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App Retention Rate

What is App Retention Rate?

In the dynamic landscape of digital businesses, app retention rate stands out as a pivotal metric that determines the success and sustainability of any mobile application. App retention rate, often referred to as user retention rate, specifically measures the percentage of customers who continue to engage with and use a mobile app over a defined period. This metric is particularly significant for subscription-based services, offering crucial insights into customer satisfaction, product appeal, and revenue stability. This comprehensive guide will delve deep into the nuances of app retention rate, its importance, calculation methods, and strategies to enhance it.

Importance of App Retention Rate

Understanding the app retention rate is essential for businesses for several reasons:

1. Revenue Stability: App retention rate directly influences a company’s revenue stream. Satisfied, retained customers are more likely to continue their subscriptions, ensuring a stable income for the business.

2. Customer Satisfaction: High app retention rates are indicative of satisfied users. When users find value in an application, they are more likely to stay engaged, leading to positive reviews and word-of-mouth referrals.

3. Cost-Efficiency: Acquiring new customers can be significantly more expensive than retaining existing ones. High app retention rates reduce the need for aggressive user acquisition strategies, saving valuable marketing resources.

4. Product Improvement: By analyzing patterns within the app retention rate, businesses can identify specific features or stages where users tend to drop off. This valuable feedback aids in refining the user experience and enhancing the app’s overall appeal.

Calculating App Retention Rate

Calculating the app retention rate involves comparing the number of users at the beginning of a specific timeframe with the number of users at the end of that period. The formula is as follows:

App Retention Rate=(Initial Users−Churned UsersInitial Users)×100%App Retention Rate=(Initial UsersInitial Users−Churned Users)×100%

Here, “Initial Users” represent the number of users at the start of the period, and “Churned Users” represent the users who ceased using the app during the same period.

Strategies to Improve App Retention Rate

Enhancing app retention rate requires a strategic approach and a deep understanding of user behavior. Here are some effective strategies to boost app retention rates:

1. Personalized User Experience: Tailoring the app experience based on user preferences and behavior fosters a sense of belonging and encourages prolonged engagement.

2. Regular Updates: Continuous improvement and the addition of new features keep the app fresh and exciting for users, reducing the likelihood of them switching to competitors.

3. Effective Onboarding: A seamless and intuitive onboarding process ensures that users quickly understand the app’s value proposition, reducing early churn rates.

4. Push Notifications: Thoughtful and relevant push notifications can re-engage users, reminding them of the app’s benefits and encouraging return visits.

5. Incentives and Rewards: Implementing loyalty programs, discounts, or exclusive content for long-term users creates incentives for continued app usage.

6. Feedback Loops: Actively seek feedback from users, addressing their concerns, suggestions, and grievances promptly. Users who feel heard are more likely to stay loyal.

7. Community Building: Encourage users to interact with each other within the app, fostering a sense of community. Social engagement often leads to higher retention rates.

Calculating App Retention Rate Using Cohort Analysis

Cohort analysis is a powerful tool for understanding app retention rates in specific user segments. By dividing users into cohorts based on criteria such as acquisition date or user behavior, businesses can gain valuable insights into user retention patterns.

1. Acquisition Cohorts: Dividing users based on acquisition date helps in understanding the retention rate of users acquired during specific marketing campaigns or timeframes. This information is invaluable for refining marketing strategies.

2. Behavioral Cohorts: Grouping users based on specific interactions within the app provides insights into which features or activities lead to higher retention rates. This data can inform app redesigns and marketing efforts.

In the competitive world of mobile applications, understanding and optimizing app retention rate is paramount to long-term success. By adopting data-driven strategies, staying responsive to user feedback, and continuously refining the user experience, businesses can not only retain existing users but also attract new ones through positive word-of-mouth and app store reviews. As the digital landscape evolves, businesses that prioritize user satisfaction and retention will undoubtedly emerge as leaders, shaping the future of the app economy.

App Store Analytics

App store analytics play a pivotal role in providing app owners with invaluable insights into the performance and reception of their applications.

What are App Store Analytics?

App store analytics encompass a spectrum of data insights crucial for gauging an app’s success. This data can be categorized into two primary tiers: basic insights readily available through app stores and more intricate analytics provided by third-party tools.

Basic app store analytics serve as an entry point, furnishing beginners with essential key performance indicators (KPIs) including download metrics, rankings, device demographics, geographical distribution, and revenue streams.

