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Estd 4.22.2023
In the competitive SaaS landscape, understanding how different groups of customers interact with your product over time isn't just helpful—it's essential for sustainable growth. While overall metrics like monthly recurring revenue (MRR) and total user count provide a broad view of business performance, they often mask critical patterns in user behavior.
This is where cohort analysis becomes invaluable.
What is Cohort Analysis?
Cohort analysis is an analytical technique that groups users based on shared characteristics or experiences within defined time periods, then tracks their behaviors over time. Instead of looking at all users as a single unit, cohort analysis segments them into "cohorts" that can be compared against one another.
A cohort typically represents users who began using your product during the same time period. For example, all users who signed up in January 2023 would form one cohort, while those who joined in February 2023 would form another.
According to Amplitude, a leading product analytics platform, "Cohort analysis reveals whether product changes actually improve user retention, or if your product is getting better or worse over time for new users."
Why is Cohort Analysis Important for SaaS Executives?
1. Identifies Retention Patterns
Customer retention is often the most significant driver of SaaS profitability. A Harvard Business Review study found that increasing customer retention rates by just 5% can increase profits by 25% to 95%.
Cohort analysis helps you visualize how retention rates change over time for different user groups. This visualization makes it immediately clear whether your product is becoming more or less "sticky" for new users.
2. Reveals the Impact of Changes
When you implement product updates, pricing changes, or new onboarding processes, cohort analysis helps isolate their effects. By comparing cohorts before and after changes, you can determine if your innovations are delivering the expected results.
3. Forecasts Customer Lifetime Value
Understanding how long customers typically remain active and how their spending evolves over time helps you accurately predict future revenue. According to McKinsey & Company, companies that use customer analytics comprehensively report outstripping their competitors in terms of profit by 126%.
4. Identifies Problem Areas
Cohort analysis can reveal when and why users typically disengage with your product. If most cohorts show sharp drops in engagement during the third month, for instance, you've identified a critical opportunity to improve the user experience.
5. Optimizes Customer Acquisition
By linking acquisition channels to long-term cohort performance, you can identify which marketing channels bring in not just more customers, but better customers. This insight allows you to allocate your marketing budget more effectively.
How to Measure Cohort Analysis
Step 1: Define Your Cohorts
Start by determining the basis for your cohorts. The most common approach is time-based cohorts (users who signed up in the same period), but you might also consider:
- Acquisition channel cohorts (users who came from paid ads vs. organic search)
- Product version cohorts (users who started with different versions of your product)
- Plan or pricing tier cohorts (enterprise vs. professional vs. basic users)
Step 2: Choose Your Metrics
Select the behaviors or outcomes you want to measure across these cohorts. Common metrics include:
- Retention rate: The percentage of users who remain active after a specific period
- Average revenue per user (ARPU): How user spending evolves over time
- Feature adoption: Which features users engage with and when
- Upgrade/downgrade rates: How users move between pricing tiers
Step 3: Visualize the Data
Cohort analysis is typically displayed in a grid format where:
- Rows represent different cohorts
- Columns represent time periods since the cohort was formed
- Cells contain the value of your chosen metric for that cohort at that time
Many analytics tools like Mixpanel, Amplitude, and Google Analytics offer built-in cohort analysis features with visualization tools.
Step 4: Look for Patterns
When analyzing cohort data, focus on:
- Horizontal patterns: How does a single cohort's behavior change over time?
- Vertical patterns: At the same stage in the lifecycle, how do different cohorts compare?
- Diagonal patterns: Are there seasonal trends affecting behavior across cohorts?
Practical Example: Subscription Retention Cohort Analysis
Let's consider a practical example. Imagine you're analyzing subscription retention rates for a SaaS product on a monthly basis:
| Cohort | Month 1 | Month 2 | Month 3 | Month 4 | Month 5 | Month 6 |
|--------|---------|---------|---------|---------|---------|---------|
| Jan '23 | 100% | 82% | 74% | 65% | 63% | 60% |
| Feb '23 | 100% | 80% | 72% | 64% | 62% | - |
| Mar '23 | 100% | 85% | 78% | 72% | - | - |
| Apr '23 | 100% | 87% | 81% | - | - | - |
| May '23 | 100% | 88% | - | - | - | - |
From this analysis, we can see:
- Retention is improving for newer cohorts (horizontal analysis)
- Month 2 to Month 3 typically sees less drop-off than Month 1 to Month 2 (vertical analysis)
- The March cohort shows markedly better retention than January and February, possibly indicating that product improvements implemented in March had a positive effect
Implementing Advanced Cohort Analysis
As your cohort analysis practice matures, consider these advanced approaches:
Predictive Cohort Analysis
Use machine learning models to predict future behaviors of current cohorts based on patterns observed in historical cohorts. Companies like Spotify and Netflix use this approach to forecast user engagement and churn.
Multivariate Cohort Analysis
Combine multiple factors (e.g., acquisition channel AND initial plan selection) to identify particularly valuable user segments. According to Profitwell, companies using multivariate cohort analysis can identify segments with up to 30% higher lifetime value than those using basic analysis.
Behavioral Cohort Analysis
Rather than grouping users based on when they joined, group them by specific actions they took. For example, compare users who completed your onboarding process within one day versus those who took longer.
Conclusion
Cohort analysis transforms how SaaS executives understand their business by providing a dynamic view of customer behavior over time. While overall metrics might suggest your business is growing steadily, cohort analysis might reveal that newer customers are churning faster—a critical insight that would otherwise remain hidden.
By implementing cohort analysis as a core component of your analytics strategy, you gain the ability to:
- Make data-driven product decisions
- Allocate marketing resources more effectively
- Predict future revenue with greater accuracy
- Identify and address customer experience issues before they impact growth
The companies that thrive in today's competitive SaaS environment aren't just those with the best products or the most aggressive marketing—they're the ones with the deepest understanding of their customers. Cohort analysis is one of the most powerful tools available for developing that understanding.