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Estd 4.22.2023
Introduction
In the competitive landscape of SaaS, understanding user behavior patterns is essential for sustainable growth. One of the most powerful analytical methods available to modern executives is cohort analysis. While many SaaS leaders track topline metrics like MRR and user counts, those who leverage cohort analysis gain deeper insights that lead to more informed strategic decisions and ultimately better customer retention.
According to OpenView Partners' 2023 SaaS Benchmarks Report, companies that regularly perform cohort analysis are 37% more likely to achieve best-in-class retention rates. This article explores what cohort analysis is, why it's critical for your business, and how to implement it effectively.
What Is Cohort Analysis?
Cohort analysis is a subset of behavioral analytics that groups users into "cohorts" based on shared characteristics or experiences within a defined time period. Rather than looking at all users as a single unit, cohort analysis segments users by when they signed up, what plan they purchased, which acquisition channel they came from, or other defining factors.
The fundamental principle behind cohort analysis is that users who join your platform at different times or through different channels might exhibit distinct behavioral patterns. By tracking these segments separately over time, you can identify trends that would otherwise remain hidden in aggregate data.
Types of Cohorts
There are several ways to segment your user base into cohorts:
Time-based Cohorts
The most common form of cohort analysis groups users by when they joined your service. For example, "January 2023 signups" would be a time-based cohort. This type of analysis helps you understand how retention or engagement evolves over the customer lifecycle.
Behavior-based Cohorts
These cohorts group users based on actions they've taken, such as "users who integrated with Salesforce" or "users who completed onboarding." This reveals how specific behaviors correlate with long-term value.
Acquisition-based Cohorts
These segment users by how they discovered your product: organic search, paid advertising, referrals, etc. This helps optimize marketing spend by showing which channels bring in the most valuable customers.
Demographic Cohorts
For B2B SaaS companies, this might include industry, company size, or user role. These cohorts help you understand how different market segments interact with your product.
Why Is Cohort Analysis Important for SaaS Companies?
1. Reveals True Retention Patterns
Aggregate metrics can hide serious retention problems. For example, your overall active user count might be growing, making it seem like retention is healthy. However, cohort analysis might reveal that newer user groups are churning at increasingly higher rates, masked by the growth in new acquisitions.
According to data from ProfitWell, SaaS companies that improve their retention by just 5% can increase profitability by 25-95%. Cohort analysis is the most effective tool for identifying where and why retention issues occur.
2. Measures Product and Feature Impact
When you release a new feature or product update, cohort analysis allows you to measure its impact on specific user groups. Did users who joined after the release show better retention? Did existing cohorts experience a boost in engagement? These insights help quantify ROI on product investments.
3. Optimizes Customer Acquisition
Not all customers are equally valuable. Mixpanel research shows that for the average SaaS company, the top 10% of customers generate 3-4x more revenue than the average customer. Cohort analysis helps identify which acquisition channels bring in these high-value users, allowing for more targeted marketing spend.
4. Forecasts Long-term Business Health
By analyzing how previous cohorts have performed over time, you can make more accurate projections about future revenue, churn, and growth. This is particularly valuable for SaaS companies with subscription models, where long-term customer value is paramount.
5. Drives Product Development Priorities
Understanding which features drive engagement for which cohorts helps product teams prioritize development resources. If a specific feature significantly improves retention for enterprise customers but not for small businesses, that insight can inform your product roadmap.
How to Measure Cohort Analysis
Step 1: Define Your Cohorts
Begin by deciding which cohort type will provide the most valuable insights. For most SaaS companies, starting with time-based (acquisition date) cohorts provides a solid foundation for analysis.
Step 2: Select Key Metrics to Track
While retention is the most common metric tracked in cohort analysis, consider measuring:
- Revenue retention (not just user retention)
- Feature adoption rates
- Upgrade/downgrade patterns
- Expansion revenue
- Support ticket submission rates
- NPS or other satisfaction scores
Step 3: Choose Your Time Intervals
Monthly cohorts are standard for SaaS businesses with monthly subscription models, but the appropriate interval depends on your business. B2B enterprises with longer sales cycles might use quarterly cohorts, while high-frequency products might benefit from weekly cohorts.
Step 4: Build Your Cohort Table or Visualization
A classic cohort table shows:
- Cohort groups in rows (e.g., Jan 2023, Feb 2023, etc.)
- Time periods in columns (Month 0, Month 1, Month 2, etc.)
- Values in cells representing the retention rate or other metric
Modern analytics tools like Amplitude, Mixpanel, or even Google Analytics offer built-in cohort analysis features with visualizations that make patterns easier to spot.
Step 5: Analyze for Insights
Look for patterns like:
- Retention cliffs: Points where many users tend to drop off
- Improvements in newer cohorts: Indicating product or onboarding enhancements
- Seasonal variations: Do cohorts acquired during certain periods perform better?
- Long-term plateau: The level at which retention stabilizes, indicating your core user base
Implementing Cohort Analysis in Your Organization
Using Specialized Tools
Several platforms make cohort analysis more accessible:
Product analytics platforms: Mixpanel, Amplitude, and Heap provide robust cohort analysis capabilities designed specifically for this purpose.
Customer data platforms: Segment, mParticle, and similar tools help collect and organize data in ways that facilitate cohort analysis.
Business intelligence tools: Looker, Tableau, and Power BI can create custom cohort analyses when connected to your customer database.
Purpose-built SaaS metrics tools: ChartMogul, ProfitWell, and Baremetrics offer cohort analysis specifically designed for subscription businesses.
Practical Example: Retention Cohort Analysis
Let's consider a practical example. Suppose you're analyzing user retention for a SaaS product with monthly cohorts:
| Cohort | Month 0 | Month 1 | Month 2 | Month 3 | Month 4 | Month 5 | Month 6 |
|--------|---------|---------|---------|---------|---------|---------|---------|
| Jan '23 | 100% | 65% | 45% | 40% | 38% | 37% | 37% |
| Feb '23 | 100% | 68% | 50% | 45% | 42% | 41% | - |
| Mar '23 | 100% | 72% | 55% | 48% | 45% | - | - |
| Apr '23 | 100% | 75% | 58% | 52% | - | - | - |
| May '23 | 100% | 78% | 62% | - | - | - | - |
| Jun '23 | 100% | 80% | - | - | - | - | - |
From this table, several insights emerge:
Improving initial retention: Month 1 retention improved from 65% to 80% over six months, suggesting that onboarding or initial value delivery has improved.
Retention cliff: The biggest drop consistently occurs between Month 0 and Month 1, indicating that the first month is crucial for retention efforts.
Stable long-term retention: For the oldest cohort, retention stabilizes around 37% after Month 5, representing your core user base.
Effectiveness of changes: If you implemented a new onboarding process in March, the jump from February's 68% to March's 72% Month 1 retention would quantify that improvement.
Common Challenges and Solutions
Challenge 1: Data Quality and Collection
Incomplete or inconsistent data can undermine cohort analysis. Ensure your analytics implementation properly tracks user attributes and events.
Solution: Implement a customer data platform like Segment or Rudderstack to standardize data collection across touchpoints.