Third-party analytic tools, such as App Annie and Sensor Tower, offer a deeper dive into app performance. They enable app owners to conduct comparative analyses against competitors, employing various slicing and filtering mechanisms for enhanced granularity.

Key metrics measurable through advanced app store analytics encompass revenue streams, download rates, update frequencies, rankings, user reviews, and keyword-specific rankings.

Advanced app store analytics further facilitate comprehensive competitive analyses and vertical assessments. They provide in-depth reports and aggregated data tailored to individual app stores, making them indispensable for owners managing multiple applications or diverse app versions.

App Store Analytics Explained

App store analytics encompass a spectrum of data insights crucial for gauging an app’s success. This data can be categorized into two primary tiers: basic insights readily available through app stores and more intricate analytics provided by third-party tools.

Basic Insights:

Basic app store analytics serve as an entry point, furnishing beginners with essential key performance indicators (KPIs) including download metrics, rankings, device demographics, geographical distribution, and revenue streams.

Third-Party Tools:

Third-party analytic tools, such as App Annie and Sensor Tower, offer a deeper dive into app performance. They enable app owners to conduct comparative analyses against competitors, employing various slicing and filtering mechanisms for enhanced granularity.

Key Metrics:

Key metrics measurable through advanced app store analytics encompass revenue streams, download rates, update frequencies, rankings, user reviews, and keyword-specific rankings.

Advanced Insights:

Advanced app store analytics further facilitate comprehensive competitive analyses and vertical assessments. They provide in-depth reports and aggregated data tailored to individual app stores, making them indispensable for owners managing multiple applications or diverse app versions.

Utilizing App Store Analytics

Optimizing Performance:

App store analytics empower app owners to optimize their applications for better performance. By tracking KPIs such as user engagement, retention rates, and revenue generation, developers can make informed decisions regarding updates, feature enhancements, and marketing strategies.

Competitive Analysis:

Thorough competitor analysis is made possible through app store analytics. By benchmarking their apps against industry rivals, developers can identify strengths, weaknesses, and market trends, thus refining their own offerings for greater competitiveness and market penetration.

User Feedback Integration:

App store analytics allow for seamless integration of user feedback into app development cycles. By monitoring user reviews, ratings, and sentiment analysis, developers can address user concerns, improve user experience, and cultivate positive app perceptions.

App Tracking Transparency (ATT)

App Tracking Transparency (ATT) 

App Tracking Transparency (ATT), succinctly referred to as ATT, stands as Apple’s proactive privacy paradigm that mandates every iOS app to solicit user permission for data sharing. This is orchestrated through a pop-up interface, proffering users the choice to either authorize or decline data tracking.

What is App Tracking Transparency (ATT)?

 What exactly constitutes App Tracking Transparency (ATT)? Emerging subsequent to the iOS 14 launch (and enforced post iOS 14.5), the ATT privacy framework was introduced across the gamut of Apple devices. Its principal aim was to curtail the extent to which app developers could disseminate user data to external entities. This initiative has yielded consequential ramifications for the mobile advertising sector.

Preceding ATT, all iPhone users were involuntarily enrolled in data tracking, unless they had proactively opted out via the Limit Ad Tracking feature. In such instances, developers and marketers could access user-specific data and attribution through a distinct iOS advertising identifier labeled as IDFA.

Under the ATT umbrella, app users are compelled to actively opt into data tracking via a pop-up displayed by the app. Owing to the majority of users opting out, this has posed a considerable challenge for advertisers, publishers, and app developers alike, rendering the task of targeting precise demographics and refining campaigns based on high-performance user data significantly more onerous. Further elaboration on this will follow.

Visualizing App Tracking Transparency

 How does the interface of App Tracking Transparency manifest? The crux of ATT lies in the in-app pop-up, colloquially known as the ATT prompt. This interface queries users if they wish to “permit the app to track your activity across other companies’ apps and websites.” Users possess the liberty to abstain or assent, with the default setting being opt-out. While the wording of this prompt remains immutable, there exist strategies to augment opt-in rates, which we shall delve into later.

Not a Mandatory Fixture for Apps Apps aren’t under obligation to showcase the prompt since it functions on an opt-in basis. Should developers opt out of this display, they forego the collection of user-specific data. The ATT prompt serves as an avenue for apps to accumulate user-centric data, which in turn can augment performance and furnish insights for benchmarking, extrapolation, and more. Consequently, a substantial majority of apps (nearly 70%) opt to feature the prompt.

