4 Models for App User Segmentation

App user value segmentation model, AARRR segmentation model, app user behavioral segmentation model, demographic segmentation model.

Mobile app user segmentation models refer to the method of dividing app users into groups with common characteristics. For instance, the behavioral segmentation model categorizes app users based on their behaviors in the app, such as users who make a subscription/trial/renewal off/churn within 7 days after installing the app. 

The ultimate goal of app user segmentation is to personalize marketing strategies for different groups to improve user conversion and enhance retention, to generate higher lifetime value for each acquisition. Here are four user segmentation models for mobile apps, which should be applied based on the nature and status of your mobile app business:

  • App User Value Segmentation Model [ CLV model & RFM model ]
  • AARRR Segmentation Model
  • Behavioral Segmentation Model
  • Demographic Segmentation Model

App User Value Segmentation Model

App user value segmentation model groups app users based on their value and contribution to the app. There are two main methods to conduct this model of segmentation: one is CLV(Customer Lifetime Value) and the other is RFM analysis (Recency, Frequency, and Monetary Model Analysis)

CLV App User Segmentation

CLV is a short name for customer lifetime value, which is the predicted net profit generated from users during their lifetime of purchasing a product or service. CLV segmentation groups app users based on their present and potential profitability during the business relationship. 

This segmentation model allows companies to predict the most profitable group of app users, gain insight into users’ common traits, and pay more attention to them instead of less profitable customers. 

One of the practices of this model would be judging which user acquisition campaign can bring bigger profit, which can help make smarter marketing decisions. 

For example, when launching acquisition campaigns in 2 different marketing channels, one with a 50% discount and one with an 80% discount. It is possible that the one with a bigger discount would acquire more users. But these users can be less profitable and loyal in the long run when there is no more discount. 

In the end, it could turn out that acquiring users with an 80% discount can bring you more revenue from a long-term perspective. So there is no doubt that the CLV model for mobile app user segmentation empowers better evaluation of the ROI of marketing campaigns and makes data-driven decisions in the future.

RFM Analysis

RFM, a data-and-value-based user segmentable model, stands for recency, frequency, and monetary. These three metrics are all related to user transactions with the app. Recency means the most recent time when a user makes a deal with the app. Frequency refers to how often the user makes a transaction within a specific timeframe. 

And monetary is the overcall money a user spends during a particular time period. RFM can also be an effective indicator of a user’s willingness to engage with marketing messages and offers. 

One example of RFM analysis is to divide users based on high and low REM and target them with different marketing strategies. For instance, users with high scores of RFM should be enjoying a high priority of support and more premium services. 

For users who have both high recency and monetary, but a low frequency, we should try to increase their paying frequency, like offering them discounts from time to time. And for users who don’t pay recently but with high frequency and monetary, it is time to keep in touch and call them back.

In the above RFM analysis, we can see various groups of users and their uneven values for apps. Therefore, to generate much higher rates of response, app marketers should target specific clusters of users with more personalized and more relevant marketing strategies. And most importantly, app companies should tailor service and sales efforts to the needs of specific groups.

AARRR Segmentation Model

The AARRR app user segmentation model plays an important part in the analysis of the App. AARRR is the idea process from a user downloading an app to recommending the app to others. Here is a detailed explanation of AARRR:

Acquisition: User acquisition is the most essential part of app promotion. It is only with users that app business can be done.

Activation: In AARRR segmentation, users’ activity and engagement in the app is the first important step to a potential in-app deal.

Revenue: Revenue is the money made from users making a transaction with the app. Users in this segmentation should have one deal with the app.

Retention rate: Retention rate is the percentage of users who keep using the app or paying for the app service during a specific period of time. It is the most important element that ensures stable app revenue growth.

Referral: Users in this segmentation is the most loyal one. They trust the app and have a good experience with app, so they are happy to refer or recommend the app to their friends, family, colleagues, or acquaintances. 


AARRR user segmentation model is applicable for any type of app, especially for subscription apps with trial offers. The basic step is to define crucial steps users would take in the apps according to the AARRR model. And then users would be segmented based on this model naturally. 

Offering targeted incentives to users in different segments in this model can have an unexpected result in boosting user conversions. One example would be improving activation: setting up a user-friendly onboarding process for users to engage with the core features, then activating users with a trial to keep user retention after installing.

App User Behavioral Segmentation Model

Behavioral user segmentation is to groups users based on what they do on the app. It is a great technique that helps decide the possibilities a user would make a purchase within the app or the user’s particular needs. So app marketers can target and engage them with relevant content and offers in which users are interested. 

One of the examples is to build an automated email when someone upvotes or comments on a post in a forum app. The behavior of upvoting or commenting can be tracked easily and the content upvoted by users can show their interest. 

Based on this behavior, sending relevant content about the topic the user is interested in or the update of the upvoted post to the user can bring users back to the app with a high probability.

Demographic Segmentation Model

The demographic segmentation model divides users into different groups based on who they are. This model involves a lot of metrics like gender, age, location, preferred language, job title, income, marital status, personal beliefs, religion, educational level, etc. 

For example, if you want to scale your app business globally, it would be wise to have a multi-language version of the app designed for users from different countries. 

Demographics is a significant aspect of user segmentation as it gives basic information about users. Combined with the services apps provide, app marketers can know who their true consumers are which would contribute to app growth.

Segment App Users with Appflow.ai

Appflow.ai is a comprehensive tool for analyzing in-app subscription data and performing monetization tests within the app. It diligently keeps track of users' in-app subscriptions and provides in-depth user information via its CRM tool.

One of the best parts of CRM is user segmentation. You can segment app users based on various attributes such as country, timezone, language, acquisition, device, ad channel, and user behavior (such as installing an app or subscribing for the first time), etc. Additionally, custom attributes can also be configured and implemented as criteria for segmenting users.

User segmentation is easy with appflow.ai's built-in segment tool: simply create a new user segment and choose from the user attributes that you want to filter your users. And the users who meet the criteria will be included in this segment. 


Leverage User Segmentation to Increase Monetization 

1. Compare user behavior across segments using cohort analysis

With Appflow.ai's cohort analysis, you can compare user behavior among different user segments. 

An example of utilizing this feature is comparing subscription revenue between different user segments. You can select the desired metrics, specific cohorts or segments, values, and time periods to filter the data. 

By doing so, you can gain insights into your users through the cohort chart, such as which cohort generates the most subscription revenue and which generates the least. This information can help you make informed marketing decisions.


2. Test monetization ideas for each segment

If you want to tailor different push notifications to users in different segmentation, you can directly build the notifications in appflow.ai. And don’t forget to choose the segment that you are targeting. 

After that, you may think of doing paywall A/B testing in the same segmentation to see which paywall is the most effective one. Don’t worry, Appflow.ai can help you with that too.

For example, you run your app in Spain and want to A/B test whether a paywall in Spanish would bring more paying users than a paywall in English. 

You can create two paywalls with appflow.ai, one in English and the other in Spanish, and then select the segment where users are from Spain. And then hit the begin button and go to drink coffee without further operations. Data tracked by appflow.ai would soon tell you the result. 


The app user segmentation model helps understand users’ unique needs so app marketers can efficiently meet their expectations with targeted marketing strategies. App marketers could use one or a combined app user segmentation model to segment their users. 

In this way, an effective marketing campaign can be delivered to the right people, in the right place, and at the right time during the user journey. And don’t forget, the ultimate mission of app user segmentation is to increase revenue and boost app business.

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4 Models for App User Segmentation

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