Cohort Analysis for Mobile Apps: Guide

Cohort analysis is a powerful tool for mobile app developers to understand user behavior and improve their products.

Here's what you need to know:

• What it is: Grouping users by shared characteristics and tracking their actions over time
• Why it matters: Reveals trends in user retention, engagement, and monetisation
  • Key benefits:
    • Identifies what keeps users coming back
    • Shows how different user groups behave
    • Helps optimise marketing and product development
      

Here's a quick look at cohort analysis in action:

CohortDay 0Day 1Day 3Day 7March 1100%43%17%11%March 2100%40%17%10%March 3100%37%15%9%

This guide covers:

1. What cohorts are in mobile apps
2. How to set up cohort analysis
3. Tools for analysis
4. Using results to improve your app
5. Common mistakes to avoid
6. Advanced techniques

By the end, you'll know how to use cohort analysis to boost retention, acquire better users, and make data-driven decisions for your mobile app.

Related video from YouTube

What Are Cohorts in Mobile Apps

Cohorts in mobile apps are groups of users who share common traits or behaviours. App developers use cohorts to understand how people use their apps.

What Makes a Cohort

Cohorts are based on shared characteristics or actions. Some common factors:

• When users installed the app
• Their first purchase
• How they use features
• Basic info about the users
  

For example, a cohort might be all the people who installed a fitness app in January and did a workout within a day.

User Sign-up Cohorts

These group users by when they joined. It helps track what users do after installing the app.

CohortDay 0Day 1Day 3Day 7March 1100%43%17%11%March 2100%40%17%10%March 3100%37%15%9%

This table shows how many users stuck around after signing up in March. It helps spot which groups of users stay engaged longer.

User Action Cohorts

These group users by what they do in the app. It shows how certain actions affect how people use the app.

A music app might look at users who:

• Share songs
• Make Playlists
• Use the app every day for a week

Comparing these groups helps the app team see which actions keep users coming back.

Date-based Cohorts

These group users by time periods to see how behavior changes. Examples:

• Summer vs. winter users
• Before and after an update
• Holiday users

These cohorts reveal trends in how people use the app. This helps developers make smart choices about updates, marketing, and improving the app.

Getting Ready for Cohort Analysis

Let's set up cohort analysis for your mobile app. Here's what you need to do:

Pick Your Metrics

Choose metrics that show how people use your app over time:

MetricWhat It Tells YouRetention RateWho keeps coming backChurn RateWho drops offSession FrequencyHow often people open the appARPUHow much money each user brings inTime to First PurchaseHow long before users buy something

These help you spot patterns and areas to improve.

Choose Your Time Frame

Pick a time frame that fits your app:

• Daily: For apps used a lot, like social media
• Weekly: For apps used regularly, but not every day
• Monthly: For apps with longer use cycles, like fitness trackers
  

A game might look at daily cohorts to see how fast people level up. A meal planning app might use weekly cohorts to track recipe creation.

Group Your Users

Group users based on what they have in common:

• When they installed
• How they found your app
• What features they use most
• How much they spend

For example, a music app could group users by their favourite genres to see who sticks around longest.

How to Do Cohort Analysis

Let's break down cohort analysis for your mobile app:

Gathering Data

Collect these key user data points:

• When they installed
• What they do in the app
• Who they are
  

Use tools like [Firebase](https://firebase.google.com/) or [Mixpanel](https://mixpanel.com/) to track this stuff automatically.

Splitting Users into Cohorts

Group users with similar traits:

Cohort TypeExampleTimeJanuary 2023 installersBehaviorFirst-week buyersAttributes25-34 year olds

Pick cohorts that match your goals. Studying a new onboarding? Group users from before and after the change.

Making Data Charts

Turn your data into visuals:

1. Use a cohort tool (Google Analytics, [Amplitude](https://amplitude.com/))
2. Or export to a spreadsheet for custom charts
3. Create heatmaps for retention trends
4. Use line graphs to compare cohorts

Pro tip: One key metric per chart. Keep it simple.

Understanding the Results

To make sense of it all:

1. Spot patterns across cohorts
2. Compare cohorts to find differences
3. Find where users drop off
4. Connect cohort performance to app changes or marketing

Example: Users who finish your tutorial stick around 20% more after 30 days? Maybe focus on getting more people through that tutorial.

Tools for Mobile App Cohort Analysis

Let's dive into some popular tools for mobile app cohort analysis and see how they stack up.

Top Cohort Analysis Tools

1. Mixpanel

Mixpanel's got real-time data, advanced segmentation, and funnel analysis. It's easy to use and great for creating dashboards.

2. Amplitude

Amplitude shines with behavioral cohorts and predictive analytics. It's powerful but might take some time to master.

3. [UXCam](https://uxcam.com/)

UXCam focuses on visual data with session replay and retention analytics. Perfect for understanding user behavior.

4. [Heap Analytics](https://www.heap.io/)

Heap automatically captures data and excels in segmentation and journey analysis. It's built for complex mobile apps.

How They Compare

FeatureMixpanelAmplitudeUXCamHeap AnalyticsData CollectionFlexibleRigid schemaAutomaticAutomaticKey StrengthsReal-time dataBehavioral cohortsSession replaySegmentationEase of UseEasyHarderEasyEasyPricingPer userPer eventTieredTieredFree PlanYesYesYes (limited)Yes (limited)

When picking a tool, think about:

• How well it fits with your current setup
• Data accuracy
• Flexibility in defining cohorts
• Quality of reports
• Room for growth

Choose the one that best fits your needs and budget.