Pre-ATT Data Dynamics A Glimpse into the Pre-ATT Landscape 

Prior to Apple’s resolute focus on privacy, app developers and publishers reveled in unrestricted access to copious amounts of data. Apple operated on the Limited Ad Tracking (LAT) model, allowing users to renounce personalized advertising.

Though available to most users, a significant majority (around 70%) refrained from exercising the option to opt out of tracking. This facilitated publishers and advertisers in trading and disseminating user data among media companies, apps, and advertisers. This ecosystem engendered finely targeted ad campaigns based on behavior, demographics, and interests. Alas, optimization thrived at the expense of privacy.

ATT’s Ramifications on Advertisers 

Transitioning to ATT and Its Implications 

Although the shift from iOS’ opt-out model to an opt-in model did diminish tracking rates, global adoption of ATT still stands at a commendable 46%, albeit this figure pertains only to users who encountered the prompt. For advertisers, the crux of the challenge rests in the scarcity of IDFA attribution.

Navigating the Unfamiliar Data Terrain User-specific data and attribution have been pivotal for optimizing ad campaigns. Conversely, the dearth of data has proven detrimental for advertisers and publishers accustomed to working with granular user-level data, now rendered incapable of orchestrating finely targeted campaigns akin to the past.

In this context, it remains pivotal to acknowledge the ongoing industry transition. As we acclimate to the domain of aggregate-level data insights, the primary objective, innovation persists, and measurement is anticipated to be largely sustained. (Refer here for further insights.)

Unaddressed Segments in the Equation

 The initial challenge arises from a substantial user segment that eludes tracking. Users who previously opted out of personalized advertising (LAT users) are automatically classified as ‘denied’ to advertisers today, constituting over 30% of global iOS devices.

Moreover, 14% of Apple users employ restricted devices designated for minors, unknown age demographics, or educational purposes. Additional limitations on tracking could emanate from certain corporate-owned devices.

Navigating User Experience Concerns 

Certain app developers express apprehension regarding the intentionally unwelcoming verbiage (“allow app to track your activity across other companies’ apps and websites”), which could potentially exacerbate user churn and impede user experience.

Friction Arising from Dual Consent 

A noteworthy stumbling block involves dual consent when advertising across different apps. Users are required to provide consent twice for user data exchange between two distinct entities, effectively sealing the attribution loop. Consent must emanate both from the advertiser and the publisher. This dual opt-in dynamic significantly contributes to the diminished IDFA attribution rates, despite relatively high ATT opt-in rates.

Enhancing Opt-In Rates Strategies to Bolster ATT Opt-In Rates 

Having delineated the prevailing challenges presented by ATT, let’s embark on a journey through methodologies to elevate opt-in rates.

The keystone catalyst for elevated ATT opt-in rates is trust. An app with an established presence or a brand with inherent credibility garners greater user trust, rendering users more amenable to entrust their data. For nascent apps, cultivating a secure and trustworthy user experience is imperative.

Exploring Pre-Prompt Tactics

 Experimentation with diverse pre-prompt messaging emerges as a plausible approach. This involves an antecedent popup preceding the ATT prompt. Tailoring the messaging to accentuate the advantages of personalized advertising for users is pivotal. Conciseness, sincerity, and clarity should remain focal.

Strategizing Prompt Display: 

No universal panacea exists for determining the optimal prompt display timing. The decision hinges on user behavior and the value proposition your app extends. Identifying the funnel stage that aligns with your objectives is paramount.

Prompt Strategies Across Funnels 

Early Funnel: Inception of app usage, preliminary app session, early stage completion, initial app revisits Displaying the ATT prompt during the early funnel phase offers access to a substantial audience. This strategy gains efficacy when accompanied by high opt-in rates, substantiated by data indicating negligible churn or attrition due to ATT. However, this approach could be perceived as intrusive by new users.

Mid Funnel: Account establishment, app’s inaugural value juncture, meaningful engagement Positioning ATT prompts within the mid funnel hinges on user actions within the app. This could coincide with a pivotal “AHA!” moment when users discern the app’s intrinsic value. Timing the ATT prompt during this felicitous phase could capitalize on the positive user experience.

