Cohort Analysis Step-by-Step Guide

TL;DR

Master cohort analysis to track customer retention, LTV trends, and behavior patterns over time—discovering that 20-30% of customers typically come from sources with 2-3x better retention.

What you'll accomplish:

• Create cohorts based on acquisition date, source, product, or behavior
• Track retention rates and revenue for each cohort over time
• Identify which acquisition channels drive sticky vs churning customers
• Spot product or onboarding issues through cohort performance degradation
• Build predictive retention models from early cohort behavior signals

Time required: 30 minutes | Difficulty: Intermediate | Prerequisites: Customer and revenue data spanning 6+ months, connected to Cogny

Quick Start: Connect your revenue system to Cogny → Navigate to Cohort Analysis → Create time-based cohorts by month → Compare retention rates across acquisition channels.

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Related Resources

Essential guides for understanding customer behavior over time:

• LTV Prediction with AI - Use cohort data to validate LTV predictions and identify early signals of high-value customers
• CAC Analysis with AI - Compare acquisition costs across cohorts to find the most cost-effective customer sources
• AI Growth Strategies Playbook - Apply cohort insights to build data-driven growth strategies

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Question

How do I use AI to perform cohort analysis and understand which customer groups retain best?

Answer

Connect your customer and revenue data to Cogny.

The AI automatically groups customers by acquisition date, source, product, or behavior.

It tracks retention, revenue, and engagement for each cohort over time.

You'll see exactly which customer groups become valuable—and which churn fast.

Quick Tip: Start with monthly acquisition cohorts—they're the simplest and most revealing. Group all customers acquired in January 2024, February 2024, etc., and track their retention monthly. If you see retention dropping in recent cohorts compared to older ones, you've found a problem before it becomes critical. This simple analysis often reveals seasonal patterns, product issues, or targeting problems.

Why Cohort Analysis Matters

Most analytics show aggregate metrics.

"We have 10,000 customers and $500K revenue."

But that's misleading.

Reality:

• January customers: 80% still active, $25 average monthly revenue
• February customers: 65% still active, $18 average monthly revenue
• March customers: 45% still active, $12 average monthly revenue

Something changed in March.

Maybe ad targeting shifted. Maybe product quality declined. Maybe onboarding broke.

Without cohort analysis, you'd never know.

You'd keep doing what worked in January—even though it stopped working in March.

What You'll Learn

After this guide:

• Create cohorts based on acquisition date, source, or behavior
• Track retention rates for each cohort over time
• Identify which acquisition channels drive sticky customers
• Spot product or onboarding issues by cohort performance
• Predict long-term retention from early cohort behavior

Typical insight: 20-30% of customers come from sources with 2-3x better retention than others.

Note: Cohort analysis reveals patterns invisible in aggregate metrics. A business might show steady growth in total customers while newer cohorts actually have declining retention—a ticking time bomb masked by past success. This guide teaches you to spot these patterns early and fix retention issues before they compound. Most businesses discover their retention problem 6-12 months too late; cohort analysis lets you see it immediately.

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Step 1: Define Your Cohorts

A cohort is a group of customers who share something in common.

Common cohort types:

Time-based:

• Acquired in same month
• First purchase in same week
• Signed up during holiday promotion

Source-based:

• Acquired via Google Ads
• Came from organic search
• Referred by existing customer

Behavior-based:

• Purchased product category X first
• Spent more than $100 on first order
• Completed onboarding within 24 hours

In Cogny: Go to Analytics → Cohort Analysis Click "Create Cohort" Choose cohort type

Example setup:

Monthly acquisition cohorts:

• All customers acquired Jan 2024
• All customers acquired Feb 2024
• All customers acquired Mar 2024
• (etc.)

Track each cohort's behavior over their first 12 months.

Time: 5 minutes to create your first cohort structure

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Step 2: Choose Retention Metrics

What counts as "retained"?

This depends on your business model.

