How to Use MMP Cohorts to Improve LTV (2025 Guide)

Most apps track total installs, overall retention, and average revenue per user.

These aggregate metrics miss a critical insight: not all users are equally valuable.

Users acquired from TikTok might have 40% Day 7 retention. Users from Google App Campaigns might have 60%. If you're only looking at blended metrics, you're making budget decisions with incomplete information.

Cohort analysis solves this. By segmenting users based on when and how they were acquired, you can identify which campaigns drive durable, high-value usersand which ones deliver cheap installs that churn immediately.

Here's how to set up and use cohorts in your MMP to make better growth decisions.

What is a Cohort?

A cohort is a group of users who share a common characteristic within a defined time period.

In mobile attribution, the most common cohort types are:

Install date cohorts: Users who installed on the same day or week Source cohorts: Users acquired from the same channel (TikTok, Meta, Apple Search Ads) Campaign cohorts: Users from a specific campaign or ad set Creative cohorts: Users who clicked a particular ad creative Event cohorts: Users who completed a specific action (onboarding, first purchase)

The goal is to create comparable groups so you can measure differences in behavior, retention, and revenue.

Why Cohorts Matter More Than Aggregate Metrics

Aggregate metrics obscure variance.

If your app has 50% overall Day 7 retention, that might include:

70% retention from Apple Search Ads
50% retention from Meta
30% retention from TikTok

Without cohort segmentation, you'd never see that TikTok users churn at twice the rate of ASA users. You'd keep allocating budget equally and wonder why your blended retention is declining.

Cohort analysis surfaces these differences so you can optimize at the campaign level, not just the portfolio level.

Setting Up Cohorts in Your MMP

All major MMPs (Adjust, AppsFlyer, Branch) support cohort analysis, though the interface and naming conventions differ.

Defining Cohort Parameters

Start by selecting the dimension you want to cohort on:

Time-based: Install date, first session, first purchase date Attribution-based: Media source, campaign, ad set, creative Behavior-based: Users who completed onboarding, users who made first purchase, users who reached level 5

Most analysis starts with install date cohorts segmented by media source. This shows which channels deliver users with strong retention and revenue curves.

Time Windows

Define the time window for cohort creation:

Daily cohorts: Useful for analyzing the impact of creative changes or campaign optimizations Weekly cohorts: Standard for most apps, balances granularity with statistical significance Monthly cohorts: Better for low-install-volume apps where daily or weekly cohorts lack sufficient sample size

Weekly cohorts are the default for most apps. They smooth out day-to-day volatility while maintaining enough granularity to spot trends.

Metrics to Track

For each cohort, track:

Retention: Percentage of users who return on D1, D7, D30, D90 Session frequency: Average sessions per user over time Revenue: Cumulative revenue per user at each time interval Conversion rate: Percentage who complete key actions (subscription, purchase, level completion) LTV projection: Estimated lifetime value based on observed behavior

Analyzing Cohort Retention

Retention is the foundation of cohort analysis. If a cohort doesn't retain, nothing else matters.

Key Retention Milestones

Day 1 retention: Measures product-market fit and onboarding quality. Good benchmarks: 30-40% for utilities, 40-50% for social, 25-35% for games.

Day 7 retention: Indicates whether users find ongoing value. Benchmarks: 15-25% for most categories.

Day 30 retention: Shows long-term engagement potential. Benchmarks: 8-15% for non-subscription apps, 20-40% for subscription apps with strong retention mechanics.

If Day 1 retention is low across all cohorts, you have an onboarding problem. If Day 1 is high but Day 7 drops sharply, you have a value delivery problem.

If specific cohorts (e.g., TikTok traffic) show significantly lower retention than others, you're either targeting the wrong audience or your creative is misrepresenting your product.

Retention Curves

Plot retention over time for each cohort on the same chart.

