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Estd 4.22.2023
In the fast-paced world of SaaS, understanding user behavior patterns is crucial for sustainable growth. While traditional metrics like monthly recurring revenue (MRR) and customer acquisition cost (CAC) provide valuable insights, they often fail to reveal the complete picture of how different customer segments behave over time. This is where cohort analysis comes in—a sophisticated analytical approach that allows SaaS executives to track specific groups of users as they move through their customer journey.
What is Cohort Analysis?
Cohort analysis is a method of breaking down your user data into related groups (cohorts) that share common characteristics or experiences within defined time periods. Rather than looking at all users as one unit, cohort analysis allows you to segment users based on when they signed up, which features they use, their subscription tier, or other relevant factors.
David Skok, venture capitalist at Matrix Partners, describes cohort analysis as "one of the most powerful tools in a SaaS executive's arsenal" because it reveals patterns that are otherwise invisible in aggregate data.
The most common type of cohort is acquisition-based—grouping users by when they first signed up or purchased your product. For example, all users who subscribed in January 2023 would form one cohort, while those who subscribed in February 2023 would form another.
Why is Cohort Analysis Important for SaaS Companies?
1. Reveals True Retention Patterns
According to research by ProfitWell, a 5% increase in customer retention can increase profits by 25% to 95%. Cohort analysis provides the clearest picture of how well you're retaining customers over time, allowing you to see if newer cohorts are being retained better than older ones—a critical indicator of product and business health.
2. Evaluates Product Changes and Feature Adoption
When you implement new features or pricing changes, cohort analysis helps you measure their impact. By comparing the behavior of cohorts before and after changes, you can determine if your product improvements are actually improving key metrics.
3. Identifies Your Most Valuable Customer Segments
Not all customers deliver equal value. Tomasz Tunguz, venture capitalist at Redpoint Ventures, notes that "typically, 20% of customers generate 80% of revenue." Cohort analysis helps you identify which customer segments have the highest lifetime value, informing more targeted marketing and product development efforts.
4. Provides Early Warning Signals
Negative trends in newer cohorts can serve as early warning signals of product-market fit issues. If recent cohorts are churning faster than historical cohorts, this indicates problems that aggregate metrics might not reveal until significant damage is done.
5. Improves Financial Planning and Forecasting
Understanding how cohorts behave over time allows for more accurate financial forecasting. When you know the typical retention and spending patterns of different customer segments, you can make more informed decisions about resource allocation and growth strategies.
How to Measure Cohort Analysis Effectively
Step 1: Define Your Cohorts
Begin by deciding which characteristics will define your cohorts:
- Time-based cohorts: Users grouped by when they signed up (most common)
- Behavior-based cohorts: Users grouped by actions they've taken (e.g., users who used a specific feature)
- Size-based cohorts: Users grouped by company size or subscription tier
- Channel-based cohorts: Users grouped by acquisition channel
Step 2: Select Key Metrics to Track
While you can track numerous metrics through cohort analysis, these are particularly valuable for SaaS companies:
- Retention rate: The percentage of users who remain active over time
- Revenue retention: How revenue from a specific cohort changes over time
- Feature adoption: Which features are being used by which cohorts
- Conversion rate: How cohorts move through your conversion funnel
- Lifetime value (LTV): How much revenue each cohort generates over time
Step 3: Create Your Cohort Analysis Table or Visualization
A typical cohort analysis is displayed as a table with:
- Rows representing different cohorts (e.g., January 2023 sign-ups)
- Columns representing time periods (e.g., Month 1, Month 2, etc.)
- Cells showing the metric value for each cohort at each time period
Most analytics platforms like Amplitude, Mixpanel, or Google Analytics now offer built-in cohort analysis tools. For more customized analysis, tools like Tableau or even Excel can be used.
Step 4: Analyze Patterns and Take Action
Look for these patterns in your cohort analysis:
- Declining retention across all cohorts at a specific time period: This might indicate a product issue that emerges at that usage stage
- Newer cohorts performing better than older ones: This suggests your product or onboarding improvements are working
- Specific cohorts outperforming others: This helps identify your ideal customer profile
Jason Lemkin, founder of SaaStr, advises focusing on "improving the core leading indicator metrics for your newest cohorts," as this approach compounds over time and yields the greatest long-term benefits.
Practical Example: Retention Cohort Analysis for a SaaS Product
Let's examine a simplified retention cohort analysis for a hypothetical SaaS company:
| Cohort | Month 0 | Month 1 | Month 2 | Month 3 | Month 4 | Month 5 | Month 6 |
|-------------|---------|---------|---------|---------|---------|---------|---------|
| Jan 2023 | 100% | 85% | 75% | 68% | 65% | 62% | 60% |
| Feb 2023 | 100% | 87% | 77% | 70% | 67% | 65% | - |
| Mar 2023 | 100% | 88% | 78% | 72% | 69% | - | - |
| Apr 2023 | 100% | 90% | 82% | 76% | - | - | - |
| May 2023 | 100% | 92% | 85% | - | - | - | - |
| Jun 2023 | 100% | 94% | - | - | - | - | - |
In this example, we can observe:
Improving retention: Newer cohorts show better retention rates at each stage (Month 1, Month 2, etc.), suggesting that product improvements or better customer acquisition targeting is working.
Critical drop-off period: All cohorts experience the largest drop in Month 1, indicating that onboarding or initial value delivery may need improvement.
Stabilization pattern: Retention tends to stabilize around Month 4, which helps in predicting long-term retention and customer lifetime value.
Conclusion: Making Cohort Analysis a Strategic Advantage
Cohort analysis provides SaaS executives with a powerful lens to understand user behavior across the customer lifecycle. By moving beyond aggregate metrics to understand how different customer segments behave over time, you can make more informed decisions about product development, marketing strategies, and resource allocation.
According to OpenView Partners' 2023 SaaS Benchmarks report, companies that regularly use cohort analysis in their decision-making processes show 23% higher growth rates than those that don't. This isn't surprising—cohort analysis helps companies identify problems earlier, double down on what's working, and create more personalized experiences for different customer segments.
As you implement cohort analysis in your organization:
- Start simple with time-based cohorts focused on retention
- Gradually introduce more sophisticated cohort definitions as you build analytical maturity
- Ensure insights from cohort analysis are shared across teams and influence key decisions
- Use cohort insights to create more personalized experiences for different customer segments
By making cohort analysis a cornerstone of your analytical strategy, you'll be positioned to make more informed decisions that drive sustainable growth in an increasingly competitive SaaS landscape.