E-commerce: Made 2nd purchase within 90 days

SaaS: Still has active subscription in month 3

Media/Content: Logged in at least once in last 30 days

Marketplace: Completed transaction in last 60 days

Be specific.

"Customer is active" is too vague.

Better: "Customer who made at least one purchase in the past 30 days OR has active subscription."

Why this matters:

Loose definition: "Logged in once" = 85% retention Strict definition: "Made purchase" = 32% retention

Very different pictures.

In Cogny: Settings → Retention Definition Set your criteria AI applies it consistently across all cohorts

Time: 3 minutes

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Step 3: Connect All Data Sources

Cohort analysis needs complete customer data.

Required:

• Customer acquisition date
• Customer source/channel
• Purchase history
• Subscription status (if applicable)

Recommended:

• Product usage data
• Email engagement
• Support tickets
• Referral behavior

Why all of this?

You want to understand not just IF customers retain, but WHY.

Cohort A retains at 70%. Cohort B retains at 40%.

What's different?

AI discovers:

• Cohort A: 85% opened welcome email, 60% used feature X
• Cohort B: 45% opened welcome email, 20% used feature X

The pattern: Email engagement + feature usage = retention.

In Cogny: Connect platforms via integrations Or upload CSV with customer data

AI automatically creates cohorts and tracks metrics.

Time: 10 minutes to connect all sources

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Step 4: Analyze Retention Curves

Once cohorts are set up, Cogny generates retention curves.

What to look for:

Healthy curve: Month 0: 100% Month 1: 75% Month 2: 65% Month 3: 60% Month 4: 58% Month 5: 57% Month 6: 56%

Flattening after month 3 = good. Churn stabilizes, retained customers stick.

Unhealthy curve: Month 0: 100% Month 1: 60% Month 2: 35% Month 3: 20% Month 4: 12% Month 5: 8% Month 6: 5%

Continuous steep decline = problem. You're bleeding customers every month.

Red flag patterns:

Cliff drop in month 1: Onboarding failure or product mismatch.

Gradual consistent decline: No product stickiness or engagement drivers.

Drop at specific month: Subscription renewal issue or pricing problem.

In Cogny dashboard: View "Cohort Retention Curves" Compare cohorts side-by-side See where curves diverge

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Step 5: Segment Cohorts by Acquisition Source

Not all traffic sources are equal.

Real example:

E-commerce company analyzed 6-month retention by source.

Organic search: Month 1: 68% retained Month 6: 51% retained

Google Ads (brand keywords): Month 1: 72% retained Month 6: 55% retained

Google Ads (generic keywords): Month 1: 45% retained Month 6: 18% retained

Facebook Ads: Month 1: 38% retained Month 6: 12% retained

Email marketing: Month 1: 82% retained Month 6: 67% retained

The insight:

Facebook drove the most conversions (35% of total). But retention was terrible.

Email and organic drove fewer conversions (20% combined). But retention was 3-5x better.

The fix:

Reduce Facebook budget by 60%. Invest in SEO and email list building.

First 2 months: Total conversions down 15%. Month 6: Active customer base up 40%. Revenue up 52%.

Why?

Acquiring 100 low-retention customers generates less revenue than acquiring 40 high-retention customers.

This is exactly why LTV prediction is so powerful—it helps you identify high-retention customers early so you can double down on the right acquisition channels.

In Cogny: Segment cohorts by "Acquisition Source" Compare retention curves See which sources drive sticky customers

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Step 6: Identify Early Retention Signals

Some behaviors in the first 7-30 days predict long-term retention.

Cogny AI finds these patterns:

Example patterns:

Customers who viewed "How It Works" page in first week:

• 3-month retention: 72%

Customers who didn't:

• 3-month retention: 34%

Customers who made 2nd purchase within 21 days:

• 6-month retention: 85%

Customers who waited 21+ days:

• 6-month retention: 41%

The value:

You can predict retention EARLY and intervene.