High-quality cohorts show:

Strong Day 1 retention (30%+)
Gradual decline rather than cliff drop
Retention curves that flatten after Day 30 (indicating stable engaged users)

Low-quality cohorts show:

Weak Day 1 retention (<20%)
Steep drop-off between Day 1 and Day 7
Continued decline past Day 30

If two cohorts have similar Day 1 retention but diverge by Day 7, investigate what happens during the first week. Are high-retention cohorts completing onboarding more frequently? Engaging with specific features?

Analyzing Cohort Revenue

Retention matters, but revenue determines profitability.

Cumulative Revenue Per User

Track total revenue generated by each cohort over time:

Day 7 revenue per user
Day 30 revenue per user
Day 90 revenue per user

This shows the revenue curve for each acquisition source.

Some channels deliver users who monetize quickly but plateau. Others show slow initial monetization but higher long-term value.

Revenue Curves and LTV

Plot cumulative revenue per user over time for each cohort.

Steep early curve: Users monetize quickly (good for short payback period apps) Gradual curve: Users monetize slowly but continue over time (subscription or high-LTV models)

Your business model determines which curve is better. Paid acquisition at scale requires fast payback. If your LTV accrues over 180 days but your payback window is 30 days, you'll struggle to scale profitably.

LTV Projection

Most MMPs offer LTV prediction based on early cohort behavior.

These projections use historical cohort data to estimate long-term value. The accuracy improves over time as you accumulate more cohort history.

Use LTV projections to:

Set initial CPI targets for new campaigns
Estimate breakeven timelines for paid acquisition tests
Forecast revenue based on install volume

Treat projections as directional, not precise. Actual LTV often varies 20-40% from early predictions.

Comparing Cohorts to Optimize Spend

Once you have 4-6 weeks of cohort data, you can start making optimization decisions.

Identifying High-Value Sources

Rank media sources by:

LTV:CAC ratio Revenue per user divided by cost per install
Retention rate Day 30 retention percentage
Payback period Days until cumulative revenue equals CAC

Sources with LTV:CAC above 3:1 and payback under 60 days are strong candidates for scaling.

Sources with LTV:CAC below 1.5:1 or payback over 180 days should be deprioritized or paused unless you have strategic reasons to continue (e.g., market share, brand awareness).

Campaign-Level Cohort Analysis

Don't stop at media source. Segment further by campaign and creative.

Within a single channel (e.g., Meta), you might find:

Campaign A: D30 retention 18%, LTV $12, CAC $8 (LTV:CAC 1.5)
Campaign B: D30 retention 24%, LTV $18, CAC $10 (LTV:CAC 1.8)

Both campaigns are on the same platform, but Campaign B delivers 33% higher LTV:CAC. Shift budget from A to B.

Creative-Level Cohort Analysis

The same logic applies to creatives.

Two ads in the same campaign might drive identical CPIs but vastly different user quality:

Creative 1: High CTR, low retention (clickbait)
Creative 2: Lower CTR, high retention (accurate representation)

Cohort analysis surfaces this. Optimize for quality, not just volume.

Advanced Cohort Use Cases

Cohort Comparison Over Time

Compare the same cohort type across different time periods.

Example: Week 1 Meta cohort vs Week 8 Meta cohort.

If retention or LTV declines over time for the same source, your creative is fatiguing or audience targeting is degrading. Refresh creatives or adjust targeting.

Multi-Dimensional Cohorts

Combine multiple dimensions:

Users from TikTok who completed onboarding
iOS users from Meta who made a purchase within 7 days
Users from Google who installed between January 1-7

This creates highly specific segments for deeper analysis.

Use multi-dimensional cohorts to:

Isolate the impact of platform (iOS vs Android) within a single source
Understand how onboarding completion affects LTV across sources
Identify high-intent user segments worth targeting

Cohort-Based Forecasting

Use historical cohort performance to forecast future revenue.

If you know that users acquired in January typically generate $15 in cumulative revenue by Day 90, and you're planning to acquire 10,000 users in March, you can forecast $150,000 in revenue by June.

