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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 businesses, understanding customer behavior is crucial for sustainable growth. While traditional metrics like monthly recurring revenue (MRR) and customer acquisition cost (CAC) provide valuable snapshots, they often fail to tell the complete story of how different customer segments perform over time. This is where cohort analysis becomes an invaluable tool in a SaaS executive's analytical arsenal.
Cohort analysis groups customers based on shared characteristics or experiences within specific time periods and tracks their behaviors over time. By examining how different customer segments interact with your product throughout their lifecycle, you can uncover patterns that impact retention, revenue, and overall business health that might otherwise remain hidden in aggregate data.
What is Cohort Analysis?
Cohort analysis is a subset of behavioral analytics that groups users into "cohorts" based on shared characteristics and examines their behaviors over time. In SaaS, cohorts are most commonly formed by acquisition date—for example, all customers who subscribed in January 2023 would form one cohort.
Unlike snapshot metrics that provide cross-sectional views of your entire customer base at a single point in time, cohort analysis offers a longitudinal perspective that reveals how specific customer segments behave as they progress through their customer journey.
Types of Cohorts
Acquisition Cohorts: Groups customers based on when they first subscribed to your service.
Behavioral Cohorts: Segments users based on actions they've taken (or not taken) within your product, such as "users who activated feature X" versus those who didn't.
Segment Cohorts: Divides users based on demographic or firmographic characteristics like industry, company size, or pricing tier.
Why Cohort Analysis Matters for SaaS Companies
1. Reveals True Retention Patterns
Aggregate retention rates can mask significant variations between different customer segments. For instance, your overall retention might appear stable at 85%, but cohort analysis might reveal that customers acquired through a particular channel have a 95% retention rate, while those from another channel retain at only 70%.
According to a study by ProfitWell, SaaS companies that regularly perform cohort analysis and act on those insights see a 17% higher retention rate than companies that don't.
2. Identifies Product-Market Fit Progression
Cohort analysis allows you to determine whether product changes are actually improving customer retention and engagement over time. If newer cohorts consistently outperform older ones in terms of retention, it suggests your product is evolving in the right direction.
3. Evaluates Marketing Channel Effectiveness
By analyzing cohorts based on acquisition channels, you can determine which sources not only bring in the most customers but also the most valuable and loyal ones. This helps optimize marketing spend toward channels that deliver the best long-term ROI.
4. Informs Pricing Strategy
Tracking how different pricing tiers perform in terms of retention and expansion revenue provides insights into pricing effectiveness. Research from OpenView Partners shows that companies using cohort analysis to inform pricing decisions achieve 30% higher revenue growth compared to those using only conventional pricing metrics.
5. Forecasts Revenue More Accurately
Understanding how cohorts behave over time enables more precise revenue projections. Rather than applying a blanket churn rate across all customers, you can forecast based on the historical performance of similar cohorts.
How to Conduct Cohort Analysis
Step 1: Define Your Objective
Begin with a clear question you're trying to answer:
- Is product feature X improving retention?
- Which acquisition channels bring in the most valuable customers?
- How does onboarding impact long-term customer success?
Step 2: Identify and Segment Your Cohorts
Determine the appropriate cohort type based on your objective. Common SaaS cohorts include:
- Subscription start date (monthly or quarterly cohorts)
- Plan type or pricing tier
- Acquisition channel
- User persona or company size
Step 3: Select Metrics to Measure
Choose metrics that align with your business goals:
- Retention Rate: Percentage of users still active after a specific period
- Churn Rate: Percentage of users who cancel within a time frame
- Average Revenue Per User (ARPU): How revenue per user changes over time
- Customer Lifetime Value (LTV): Total value generated by cohorts over time
- Feature Adoption: Usage of specific features across time
- Expansion Revenue: Upsells and cross-sells within cohorts
Step 4: Determine Your Timeframe
While B2C companies often measure in days or weeks, SaaS businesses typically track cohorts over months or quarters. For enterprise SaaS with longer sales cycles, quarterly or even annual cohorts may be most appropriate.
Step 5: Create Your Cohort Table or Visualization
A typical cohort analysis table has:
- Cohorts listed vertically (e.g., by month of acquisition)
- Time periods displayed horizontally (e.g., months 1, 2, 3 after acquisition)
- Cells showing the relevant metric for each cohort at each time period
Step 6: Analyze Patterns and Take Action
Look for:
- Retention curves: How quickly do customers drop off?
- Cohort comparisons: Are newer cohorts performing better than older ones?
- Anomalies: Unusually high or low performance in specific cohorts
- Inflection points: Time periods where significant changes in behavior occur
Key Metrics for SaaS Cohort Analysis
1. Retention Rate by Cohort
This fundamental metric shows the percentage of users who remain active over time. A retention cohort table typically shows:
| Cohort | Month 1 | Month 2 | Month 3 | Month 4 | Month 5 |
|--------|---------|---------|---------|---------|---------|
| Jan '23 | 100% | 92% | 85% | 82% | 78% |
| Feb '23 | 100% | 94% | 89% | 84% | -- |
| Mar '23 | 100% | 95% | 92% | -- | -- |
| Apr '23 | 100% | 96% | -- | -- | -- |
In this example, each row represents customers who subscribed in a particular month, while columns show retention rates for subsequent months. The gradual improvement in retention from January to April cohorts suggests that product or customer success initiatives may be having a positive impact.
2. MRR Retention and Expansion
Beyond user retention, tracking how revenue behaves over time reveals expansion opportunities and revenue churn:
| Cohort | Initial MRR | Month 1 | Month 2 | Month 3 |
|--------|-------------|---------|---------|---------|
| Jan '23 | $10,000 | 98% | 105% | 112% |
| Feb '23 | $12,000 | 97% | 107% | 114% |
| Mar '23 | $15,000 | 99% | 108% | -- |
When percentages exceed 100%, it indicates that remaining customers are generating more revenue than the original cohort through upsells and expansions—a healthy sign of product value.
3. Payback Period by Cohort
How long does it take for different cohorts to recoup their acquisition costs?
| Cohort | CAC | Month 1 | Month 2 | Month 3 | Month 4 | Payback Period |
|--------|------|---------|---------|---------|---------|----------------|
| Jan '23 | $950 | $200 | $400 | $600 | $800 | 4.75 months |
| Feb '23 | $900 | $250 | $500 | $750 | $900 | 3.60 months |
| Mar '23 | $850 | $300 | $600 | $900 | -- | 2.83 months |
This analysis reveals improvements in efficiency over time, with newer cohorts paying back their acquisition costs more quickly.
Advanced Cohort Analysis Techniques
1. Multi-dimensional Cohort Analysis
Combine multiple cohort dimensions to uncover deeper insights:
- Acquisition channel × pricing tier
- Company size × industry
- Feature adoption × retention
2. Predictive Cohort Analysis
Apply machine learning algorithms to predict future cohort behavior based on early indicators, helping identify at-risk accounts before they churn.
3. Behavioral Milestone Analysis
Track the time it takes cohorts to reach key activation points or usage milestones that correlate with long-term success.