Lower Funnel: In-app purchases and beyond While this targets a narrower audience, engaging users in the lower funnel entails reaching a highly-trusted cohort. These individuals have already recognized the app’s utility, evident through their purchases.

The optimal choice hinges on your objective, audience profile, and their familiarity with your brand or app. A singular best practice remains elusive, with trade-offs between audience scope, timely campaign optimization, and distinctive opt-in rates.

Android’s Terrain: A Glimpse Unpacking Android’s Trajectory Apple’s proclamation was promptly followed by Google’s announcement in June 2021. This announcement heralded heightened privacy measures for all Android devices, a policy slated for Android 12 and beyond.

Parallel to Apple’s precedent LAT model, Google’s update empowers users to disengage from personalized advertising.

With Google poised to sunset cookies in 2023, conjecture mounts that Google might eventually curtail user-level data exchange via its GAID (Google Advertising ID), akin to iOS’ IDFA. Nonetheless, the constraints are anticipated to be comparatively less stringent than Apple’s stipulations.

Salient Insights for Users Equipping Users with Practical Insights

 Now that we’ve unraveled the nuances and significance of ATT, let’s delve into its implications for users. For iPhone, iPad, and tvOS users, evading ad tracking necessitates no action on their part.

Apple has enriched their informational guide “Day in the Life of Your Data,” elucidating the benefits of ATT for ordinary users.

Enabling ATT on Your Device

 On your iPhone, access Settings, followed by Privacy. A conspicuous orange icon denoting Tracking will appear. Clicking on it reveals a toggle labeled “Allow Apps to Request to Track.”

The master toggle dictates universal app tracking settings. Alternatively, users can independently designate tracking permissions for specific apps.

Revising App Tracking Choices

 Should users alter their preference, it’s seamless to inhibit the ATT prompt from appearing on iOS or iPadOS devices. The process mirrors activation, with the difference being the selection of the tracking toggle under Settings, Privacy, and Tracking. Simply toggling tracking on or off orchestrates the desired changes.

Crux of the Matter In a nutshell: App Tracking Transparency (ATT) necessitates iOS 14.5+ apps to secure user consent through a pop-up before sharing their data. Pre-ATT era enabled app developers and publishers to access copious user-level data. Although ATT opt-in rates are commendable, dual consent requisites and user experience hurdles pose challenges for attribution and campaign measurement. Strategically selecting the prompt display timing hinges on the app’s value proposition and user behavior. Parallel to Apple, Google’s Android is adopting augmented privacy measures, permitting users to opt out of data tracking. Armed with the insight into ATT’s implications and mechanics, users can navigate their privacy preferences with greater clarity and control.

Average Revenue per Daily Active User (ARPDAU)

What is ARPDAU?

ARPDAU, or average revenue per daily active user, serves as a crucial key performance indicator (KPI) that evaluates app monetization for each active user on a daily basis. This metric is valuable for detecting the impact of app changes on its user monetization potential.

Understanding ARPDAU

ARPDAU employs earnings derived from in-app purchases (IAP), subscriptions, and advertisements to compute the daily revenue per active user of the application.

Whether your app undergoes updates, launches new campaigns, or introduces promotional events, ARPDAU provides a high-level overview of the app’s revenue trends in response to these modifications.

Primary Benefits of ARPDAU

ARPDAU functions as a metric enabling app developers and marketers to gain real-time insights into their app’s monetization capabilities, delving into the finest details.

For instance, imagine your app offers special benefits to its paid users. You decide to decrease the price of the paid version to attract more subscriptions. Utilizing ARPDAU, you can assess whether this price reduction effectively increases paid subscriptions and overall revenue.

This metric benefits app developers because:

1. Regular Analysis for Maximizing Monetization Efforts Frequent evaluation guarantees optimal utilization of monetization strategies.

2. Visibility and Balance in Advertising Strategy ARPDAU provides evidence of a balanced and visible advertising strategy.

Limitations of ARPDAU

While many app developers use ARPDAU to compare monetization trends over time, they are aware that ARPDAU is a short-term metric and possesses some limitations. This is primarily due to the fact that lifetime value (LTV) serves as a superior metric for long-term monetization performance evaluation.

So, what challenges or constraints are associated with using this metric?

Consider an app that is a two-player game. Player one is active from 8 am to 11 am daily, spending $1 each day. Player two is active from 10 pm to 1 am and also spends $1 daily. In this scenario, the average revenue per day amounts to $2, resulting in an ARPDAU of $1.