This supports budgeting, fundraising, and strategic planning.

Common Cohort Analysis Mistakes

Mistake 1: Too-Short Observation Windows

Don't make LTV decisions based on 7 days of data unless you have a very short monetization cycle.

For most apps, you need at least 30 dayspreferably 60-90to see meaningful revenue and retention patterns.

Mistake 2: Ignoring Statistical Significance

A cohort with 50 users showing 40% retention isn't necessarily better than a cohort with 5,000 users showing 35% retention.

Small cohorts have high variance. Wait until cohorts reach at least 500-1,000 users before making definitive optimization decisions.

Mistake 3: Focusing Only on Early Metrics

High Day 1 retention is great, but if users churn by Day 30, it doesn't matter.

Always track cohorts through at least one full retention curve cycle (typically 30-90 days depending on your app category).

Mistake 4: Not Accounting for Seasonality

Users acquired in December (holiday shopping) often behave differently than users acquired in February.

Compare cohorts from similar time periods when making year-over-year or long-term trend analyses.

Cohort Dashboards in Major MMPs

AppsFlyer Cohort Reports

AppsFlyer's cohort reports let you segment by:

Media source, campaign, ad set, creative
Geo, platform, device type
Custom attribution parameters

Reports show retention, revenue, and in-app events over time. You can export cohort data for further analysis in Excel or BI tools.

Adjust Cohort Analysis

Adjust's cohort analysis includes:

Retention curves
Revenue tracking
Event completion rates
Cohort comparison views

Adjust integrates with data warehouses (BigQuery, Redshift) for advanced cohort modeling.

Branch Cohort Tracking

Branch focuses on deep link cohortsusers who entered via specific links, campaigns, or channels.

This is particularly useful for referral programs, email campaigns, and web-to-app flows where link context matters.

Optimizing Based on Cohort Insights

Once you've identified high-value cohorts, take action:

Increase Budget to High-LTV Sources

If TikTok cohorts consistently show LTV:CAC above 3:1, increase spend.

Scale until performance degrades (typically happens as you exhaust high-intent audiences).

Pause or Reduce Low-LTV Sources

If a source consistently delivers LTV:CAC below 1.5:1 after 60 days, pause or significantly reduce spend.

Reallocate that budget to higher-performing channels.

Refine Creative Strategy

If certain creatives drive high-retention cohorts, analyze what makes them effective:

Accurate product representation?
Specific value proposition?
Targeting a particular user persona?

Double down on those elements in future creative iterations.

Adjust Targeting

If platform-level performance is strong but certain campaigns underperform, the issue is often targeting.

Narrow or broaden audiences based on cohort performance data.

FAQs

What is a cohort in mobile analytics?

A cohort is a group of users who share a common characteristic or experience within a defined time period. In mobile analytics, cohorts are typically grouped by install date, acquisition source, or first action to compare behavior patterns and lifetime value across different user segments.

How long should I track cohort performance?

Track cohorts for at least 90 days to understand medium-term retention and revenue patterns. For subscription apps, track 180+ days. For transactional apps with shorter cycles, 30-60 days may be sufficient to identify trends.

Which cohort metrics matter most for optimization?

The three critical metrics are retention rate (D1, D7, D30), cumulative revenue per user, and LTV:CAC ratio. These metrics together tell you which acquisition sources deliver sustainable, profitable growth.

How do I know if a cohort is large enough to be meaningful?

Aim for at least 500-1,000 users per cohort before making major optimization decisions. Smaller cohorts have high variance and can lead to false conclusions based on statistical noise.

Can I create cohorts retroactively?

Yes. MMPs store historical attribution data, so you can create and analyze cohorts from past install dates. This is useful for validating hypotheses or analyzing the impact of past campaigns.

Cohort analysis transforms raw install data into actionable insights about user quality. By segmenting users and tracking their behavior over time, you can identify which acquisition sources deliver sustainable, profitable growthand which ones just deliver vanity metrics.