Setting Up Cohort Analysis for a Mobile App

We often see teams focusing on DAU while missing that retention is falling. Our experience shows: day-1 retention of 40% and day-7 retention of 20% indicate different issues. Cohort analysis is the only way to see real user behavior. It shows how users from the same install week behave 7, 14, 30 days after first launch. Aggregated DAU hides degradation: if new users arrive faster than old ones leave, DAU grows—but retention drops. Cohorts reveal this. The concept of cohort analysis is widely used in mobile analytics. However, in practice, typical errors arise: unstable user identifier, wrong choice of the first valuable action, lack of version breakdown. These problems distort the data, misleading the team. Contact us to set up cohort analysis correctly.

What is needed for cohort analysis

Two mandatory conditions: a stable user_id and an activation event. Without them, cohorts cannot be built correctly. Savings on advertising budget after precise setup can reach 20%.

User ID must be the same after app reinstall. If you generate a new one each time, the user will always be in a new cohort. Solutions:

• iOS: Keychain to store a generated UUID (survives app deletion)
• Android: AccountManager or server-side ID after registration
• After authentication: Analytics.setUserId(serverUserId) — user binds to the account

Activation event—the first action that shows the product's value. Different apps have different ones:

Choosing the activation event affects what the cohort shows. app_open is too broad—includes casual users. premium_purchase is too narrow for retention analysis of the entire audience.

Why stable user_id matters

Without a stable identifier, you can't distinguish a new user from a returning one after reinstall. This inflates new user numbers and deflates retention. We guarantee correct configuration of Keychain and AccountManager based on your platform. According to our data, 90% of clients had unstable user_id before setup, distorting cohorts by 50% or more. Get a free audit of your current analytics—we'll evaluate user_id stability and event correctness.

How to choose the activation event?

The activation event should reflect the first moment the user gets value. For a fitness app, it's workout_completed, not app_open. One of our clients (a fitness app) after setting cohorts by workout_completed discovered that retention among those who completed their first workout within the first 2 days was 30% higher. We changed the onboarding to push the first workout, and retention grew by 15% in a month. Contact us for a consultation to select the activation event for your app.

Implementing cohort analysis

Firebase / BigQuery—setting up cohort analysis

Firebase builds Retention Chart in the Analytics section natively, but with limited flexibility. For deep analysis, we export raw data to BigQuery via Firebase → Integrations → BigQuery. Then SQL:

-- Cohort by install week, retention on day 7
WITH cohorts AS (
    SELECT
        user_pseudo_id,
        DATE_TRUNC(MIN(PARSE_DATE('%Y%m%d', event_date)), WEEK) AS cohort_week,
        MIN(event_timestamp) AS first_open_ts
    FROM `project.analytics_*.events_*`
    WHERE event_name = 'first_open'
    GROUP BY user_pseudo_id
),
activity AS (
    SELECT DISTINCT
        user_pseudo_id,
        DATE_TRUNC(PARSE_DATE('%Y%m%d', event_date), WEEK) AS activity_week
    FROM `project.analytics_*.events_*`
    WHERE event_name = 'session_start'
)
SELECT
    c.cohort_week,
    DATE_DIFF(a.activity_week, c.cohort_week, WEEK) AS week_number,
    COUNT(DISTINCT c.user_pseudo_id) AS cohort_size,
    COUNT(DISTINCT a.user_pseudo_id) AS retained_users,
    ROUND(COUNT(DISTINCT a.user_pseudo_id) / COUNT(DISTINCT c.user_pseudo_id) * 100, 1) AS retention_pct
FROM cohorts c
LEFT JOIN activity a ON c.user_pseudo_id = a.user_pseudo_id
GROUP BY 1, 2
ORDER BY 1, 2

This query produces a weekly retention table.

Amplitude

In Amplitude, cohort analysis is a native tool in the Retention Analysis section. We configure:

1. Starting Event—first_open or activation event
2. Return Event—session_start or app_open
3. Grouping by days/weeks
4. Breakdown by: install source, platform, app version

Amplitude allows side-by-side cohort comparison—easy to see if retention improved after a product update.

Mixpanel

In Mixpanel, the Retention section builds a classic retention matrix. Additionally, Lifecycle shows what percentage of users return after a long absence. For casual games, this is an important metric.

Comparison of tools for cohort analysis

Behavioral cohorts

Besides time-based cohorts (by install date), behavioral cohorts are useful—groups of users who performed a specific action. For example, in Amplitude Behavioral Cohorts you can segment users by actions: "added to cart", "shared content".

# Example logic in BigQuery:
# Cohort: users who completed onboarding
# Question: what is their retention vs users who skipped onboarding?

If retention among those who completed onboarding is 2x higher—that's proof that onboarding needs improvement, not reduction.

Typical problems when setting up cohort analysis

• Unstable user_id—data is distorted.
• Wrong activation event—cohort doesn't reflect value.
• Using only app_open as return—mixes sessions and active users.
• Missing breakdown by version—can't see effect of updates.

Our experience includes setting up cohort analysis for apps with audiences from 10,000 to 5 million users. We guarantee correct retention and LTV display.

What is included in the work

• Audit of user_id stability in the current implementation
• Definition of activation events together with the product manager
• Configuration of cohort analysis in Firebase + BigQuery / Amplitude / Mixpanel
• SQL queries for custom cohort reports
• Setup of behavioral cohorts for key hypotheses
• Documentation and handover to the team

Timelines

Setup of cohort analysis in a ready-made tool (Amplitude/Mixpanel): 1–2 days. BigQuery + custom SQL queries: 2–4 days. Cost is calculated individually.

