.png)
Frameworks, core principles and top case studies for SaaS pricing, learnt and refined over 28+ years of SaaS-monetization experience.
Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.
In the competitive SaaS landscape, understanding customer behavior is crucial for sustainable growth. While traditional metrics like Monthly Recurring Revenue (MRR) and Customer Acquisition Cost (CAC) offer valuable snapshots, they often fail to reveal how customer behaviors evolve over time. This is where cohort analysis becomes indispensable. Let's explore what cohort analysis is, why it's particularly important for SaaS businesses, and how to effectively implement it.
What is Cohort Analysis?
Cohort analysis is a data analytics technique that groups customers based on shared characteristics and tracks their behavior over time. Rather than examining all customers as one homogeneous group, cohort analysis divides them into related groups (cohorts) based on when they were acquired or other defining traits.
The most common type of cohort analysis in SaaS is time-based, where customers are grouped by their signup or conversion date (typically by month or quarter). This allows businesses to see how behaviors, retention, and revenue generation patterns differ across customer groups acquired at different times.
Why Cohort Analysis is Crucial for SaaS Executives
1. Reveals True Customer Retention Patterns
According to a study by Bain & Company, a 5% increase in customer retention can increase profits by 25% to 95%. Cohort analysis provides visibility into which customers are staying and which are leaving, along with when these patterns occur in the customer lifecycle.
2. Measures Product and Business Changes Accurately
When you roll out new features, pricing changes, or customer success initiatives, cohort analysis helps isolate the impact of these changes by comparing cohorts before and after implementation.
3. Identifies Your Most Valuable Customer Segments
Not all customers contribute equally to your bottom line. Research from Price Intelligently suggests that the top 20% of SaaS customers often generate more than 80% of revenue. Cohort analysis helps identify which acquisition channels, user personas, or market segments produce the most valuable long-term customers.
4. Improves Financial Forecasting
By understanding how different cohorts behave over time, you can make more accurate predictions about future revenue, churn, and customer lifetime value.
5. Diagnoses Problems Early
Cohort analysis acts as an early warning system, detecting potential issues before they affect your overall business metrics. According to Profitwell data, most SaaS businesses see early warning signs in cohort behavior 4-8 weeks before seeing impacts in their aggregate metrics.
How to Measure Cohort Analysis
Step 1: Define Your Cohorts
Start by determining how you'll group your customers:
- Acquisition cohorts: Customers grouped by when they signed up (most common)
- Behavioral cohorts: Customers grouped by actions they've taken (e.g., used a specific feature)
- Size cohorts: Customers grouped by company size or deal value
- Channel cohorts: Customers grouped by acquisition channel
Step 2: Select Key Metrics to Track
For SaaS businesses, these typically include:
- Retention rate: The percentage of customers still active after a specific period
- Revenue retention: How revenue from each cohort changes over time (accounts for expansions and contractions)
- Average revenue per user (ARPU): How customer spending evolves
- Feature adoption: Which features cohorts use over time
- Expansion revenue: How cohorts grow in value through upsells and cross-sells
Step 3: Set Your Time Intervals
Determine the time periods you'll track (typically months or quarters) and how far back you'll analyze.
Step 4: Create Cohort Analysis Visualizations
The most common visualization is the cohort grid or heat map:
- Each row represents a cohort (e.g., customers who joined in January 2023)
- Each column represents a time period since acquisition (Month 0, Month 1, etc.)
- Each cell contains the value of your chosen metric for that cohort at that point in their lifecycle
Step 5: Look for Patterns and Insights
Analyze your cohort data to identify:
- Retention curves: When do customers typically drop off?
- Improvements over time: Are newer cohorts performing better than older ones?
- Seasonal effects: Do cohorts acquired in certain periods perform differently?
- Long-term value patterns: How do cohorts' spending patterns evolve?
Practical Example: Retention Cohort Analysis
Let's consider a SaaS company that wants to analyze customer retention:
| Cohort | Month 0 | Month 1 | Month 2 | Month 3 | Month 4 | Month 5 |
|-------------|---------|---------|---------|---------|---------|---------|
| January | 100% | 87% | 76% | 72% | 68% | 65% |
| February | 100% | 83% | 75% | 70% | 67% | 63% |
| March | 100% | 85% | 79% | 75% | 72% | - |
| April | 100% | 88% | 81% | 78% | - | - |
| May | 100% | 91% | 84% | - | - | - |
| June | 100% | 92% | - | - | - | - |
From this analysis, the company can observe that:
- Recent cohorts (May, June) have better Month 1 retention than earlier cohorts, suggesting that recent product or onboarding improvements are working
- All cohorts experience their steepest drop in Month 1, indicating that early onboarding and value delivery need attention
- After Month 2, retention stabilizes, with only modest declines thereafter
Advanced Cohort Analysis Techniques
Once you've mastered basic cohort analysis, consider these advanced applications:
Multivariate Cohort Analysis
Segment cohorts by multiple variables simultaneously, such as acquisition channel AND company size, to uncover even more specific insights.
Predictive Cohort Analysis
Use historical cohort data and machine learning to predict how current and future cohorts will perform, enabling proactive decision-making.
Feature Impact Analysis
Compare cohorts that have adopted specific features against those that haven't to measure feature impact on retention and revenue.
Implementing Cohort Analysis in Your SaaS Business
According to OpenView Partners' product benchmarks report, 76% of successful SaaS companies conduct regular cohort analysis. To implement it in your organization:
Ensure proper data collection: Make sure you're tracking customer actions, revenue, and acquisition information with accurate timestamps
Choose the right tools: Use specialized analytics platforms like Amplitude, Mixpanel, or ChartMogul, or build custom reports in tools like Tableau or Power BI
Start small: Begin with basic retention cohorts before expanding to more complex analysis
Establish regular reviews: Make cohort analysis a standard part of your executive and product team reviews
Act on insights: The true value comes from implementing changes based on what you learn
Conclusion
Cohort analysis provides SaaS executives with a powerful lens to understand customer behavior over time. By moving beyond aggregate metrics to examine how specific customer groups evolve throughout their relationship with your product, you gain actionable insights that can drive retention, pricing strategy, product development, and ultimately, sustainable growth.
While implementing cohort analysis requires investment in proper tools and analytical capabilities, the returns are substantial. As the SaaS industry becomes increasingly competitive, the companies that best understand their customers' journeys will be the ones that thrive. Cohort analysis isn't just another metric—it's a fundamental approach to data-driven decision making that reveals the health and trajectory of your business in ways no other analysis can provide.