Using Cohort Analysis Results

Cohort analysis gives you a goldmine of data about your app users. Here's how to use it:

Keep More Users

Cohort data shows why users leave and how to keep them. Check this out:

> [Calm](https://www.calm.com/), a meditation app, found users who set daily reminders were 3x more likely to stick around. They made setting reminders a key part of onboarding.

A movie ticketing app? Users who saved a "Favourite" theater had 13% less Day 1 churn. So they started prompting new users to pick a favourite theater right away.

Get Better Users

Cohort analysis reveals your most valuable user groups. Use this to sharpen your marketing:

[CodeSpark](https://codespark.com/) split users by how they found the app. They tested new features with each group separately. This helped them understand which features clicked with users from different sources.

[Ticketmaster](https://www.ticketmaster.com/)? They used Mixpanel to divide B2B users into groups: venues, artists, and promoters. Then they sent each group tailored messages. Result? Better marketing ROI.

Improve Your App

Cohort data shows which features keep users coming back:

User Action30-Day Churn RateShared a song31%Did not share a song77.75%

This data screams that sharing songs = way lower churn. What could the app team do?

1. Make the share feature pop
2. Add sharing prompts after listening sessions
3. Offer rewards for sharing

Small changes can be HUGE. A 5% boost in user retention? That can bump up revenue by 25-95%.

"When we study user behavior, we gather data on what people do—and what they don't do—so we can build products that people will value." - Aaron Krivitzky, Mixpanel

That's the power of cohort analysis. Use it wisely, and watch your app soar.

Common Mistakes in Cohort Analysis

Cohort analysis is powerful, but it's easy to mess up. Here are the big no-nos:

Reading Data Wrong

Misreading data? That's a recipe for disaster. Watch out for:

• Ignoring context: Your app's not always to blame for retention drops. Look at the bigger picture.
• Tiny sample sizes: Don't bet the farm on data from a handful of users.

Here's a real-world example:

A fitness app saw user engagement plummet 30% in January 2022. Panic stations? Nope. Turns out, it lined up with a major COVID surge. Context is king.

Missing Key Factors

Skip important stuff, and your analysis goes off the rails. Be thorough:

• User segments: Different users, different behaviours. Group smart.
• Feature updates: New stuff can shake things up.

FactorWhy It MattersSeasonsUsage changes with the weatherMarketingNew user spikes happenApp updatesUsers might act differentlyWorld eventsEconomy, global stuff impacts use

Jumping to Conclusions

Rushing decisions? That'll cost you. Take a breath:

• Check if trends are real: Is that spike a one-off or the new normal?
• Use multiple metrics: One number doesn't tell the whole story.

"Look at context, use big enough samples, and don't just fixate on retention. That's how you make smart calls." - Daniel Savov, Author

Remember: Good cohort analysis takes time and a sharp eye. Don't rush it.

Advanced Cohort Analysis Methods

Let's explore some next-level cohort analysis techniques for mobile apps.

Multi-factor Cohort Analysis

Multi-factor analysis examines cohorts through multiple lenses. Instead of just grouping users by sign-up date, you might consider:

• Acquisition channel
• User demographics
• Initial app usage patterns

This approach paints a fuller picture of user behavior. For example:

FactorCohort ACohort BSign-up dateJanuary 2023January 2023AcquisitionOrganic searchPaid adsAge group18-2435-44First actionCreated profileBrowsed content

By looking at these factors together, you might find that younger users from organic search stick around longer than older users from paid ads.

Forecasting with Cohorts

Cohort data can help predict future trends:

1. Analyse past cohort behavior
2. Spot patterns in retention and engagement
3. Apply those patterns to newer cohorts

Let's say users who sign up in January have 20% higher 3-month retention rates compared to other months. You can use this info to plan your marketing and support efforts.

Mixing with Other Analysis Types

Combine cohort analysis with other methods for deeper insights:

• A/B testing: How do different cohorts react to app changes?
• User journey mapping: How do cohorts move through your app?
• Churn analysis: When and why do specific cohorts drop off?

Measuring Cohort Analysis Success

Is It Working?

Here's how to tell if your cohort analysis is paying off:

1. Retention Rate

How many users stick around? Track this over time for each cohort.

2. Churn Rate

What percentage of users bail? Monitor this closely.

3. Customer Lifetime Value (LTV)

How much money does each user bring in? This is crucial.

4. Engagement

Look at daily active users, how long they use your app, and which features they love.

5. Conversion Rate

Are users taking the actions you want? Keep an eye on this.

Here's a quick look at how these numbers might stack up:

CohortRetention Rate (30 days)Churn Rate (30 days)LTVDAUJan 202345%55%$251,200Feb 202350%50%$281,350Mar 202355%45%$321,500

The Big Picture Benefits

Stick with cohort analysis, and you'll see:

• More users sticking around
• Smarter user acquisition
• Better features that users actually want
• More money in your pocket
• Decisions based on hard data, not guesswork

Take Calm, the meditation app. They used cohort analysis to test daily reminders. Guess what? Users with reminders were 3x more likely to stick around. So they rolled out reminders to everyone.

"We A/B tested it, saw it worked, and boom - it was in our next update", said a Calm product manager.

That's the power of cohort analysis in action.

Conclusion

Cohort analysis isn't just fancy math. It's your secret weapon for understanding users and making smart moves.

Why it's a big deal:

-   See how different user groups behave over time
-   Figure out why people stick around (or don't)
-   Spend your marketing budget wisely
-   Build features people actually want

Real-world wins:

CompanyWhat They DidThe PayoffCalmTested daily remindersUsers stuck around 3x moreSamsung HealthAdded group fitness challengesUsers took 22% more steps

The key? Set clear goals, pick the right metrics, use solid tools, and never stop testing.

Bottom line: If you want your app to grow and keep users happy, cohort analysis isn't optional. It's essential.