If customer hasn't returned in 14 days, AI triggers:

• Personalized email
• Discount offer
• Check-in from support

Real impact:

SaaS company discovered: Users who added 3+ team members in first week = 89% retained after 12 months. Users who didn't = 23% retained.

The fix:

• Onboarding email series pushing team invites
• In-app prompts for team setup
• Customer success calls for solo accounts

Result: 3-month retention increased from 52% to 71%.

Combine these early signals with CAC analysis to calculate true payback period and optimize acquisition spend.

In Cogny: View "Retention Signals" report See which early behaviors predict long-term retention Set up automated interventions

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Step 7: Track Revenue by Cohort

Retention rate is one metric.

Revenue per cohort is what really matters.

Example:

Cohort A (Jan 2024):

• Month 1 retention: 70%
• Average revenue per customer: $45

Cohort B (Feb 2024):

• Month 1 retention: 65%
• Average revenue per customer: $62

Cohort B has lower retention but higher revenue.

Why?

AI analysis shows:

• Cohort B customers bought higher-priced products
• Cohort B had higher average order value
• Cohort B purchased more frequently (even though fewer stuck around)

Cohort B is more valuable.

Look at:

• Revenue per cohort per month
• Cumulative revenue by cohort age
• Revenue trajectory (increasing or declining)

Healthy pattern:

Even as retention flattens, revenue per retained customer increases. (Repeat purchases, upsells, cross-sells)

Unhealthy pattern:

Retention drops AND revenue per customer stays flat. (No engagement, no repeat behavior)

In Cogny: View "Cohort Revenue Analysis" See cumulative revenue curves by cohort Identify high-value vs. low-value cohorts

Use automated ROAS reporting alongside cohort revenue to see both immediate returns and long-term value by channel.

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Step 8: Get AI Recommendations to Improve Retention

Data without action is useless.

Cogny AI analyzes cohort performance and generates specific tickets.

Example tickets:

"March 2024 cohort has 35% lower 60-day retention than Feb cohort. Analysis shows 40% fewer email opens. Likely cause: welcome email landed in spam. Recommendation: Review email deliverability and resend welcome series."

"Customers acquired via Facebook Ads have 3-month retention of 28% vs. 65% for Google Ads. Root cause: Facebook traffic skews toward discount-seekers. Recommendation: Reduce Facebook budget by 50%, shift to Google."

"Cohorts from Q4 2024 show 15% higher retention than Q3. Difference: new onboarding flow launched Oct 1. Recommendation: Document onboarding changes and apply learnings to all future customers."

Each ticket includes:

• The retention problem
• The affected cohort
• Root cause analysis
• Specific action to take
• Expected improvement

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Real Example: Subscription Meal Kit Service

Company: Nordic meal delivery startup Challenge: High churn rate, unclear why

Before Cogny:

Tracking overall metrics:

• 5,000 active subscribers
• 15% monthly churn
• $65 average order value

Assumed churn was "just how subscription business works."

After implementing cohort analysis:

AI segmented customers by acquisition cohort and source.

Discovered:

Cohort: Instagram Ad customers (40% discount promo)

• Month 1 retention: 35%
• Month 2 retention: 18%
• Month 3 retention: 9%
• Avg lifetime revenue: $142

Cohort: Google Search "meal kit comparison" customers

• Month 1 retention: 78%
• Month 2 retention: 71%
• Month 3 retention: 68%
• Avg lifetime revenue: $1,240

Cohort: Referrals from existing customers

• Month 1 retention: 85%
• Month 2 retention: 82%
• Month 3 retention: 80%
• Avg lifetime revenue: $1,580

The patterns:

Instagram discount customers:

• Signed up for the deal
• Cancelled before first full-price box
• Never engaged with recipes or content

Google search customers:

• Did research before buying
• Committed to trying the service
• Higher engagement with app

Referral customers:

• Pre-sold by friend
• Already understood value
• Highest engagement and retention

The fix:

1. Reduced Instagram ad budget by 75%
2. Eliminated 40% discount promotions (attracted wrong cohort)
3. Invested in SEO for comparison content
4. Built referral program (rewards for successful referrals)

Results after 6 months:

• New customer acquisition down 20% (by design)
• But 3-month retention up from 42% to 68%
• Active subscriber base grew 35%
• Revenue per customer up 180%
• Total MRR increased 47% with less ad spend

Their quote:

"We were optimizing for signups. Cohort analysis showed us we should optimize for the RIGHT signups. Game changer."