Get a free audit of your current analytics—we'll evaluate user_id stability and event correctness.

Mobile App Analytics: Firebase, Amplitude, AppsFlyer and Attribution

Our team regularly encounters projects where analytics is already "set up" but yields no real insights. A typical example is a startup with 50k DAU: tracking dozens of events without a single answer to the question "why don't users reach payment?". In two weeks we built a basic funnel and found that 70% of users drop off at the phone number verification screen. After fixing the bug, retention increased by 12%. The takeaway: analytics should start with specific questions, not tracking everything indiscriminately.

Why Event Taxonomy is the Foundation of Mobile App Analytics?

Firebase Analytics, Amplitude, Mixpanel — technically similar. The difference lies in what you put into them. A common mistake: events like screen_view, button_tap_1, button_tap_2 without context. A month later, no one remembers what button_tap_2 means.

Proper taxonomy: object + action + context. product_viewed, checkout_started, payment_completed with parameters product_id, category, price, source. This allows building funnels, cohort analysis, and retention without additional tracking.

We document the naming convention in a tracking plan — a document (Google Sheet or Amplitude Data Catalog) describing every event, its parameters, and triggering conditions. The tracking plan is synced with the analytics team before development begins, not after. This approach ensures that data remains interpretable months later and doesn't become a dump. Experience from 50+ projects confirms: without a tracking plan, analytics maintenance costs increase 2-3 times due to rework.

What Should You Choose for Mobile App Analytics: Firebase, Amplitude, or Mixpanel?

The table below highlights key differences between the three popular platforms. Choice depends on budget, traffic, and tasks.

Firebase Analytics — free, deep integration with Google Ads, BigQuery export for raw data. Limitations: data latency up to 24 hours, limited funnels. For startups with Google Ads traffic, it's the first choice.

Amplitude — product analytics focused on cohorts and user journeys. Journeys (formerly Pathfinder) shows actual paths between events — not assumed funnels but real routes. Session Replay records sessions for UX analysis. The free tier up to 10M events/month is enough for most products at launch.

Mixpanel — close to Amplitude, stronger in real-time segmentation. Insights, Funnels, Retention cover 90% of product analysts' tasks.

How to Solve Multi-Channel Attribution with AppsFlyer?

Knowing where a user came from is a separate task. Firebase Attribution works only within the Google ecosystem. For multi-channel attribution (Facebook Ads, TikTok, Apple Search Ads, programmatic), an MMP (Mobile Measurement Partner) is needed.

AppsFlyer is the market leader. OneLink — universal deep link working on iOS and Android, correctly attributing installs from any channel. Protect360 — built-in fraud protection (fake installs, click injection on Android). Adjust and Branch are competitors with similar features. Branch excels in deep linking; Adjust is popular in gaming.

According to Apple, with iOS 14.5, apps must obtain user permission via ATT before collecting IDFA for tracking. AppsFlyer uses probabilistic matching (IP + user agent + timing) for these users — accuracy is lower but better than nothing. SKAdNetwork and Privacy Preserving Attribution provide aggregated data from Apple with a 24-72 hour delay.

How to Set Up Crash Analytics to Not Miss Bugs?

Firebase Crashlytics is the standard for crash reporting. It automatically groups crashes by stack trace, shows affected users %, and sends velocity alerts when crash rate increases by more than 10% per hour.

Important: symbolication. On iOS, .dSYM files must be automatically uploaded with each build — via Fastlane upload_symbols_to_crashlytics or Xcode Cloud built-in. Without symbols, crashes in Crashlytics appear as memory addresses. This happens more often than expected when switching to a new CI — in one project with 500k users, we found that 40% of crashes remained unsymbolicated due to a missing CI/CD step. After automation, bug response time dropped from 3 hours to 15 minutes.

For React Native and Flutter, @sentry/react-native and sentry_flutter provide additional context: breadcrumbs, network requests before the crash, Redux/Provider state.

Below is a comparison of popular crash analytics tools to choose according to your needs.

Environment Setup

Three environments with separate Firebase projects: dev, staging, production. Mixing analytics from test sessions and production is a common mistake that skews all metrics. On iOS via GoogleService-Info.plist per scheme, on Android via google-services.json in each flavor folder.

Timelines: basic analytics with Firebase + Crashlytics — 3-5 days. Full tracking plan + Amplitude/Mixpanel with funnels and cohorts — 2-3 weeks. Attribution via AppsFlyer with deep linking and fraud protection — 1-2 weeks. Cost is calculated individually based on integration complexity.

What Is Included in Our Work

As part of analytics implementation, we provide:

• Development and approval of a tracking plan with product and marketing teams.
• SDK integration (Firebase, Amplitude, Mixpanel, AppsFlyer) considering your stack (Swift/Kotlin/Flutter/React Native).
• Setup of funnels, cohorts, dashboards, and alerts.
• Automation of symbolication and .dSYM upload via Fastlane.
• Documentation of events and parameters.
• Team training on the analytics platform.
• Two weeks of post-release support and tracking adjustments.

Our experience: 7 years of analytics implementation and over 80 successful projects in mobile development. We guarantee data correctness and transparency at every stage.

Contact us for a consultation on setting up analytics for your app. Request an audit of your current analytics — and we will show you which metrics you are losing.