Now, what happens if player two skips a day? Given that they cross the midnight/day boundary, how does this impact their ARPDAU?

This example highlights a significant flaw in the calculation, underscoring why LTV serves as a more accurate metric.

ARPDAU solely focuses on daily active users, limiting data and overall comprehension of the effectiveness of the monetization strategy over an extended period.

Calculating ARPDAU

The formula for calculating ARPDAU is straightforward:

ARPDAU = Total daily revenue / Total active daily users

For instance, if a popular social media app boasts 11 million daily users, generating $5 million in daily revenue, the calculation appears as follows:

ARPDAU = $5,000,000 / 11,000,000 = 45 cents

In this case, the ARPDAU amounts to 45 cents, implying that, on average, each active user spends 45 cents daily on the app.

Strategies to Boost ARPDAU

Enhancing the average daily revenue per user is a feasible endeavor, with actionable strategies to implement that yield immediate effects on user spending.

1. Elevating Ad Engagement with Offerwalls Encouraging users to engage by offering rewards, such as in-game currency or extra lives, through techniques like offerwalls can yield positive outcomes. Displaying enticing ads increases the likelihood of improved revenue.

2. Captivating Users with IAPs, Special Offers, or Exclusive Deals By offering specific user segments IAPs, exclusive deals, or special offers, you can stimulate clicks and purchases, thereby augmenting revenue.

ARPDAU vs. ARPU: Choosing the Right Metric

ARPU, or average revenue per user, calculates the mean revenue generated per user within a specific timeframe. This metric is preferable when targeting new users across various channels.

Essentially, ARPU per channel or platform provides insights into the platforms or channels with the highest potential for generating app income.

Key Insights

Despite its lengthy acronym, ARPDAU is easily comprehensible as a digital marketing metric.

Key points about ARPDAU:

• It employs revenue from IAPs, subscriptions, and ads to determine daily revenue per active user.
• It comes with both advantages and limitations.
• The formula to calculate daily revenue per active user is: revenue (24 hours) / users (24 hours).
• Increasing ARPDAU involves enhancing engagement through strategies like offerwalls and exclusive deals.
• Distinct from ARPU, which considers all users over varying timeframes, ARPDAU centers on active users within a 24-hour period.

ARPPU (Average revenue per paying user)

The revenue a single paying user generates during a specific period. An example of a paid action is subscribing, making in-app purchases, or paying for a download. Using this metric, users who have not paid for anything can be removed from the equation. The calculation formula is total revenue divided by the number of paying users.

What is ARPPU (Average revenue per paying user)?

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.

How is ARPPU calculated?

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.

How is ARPPU used in mobile app marketing?

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.

How do I improve my ARPPU?

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:

1. Adjust your pricing strategy: Another way to increase the amount of money that users are spending on your product is by adjusting your pricing strategy. For example, you could offer different pricing tiers for your product, with each tier offering a different set of features and benefits. This can make your product more appealing to a wider range of users, and may encourage more users to pay for it.
2. Offer premium features or add-ons: One way to increase the amount of money that users are spending on your product is by offering premium features or add-ons that are only available to paying users. For example, if you have a mobile game, you could offer additional levels or characters that can only be accessed by users who pay for the game.
3. Improve the user experience: A great user experience can be a powerful motivator for users to pay for your product. By making your product easy to use, intuitive, and enjoyable, you can increase the chances that users will want to pay for it.
4. Invest in marketing and advertising: Finally, investing in marketing and advertising can help you reach more users and increase the number of paying users. By promoting your product and highlighting its value, you can attract more users who are willing to pay for it.

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.

ARPI (Average revenue per install)

The total revenue your app generates on average across all installs .
The calculation formula is you take the total revenue generated during a period and divide it by the number of installs during the same period.

What is ARPU (Average revenue per user)?

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.

How to Calculate ARPU?

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.

The Importance of ARPU

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.

3 Ways to Improve ARPU

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.

ARPU (Average revenue per user)

The total revenue your app generates on average across all users .
The calculation formula is you take the total revenue generated during a period and divide it by the number of users during the same period.

What is Average revenue per user (ARPU)

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.

Who Uses ARPU?

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.

Why ARPU is Important

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.

How to calculate Uses Average Revenue Per Unit (ARPU):

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.

What’s the difference between ARPU and LTV?

