Segmentation in Mobile Analytics — What It Is, Criteria and Methods for Identifying Groups

What Is User Segmentation?

User segmentation is an analytical method that divides an audience into groups (segments) based on shared characteristics for differentiated engagement. Unlike cohort analysis, segments are not tied to time — a user can belong to multiple segments simultaneously and change them as they interact.

The goal of segmentation is to replace mass communications with targeted ones: send different push notifications to beginners and experienced users, show different content in the feed, and offer personalized discounts. According to McKinsey (2024), companies with advanced segmentation increase revenue by 15–20 % through personalized engagement.

Types of Segmentation in Mobile Analytics

Behavioral Segmentation

Behavioral segmentation — dividing users by their in-app actions: launch frequency, purchases made, screens viewed, levels completed. This is the most valuable type of segmentation for product decisions because it directly reflects engagement. For example, the segment “active paying users” (made 3+ purchases in a month) is the foundation for loyalty programs.

Demographic Segmentation

Demographic segmentation — grouping by gender, age, geolocation, language, and interests. Data is collected through the user profile, during registration, or via analytics platform SDKs. The segment “users aged 18–25 from Moscow” allows adapting the tone of voice and visual style for a younger audience, while geo-segments enable local regional promotions.

Technical Segmentation

Technical segmentation — division by operating system, device model, app version, connection type (Wi-Fi / mobile data), and memory size. It is critical for performance testing: if the Crash Rate on Android 14 exceeds 2 %, the segment is isolated for an urgent fix. According to Firebase Blog (2024), technical segments help reduce bug detection time by 40 %.

RFM Analysis in Segmentation

RFM analysis — a customer segmentation method based on three parameters: Recency (time since last purchase), Frequency (purchase frequency), and Monetary (purchase amount). Each parameter is scored on a scale from 1 to 5, where 5 is the highest value. The three-digit combination (R-F-M) determines the customer type.

The 5-5-5 segment (“champions”) — the most valuable users who purchase frequently, recently, and in large amounts. The 1-1-1 segment (“lost”) — users who have not purchased in a long time and spend little. According to Optimove (2024), RFM segmentation increases marketing campaign ROI by 30–50 % through precise targeting.

Implement RFM analysis in your CRM system or analytics platform. Define a strategy for each segment: retain “champions” with loyalty programs, win back “lost” users with promotions, and educate “newcomers” to convert them into repeat purchasers.

How to Apply Segmentation in Practice

Applying segmentation starts with a hypothesis: which group of users should behave differently? For example, “users who made their first purchase may convert to a second one faster with the right incentive.” Based on the hypothesis, define the segment in your analytics platform and set up an experiment.

The second step is integrating segments into product and marketing channels: push notifications, email campaigns, in-app messages, and A/B tests. Platforms like Amplitude and Firebase allow exporting segments to Google Analytics, Adjust, and other ad networks for targeted advertising.

The third step is iterative calibration. Segments are not static: after launching a campaign, check whether the group’s behavior has changed. If a segment does not respond to personalization, redefine the criteria or merge it with another segment to increase statistical significance.

Practical Segment Examples for Different Business Models

Segments differ depending on the app’s business model. For e-commerce, the key segment is “cart abandoners” (users who added items but did not pay). For games — “paying users” broken down by spending level: whales (top 5 % of players generating 50 % of revenue) and minnows (frequent small purchases).

Each segment requires a unique communication scenario. It is important not only to define the segment but also to set up a pipeline: event → segment → action (push, email, in-app) with a delay. According to Braze (2024), automating segments with time delays increases push notification conversion by 40 %. Measure each segment’s effectiveness using ROMI (Return on Marketing Investment) — campaign cost per segment vs. additional revenue from activated users. For accurate ROMI calculation, consider not only direct conversions in the first week but also the impact on LTV and Retention within 30 days after the campaign.

Segmentation Tools and Code Examples

To create segments in Firebase Analytics, use the Kotlin code below. It sends an event with custom parameters, based on which audiences and segments are built in the Firebase console.

val firebaseAnalytics = Firebase.analytics
val bundle = Bundle().apply {
    putString("user_tier", "premium")
    putInt("purchase_count", 12)
    putString("country", "RU")
}
firebaseAnalytics.logEvent("segment_update", bundle)

After sending events, create an audience in the Firebase console using the user_tier = “premium” parameter and use it for targeted push notifications via Cloud Messaging.

Behavioral Segmentation: Deep Dive

Behavioral segmentation deserves special attention because it provides the most accurate picture of user engagement. Unlike demographic attributes, behavior directly reflects the value a user derives from the product. Key behavioral metrics for segmentation: launch frequency (daily / weekly / monthly active), session depth (time and number of screens), completed target actions, and purchase history.

To build behavioral segments, use a combination of actions and time windows. Example: the “sleeping but valuable” segment — users who have not logged in for 14 days but made 2+ purchases in the previous 30 days. This segment has high reactivation potential through remarketing. According to Clevertap (2024), campaigns targeting “sleeping valuable” segments show a 6:1 ROI due to the audience’s already confirmed purchasing power.

Behavioral segments are also divided by lifecycle stage: new (0–7 days), active (7–30 days), mature (30–90 days), and churned (90+ days). Define KPIs and engagement scenarios for each stage. For example, for new users prioritize onboarding and the first target action, for mature users focus on retention and up-sell. Automate user transitions between stages via webhook events in a CRM or CDP system. Use throttling: do not send more than one message per day to avoid triggering churn due to spam. According to Braze (2024), segments with communication frequency limits show 25 % higher Retention by reducing push notification fatigue.

Common Segmentation Mistakes

The first mistake — segments that are too small. A segment of 50 users is statistically insignificant: any A/B test will have a margin of error above 30 %. The minimum segment size for reliable conclusions is 500 users. If a segment is too small, merge it with neighboring ones.

The second mistake — overlapping segments without considering priorities. A user can simultaneously be “new” and “paying.” Define a hierarchy of segments so that the campaign for “new” users does not overwrite the campaign for “paying” ones. According to Amplitude (2024), 35 % of teams lose conversion due to conflicting segmentation rules.

The third mistake — using only demographics without behavioral data. Gender and age without considering in-app actions provide a superficial picture. Combine demographics with behavior: the segment “women aged 25–35 who made 2+ purchases” is more effective than just “women aged 25–35.”