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Frameworks, core principles and top case studies for SaaS pricing, learnt and refined over 28+ years of SaaS-monetization experience.
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Introduction
In the competitive landscape of SaaS, understanding customer behavior patterns isn't just useful—it's essential for sustainable growth. While many executives track overall metrics like MRR and churn, these aggregated numbers often mask critical underlying trends. This is where cohort analysis enters as a game-changing analytical approach.
Cohort analysis breaks down your user base into related groups (cohorts) that share common characteristics or experiences within a defined time period. By analyzing how these specific segments behave over time, you gain insights that broad metrics simply cannot provide. For SaaS leaders looking to make data-driven decisions, cohort analysis offers a clearer picture of what's actually happening within your customer base and product adoption cycles.
What Exactly is Cohort Analysis?
A cohort is a group of users who share a common characteristic, typically the time period when they started using your product. Cohort analysis tracks how these specific groups behave over time, allowing you to compare different cohorts against each other.
Unlike typical metrics that measure all users in aggregate, cohort analysis isolates variables by grouping users who experienced your product under similar conditions. This analytical approach reveals:
- How product changes affect user behavior
- Whether your customer retention is improving over time
- Which customer segments deliver the highest lifetime value
- How different acquisition channels perform long-term
- Whether your onboarding improvements are working
Types of Cohorts
While time-based cohorts (grouped by signup or purchase date) are the most common, you can create cohorts based on various characteristics:
- Acquisition cohorts: Grouped by when users signed up or became customers
- Behavioral cohorts: Grouped by specific actions users have taken (e.g., users who used feature X)
- Size cohorts: Enterprise customers vs. SMB customers
- Channel cohorts: Users acquired through different marketing channels
Why Cohort Analysis Matters for SaaS Companies
Revealing the True Retention Story
According to research from ProfitWell, improving customer retention by just 5% can increase profits by 25% to 95%. Cohort analysis is the most accurate way to measure and improve retention because it shows whether newer customer groups are actually retaining better than older ones—something aggregate churn rates hide.
Identifying Product/Market Fit Indicators
Y Combinator partner Gustaf Alströmer notes that cohort retention curves that flatten (rather than dropping to zero) are one of the strongest indicators of product/market fit. By analyzing retention cohorts, you can see if users find enduring value in your product.
Measuring the Impact of Product Changes
When you launch new features or redesigns, cohort analysis helps you accurately measure impact. By comparing cohorts who experienced different versions of your product, you isolate the effect of those changes.
Forecasting Revenue More Accurately
According to a study by KeyBanc Capital Markets, SaaS companies that effectively implement cohort analysis in their forecasting can improve prediction accuracy by up to 25%. This enables more precise financial planning and investor communications.
How to Implement Cohort Analysis
Step 1: Define Your Key Questions
Before diving into cohort data, clearly define what you want to learn:
- Is our product becoming stickier over time?
- Which customer segments have the highest LTV?
- Are our retention initiatives working?
- How do different pricing tiers impact long-term retention?
Step 2: Choose Your Cohort Type
Select the appropriate cohort type based on your questions:
- Time-based cohorts for general retention analysis
- Acquisition channel cohorts for marketing efficiency
- Pricing tier cohorts for optimizing monetization
- Feature adoption cohorts for understanding product engagement
Step 3: Select Your Metrics
Common metrics to track in cohort analysis include:
- Retention rate: The percentage of users still active after a specific period
- Revenue retention: How revenue from each cohort changes over time
- Feature adoption: Usage of specific features by cohort
- Expansion revenue: Additional revenue generated from existing customers
- Churn rate: The percentage of customers who cancel within a given period
Step 4: Create Your Cohort Table or Chart
A standard cohort table has:
- Cohorts listed vertically (e.g., Jan 2023 signups, Feb 2023 signups)
- Time periods horizontally (Month 0, Month 1, Month 2)
- Values in cells showing the metric for that cohort at that time period
For example:
| Cohort | Month 0 | Month 1 | Month 2 | Month 3 |
|--------|---------|---------|---------|---------|
| Jan '23 | 100% | 84% | 76% | 72% |
| Feb '23 | 100% | 87% | 79% | 75% |
| Mar '23 | 100% | 89% | 82% | 78% |
Step 5: Analyze Patterns and Take Action
Look for patterns such as:
- Improving retention in newer cohorts: Suggests your product or onboarding is improving
- Better performance in specific acquisition channels: Indicates where to focus marketing spend
- Higher LTV in particular segments: Shows where to concentrate sales efforts
Practical Example: SaaS Retention Cohort Analysis
Consider a B2B SaaS company that implemented product onboarding improvements in March 2023. Their cohort analysis might look like this:
| Signup Cohort | 30-Day Retention | 60-Day Retention | 90-Day Retention |
|---------------|------------------|------------------|------------------|
| Jan 2023 | 65% | 58% | 52% |
| Feb 2023 | 68% | 60% | 54% |
| Mar 2023 | 72% | 67% | 63% |
| Apr 2023 | 75% | 70% | 66% |
The data clearly shows that cohorts acquired after the onboarding improvements (March onward) maintain significantly higher retention rates. This validation allows the company to confidently invest more in similar improvements.
Common Pitfalls to Avoid
Looking at too short a timeframe: SaaS products often need 3-6 months of data to reveal meaningful patterns.
Ignoring seasonality: Compare year-over-year cohorts to account for seasonal variations.
Focusing only on acquisition date: Supplement with behavioral cohorts to gain deeper insights.
Analysis paralysis: Start with simple retention cohorts before adding complexity.
Not acting on findings: The true value comes from implementing changes based on cohort insights.
Tools for Cohort Analysis
Several tools can help you implement cohort analysis:
- Product analytics platforms: Mixpanel, Amplitude, and Pendo offer built-in cohort analysis features
- Customer data platforms: Segment and Hull allow for sophisticated cohort creation
- BI tools: Looker, Tableau, and PowerBI can create custom cohort analyses
- Purpose-built retention tools: ChartMogul and ProfitWell provide SaaS-specific cohort analytics
Conclusion
Cohort analysis transforms how SaaS leaders understand their business by revealing patterns that aggregate metrics obscure. According to OpenView Partners' 2023 SaaS Benchmarks report, companies that regularly perform cohort analysis see 15% higher growth rates on average than those that don't.
Beyond just tracking numbers, cohort analysis answers fundamental questions about your business: Is your product getting better? Are your customers finding more value over time? Which acquisition strategies yield the best long-term customers?
By implementing cohort analysis as a regular practice, you'll move from reactive decision-making to proactive strategy development based on clear patterns in customer behavior. In the rapidly evolving SaaS landscape, this level of insight isn't just advantageous—it's increasingly becoming a requirement for sustainable success.
For SaaS executives looking to improve their analytical capabilities, cohort analysis represents one of the highest-ROI investments you can make in understanding your business's true health and trajectory.