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.

How to Improve your company’s ARPU

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.

ASO (App store optimization)

The process of optimizing mobile apps to rank higher in the app store’s search results to drive more traffic to your app’s page so searchers can perform a specific action such as downloading an app.

What is App Store Optimization (ASO)

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.

How to improve your app store ranking and visibility with ASO?

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.

Attribution

The process by which user interactions are identified and measured. It’s a way in which marketers garner a better understanding of how certain events lead users to a desired outcome, referred to as a conversion. Attribution quantifies an ad’s ability to influence a consumer’s purchasing decisions, providing marketers with a way to compare the effectiveness of various marketing campaigns.

What is mobile attribution?

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.

Investing in Marketing Budget

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.

Building Trust

Providing attribution data as a service relies heavily on building trust with customers and partners. The trust that an attribution provider builds with its clients and partners is the foundation of its business. When this trust is broken, the attribution provider’s products and services can no longer be seen as reliable. Recovering from such a problem is extremely difficult for an attribution provider.

Attribution Modeling

What is Attribution Modeling?

Attribution modeling involves a framework for assessing which touchpoints receive acknowledgment, and to what extent, during the journey towards a conversion.

Defining Attribution Modeling in the Mobile Ecosystem

Attribution modeling within the mobile ecosystem pertains to utilizing various approaches to identify the origins of non-organic installations.

Attribution modeling provides advertisers with a system to attribute and quantify the impact of diverse marketing strategies across different channels. This subsequently informs decisions about budget allocation and overall strategies for mobile marketing.

As attribution modeling revolves around assigning value to specific advertising actions performed by users within a defined time frame, advertisers can more accurately pinpoint which channels are most effective in alignment with the company’s objectives.

In essence, attribution modeling serves as a navigational guide for both advertisers and advertising networks. It aids in gauging the user journey and generating revenue from user interactions with advertisements.

3 Types of Attribution Modeling

Given that users often interact with multiple ads on various channels, there are several attribution modeling types:

1. Last Touch Attribution This is the prevailing standard for attribution modeling. It occurs when an installation or re-engagement is linked to the last interaction, or touchpoint, in the user’s journey within the attribution window. The advertising network responsible for the last touch receives the credit and payment.

   For instance, if the cost per install in the user journey costs $2, the media source that was the last touch—network C—receives full credit.

2. Multi-Touch Attribution Also referred to as fractional attribution, this approach identifies multiple touchpoints throughout a user’s journey that contribute to a conversion, whether it’s an installation, purchase, or another designated in-app action.

   Multi-touch attribution can occur within a single channel (across one device), span multiple channels (across devices like mobile, desktop, or TV), or even encompass offline interactions.

   In multi-touch attribution, networks A and B in the given example would be credited as assisting in the installation, while network C would receive credit for the installation.

   This form of attribution assigns weighted credit to media sources that indirectly contributed to the conversion. Although it’s not widely used currently, it’s considered a potential future alternative due to its detailed analysis and crediting process.

   The idea is that all media sources involved in the user’s journey prior to the last touch will receive a portion of the payment.

3. Other Attribution Models The attribution ecosystem determines the specific attribution modeling method used for measurement and payment. Besides multi-touch attribution, other models like U-shaped/position-based attribution and W-shaped attribution adhere to similar principles.

Significance of Attribution Modeling

Attribution modeling enables advertisers to ascertain how to attribute and quantify the performance and value of their media sources in marketing endeavors.

Without a robust attribution model, advertisers lack a comprehensive understanding of user acquisition and revenue generation. This includes detailed insights into specific media sources, user interactions, ad traffic, user quality (retention and lifetime value), and long-term return on advertising spend (ROAS) and return on investment (ROI), among other metrics.

In relation to multi-touch attribution modeling, the multiple touchpoints leading to installations provide a more comprehensive insight into how and why a user converted. This information significantly influences decisions regarding future budget distribution.

Attribution modeling not only supports advertisers’ marketing efforts but also ensures accurate and equitable crediting and payment for installations on the network side. An impartial third-party attribution provider is vital for establishing a reliable, transaction-based attribution reporting system. This mechanism assigns both credit and accountability to networks as warranted.

In summary, attribution modeling serves as the framework within which attribution for mobile installations occurs. It maintains equilibrium and dynamism within the mobile ecosystem for both advertisers and media sources in the long run.