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Common Mistakes to Avoid

1. Not giving cohorts enough time

You need 6-12 months of data to see real patterns. Don't judge after 30 days.

2. Comparing cohorts from different seasons

Holiday cohorts behave differently than summer cohorts. Compare year-over-year, not month-over-month.

3. Ignoring cohort size

Small cohorts (under 100 customers) are too noisy. Focus on large, statistically significant cohorts.

4. Only tracking retention, not revenue

High retention with low revenue is not success. Track both metrics.

5. Not acting on insights

Cohort analysis is pointless if you don't change acquisition strategy based on findings.

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Frequently Asked Questions

How many customers do I need for cohort analysis?

Minimum: 500 customers with 6+ months of history. Ideal: 2,000+ customers with 12+ months.

What's a good retention rate?

Depends on industry:

• SaaS: 85-95% month 1, 70-80% month 6
• E-commerce: 30-50% month 1, 20-30% month 6
• Subscription boxes: 60-75% month 1, 40-50% month 6

How often should I review cohort data?

Monthly for strategic decisions. Quarterly for deep-dive analysis. Cogny alerts you to significant cohort performance changes.

Can I create custom cohorts?

Yes. Segment by any attribute: product purchased, first order value, geographic location, device type, etc.

Should I focus on improving bad cohorts or scaling good cohorts?

Scale good cohorts. Easier to acquire more of what works than fix what's broken.

What if recent cohorts look worse than old cohorts?

Common. Could be seasonal, could be market saturation, could be product/marketing changes. AI helps identify root cause.

How does cohort analysis differ from customer segmentation?

Cohorts track specific groups over time. Segments are static snapshots. Both are useful.

Can cohort analysis predict churn?

Yes. By comparing new cohorts to historical cohorts, AI predicts likely retention trajectory.

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About This Guide

Written by the Cogny team—built by the founders who created AI optimization systems for Netflix, Zalando, and Momondo at Campanja, and scaled growth for Kry, Epidemic Sound, and Yubico through GrowthHackers.se over 11 years.

Cohort analysis is the most underused analytics technique we see. It reveals patterns that aggregate metrics completely hide.

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Next Steps

After mastering cohort analysis, deepen your retention intelligence:

Immediate Actions:

1. Connect cohort insights to LTV prediction to identify which early behaviors predict long-term value
2. Combine with CAC analysis to calculate cohort-specific payback periods and optimize acquisition
3. Set up monthly cohort reviews to catch retention degradation before it impacts revenue

Advanced Segmentation:

• Segment cohorts by acquisition channel to find your highest-retention traffic sources
• Build behavior-based cohorts (feature adoption, engagement level) to understand what drives stickiness
• Create product-based cohorts to identify which offerings have best repeat purchase rates

Strategic Application:

• Use the AI Growth Strategies Playbook to build retention-focused growth loops
• Implement funnel optimization to improve onboarding and reduce early cohort churn
• Track cohort performance in automated ROAS reporting to see blended returns over time

Retention Tactics:

• Identify churn patterns in underperforming cohorts and build win-back campaigns
• Create personalized onboarding flows for different acquisition sources based on cohort data
• Set up early warning alerts when new cohorts show declining retention vs. historical averages

Need help? We can assist with:

• Schedule a cohort analysis review
• Custom cohort definitions for your business model
• Retention optimization strategy sessions

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