establishing a connection with a larger number of advertisers.
A high-quality SSP makes the publisher's inventory available to a wide variety of buyers in the market, which increases the chances of selling the inventory at the maximum price. The ultimate goal for a publisher is to sell all the inventory at the highest price possible.
Utilizing a reputable SSP ensures a successful outcome for the publisher, as it guarantees access to multiple high-quality demand sources, which results in selling the inventory at the highest price possible.
View-through attribution, or VTA, is a method of measuring the effectiveness of mobile app ads. VTA is a way to track when a user views an advertisement within a mobile app and later converts within the app. This can help advertisers to get a better understanding of the impact their ads are having, and make more informed decisions about where to allocate their mobile app advertising budget.
VTA can be particularly useful for measuring the effectiveness of in-app display advertising, where users may be more likely to see an ad without clicking on it. It can also be beneficial for tracking the effectiveness of video ads and rewarded ads within mobile apps. It is important to note that VTA requires the use of in-app tracking tools, such as mobile SDKs, to track user behavior and attribute conversions to specific ad views. With the right tools in place, VTA can be a powerful way to gain deeper insights into user engagement within mobile apps and optimize mobile app advertising strategy.
VTA works by assigning a predefined "attribution window" for each campaign. This window, also known as the "lookback window", is the period after an impression when a conversion may be attributed to it. This conversion can refer to any desired action that you want users to take, such as app installs, re-engagement, purchases, and more.
Typically, the standard attribution window is 24 hours, which means any conversion within 24 hours after a user views an ad is attributed to that particular impression. However, it's worth noting that this window can vary depending on the market and industry. For instance, mature markets like the US and Singapore tend to have longer attribution windows, as users in these countries tend to take more time to convert.
Additionally, the attribution window is longer for industries like finance, education, and e-commerce, but shorter for gaming, health, and fitness apps. The reason for this is that downloading a game, for instance, doesn’t require as much thought as a financial app would. This leads to shorter attribution windows. It’s also understandable, considering that advertisers in these industries have established ad networks designed to convert users quickly.
Therefore, it's crucial for advertisers to choose the most suitable attribution window based on their industry and target market segment. And to ensure accurate and timely attribution of conversions, advertisers must share ad impression data with relevant ad networks. With the right VTA strategy in place, mobile app advertisers can gain a more complete picture of the impact their ads are having on user engagement and optimize their mobile app advertising strategy accordingly.
One of the main benefits of using view-through attribution (VTA) for your app is that it provides a more complete picture of the impact your ads are having. Traditional click-through attribution only tracks conversions that occur directly after a user clicks on an ad, but VTA also tracks conversions that occur after a user views an ad but doesn't click on it. This allows you to see the full impact of your ad campaign, including the effectiveness of your display ads.
Another benefit of using VTA is that it allows you to make more informed decisions about where to allocate your advertising budget. By tracking the full impact of your ads, you can see which ads are driving the most conversions and optimize your ad spend accordingly. Additionally, by understanding the attribution window that works best for your industry, you can make sure that your ads are reaching the right audience at the right time. Overall, using VTA can help you to better understand your target audience and increase the ROI of your mobile app advertising campaigns.
