.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.
Introduction
In the competitive SaaS landscape, understanding customer behavior patterns is critical for sustainable growth. While traditional metrics like MRR and churn provide snapshots of performance, they often fail to reveal the underlying dynamics of how different customer segments interact with your product over time. This is where cohort analysis enters as a powerful analytical framework that can transform your understanding of your business.
Cohort analysis allows SaaS executives to group users who share common characteristics or experiences within defined time periods and track their behaviors over time. This methodology enables more accurate evaluation of product changes, marketing initiatives, and customer success strategies by isolating variables that might otherwise be obscured in aggregate data.
What is Cohort Analysis?
Cohort analysis is a subset of behavioral analytics that groups customers into "cohorts" based on shared characteristics or experiences within specified time periods. In its most common form, cohorts are organized by acquisition date—for example, all customers who subscribed in January 2023 would form one cohort, while those who subscribed in February 2023 would form another.
These cohorts are then tracked over time along various metrics (retention, revenue, feature adoption, etc.), allowing businesses to compare how different groups behave throughout their customer journey.
Unlike static metrics that provide point-in-time snapshots, cohort analysis is dynamic, revealing patterns and trends across customer lifecycles. This temporal dimension is particularly valuable in subscription-based businesses where long-term customer value is paramount.
Why is Cohort Analysis Important for SaaS Companies?
1. Accurately Measuring Retention and Churn
According to research by Bain & Company, increasing customer retention rates by just 5% can increase profits by 25% to 95%. Cohort analysis offers the most precise method for measuring retention by showing exactly how many customers from each acquisition cohort remain active over time.
This granular view prevents misleading scenarios where strong new customer acquisition might mask poor retention of earlier cohorts in aggregate metrics. By tracking retention curves across different cohorts, executives can identify whether customer retention is improving or deteriorating over time.
2. Evaluating Product Changes and Growth Initiatives
When implementing product changes or new features, cohort analysis allows executives to isolate the impact on specific customer segments. For example, if you launched a new onboarding flow in March, you can compare retention rates of the March cohort against previous months to measure effectiveness.
Mixpanel's industry benchmark report found that products that regularly ship improvements typically see a 15-25% higher retention rate in newer cohorts compared to older ones—cohort analysis makes these improvements measurable.
3. Understanding Customer Lifetime Value (LTV)
Cohort analysis enables more accurate customer lifetime value calculations by tracking how revenue from specific customer groups develops over time. This is particularly valuable for SaaS companies with expansion revenue models.
According to a study by Pacific Crest Securities, the median SaaS business earns back its customer acquisition cost (CAC) in approximately 15 months. Cohort analysis helps executives understand if newer customers are reaching profitability faster or slower than previous cohorts, informing strategic decisions about acquisition spending.
4. Identifying Problematic Periods in the Customer Journey
By examining where cohort curves show consistent drops, executives can pinpoint specific timeframes where customers are most likely to churn. These insights can direct customer success interventions to critical moments in the customer lifecycle.
How to Implement Cohort Analysis
1. Define Meaningful Cohorts
While time-based acquisition cohorts are most common, consider also analyzing cohorts based on:
- Acquisition channel (organic search, paid social, referral)
- Initial plan type or product tier
- User demographics or firmographics
- Onboarding path or initial feature usage
Each perspective provides different insights about your customer base.
2. Select Key Metrics to Track
Common metrics tracked in SaaS cohort analysis include:
- Retention rate: The percentage of users from the original cohort still active in each subsequent period
- Revenue retention: How much of the original cohort's revenue is retained over time (accounting for both churn and expansion revenue)
- Feature adoption: The percentage of each cohort using specific features
- Upgrade/downgrade rates: How subscription changes vary across cohorts
- Customer acquisition cost (CAC) payback: How quickly different cohorts recover their acquisition costs
3. Visualization and Analysis
Effective cohort analysis typically employs two visualization formats:
- Cohort tables: Grid displays with time periods on both axes, showing retention or other metrics for each cohort across time
- Retention curves: Line graphs displaying how retention rates change over time for different cohorts
When analyzing cohort data, look for these patterns:
- Flattening retention curves: Identify at what point customer retention stabilizes, indicating your core user base
- Improving retention in newer cohorts: Evidence that product or service improvements are working
- Seasonal effects: Variations in retention based on acquisition timing
- Changes after specific events: How product updates, pricing changes, or market events affected different cohorts
Practical Example: Measuring Cohort-Based Retention
Let's examine a practical example of how retention cohort analysis works:
Imagine you're analyzing monthly cohorts for a B2B SaaS product. You track what percentage of each month's new customers remain active after 1, 2, 3, 6, and 12 months:
Month-1 Retention:
- Jan 2023 Cohort: 85%
- Feb 2023 Cohort: 83%
- Mar 2023 Cohort: 87%
- Apr 2023 Cohort: 89%
- May 2023 Cohort: 92%
Month-3 Retention:
- Jan 2023 Cohort: 72%
- Feb 2023 Cohort: 71%
- Mar 2023 Cohort: 78%
- Apr 2023 Cohort: 81%
This data reveals that newer cohorts are exhibiting stronger retention at both the 1-month and 3-month marks, suggesting that recent product improvements or customer success initiatives are having a positive impact.
Advanced Cohort Analysis Techniques
1. Behavioral Cohorts
Beyond time-based groupings, segment users based on specific actions they've taken. For example, compare retention rates between users who:
- Completed vs. skipped your onboarding process
- Connected vs. didn't connect integration options
- Used vs. didn't use a specific feature within their first week
According to Amplitude's product benchmark report, users who activate key features within the first week show retention rates up to 50% higher than those who don't.
2. Multivariate Cohort Analysis
Combine multiple variables to identify your most valuable customer segments. For example, analyze how retention differs for enterprise customers acquired through partner referrals versus those from direct sales.
3. Predict Future Behavior With Cohort Trends
By analyzing patterns across multiple cohorts, predictive models can forecast how newer cohorts will likely perform in future periods. These forecasts enable proactive resource allocation for customer success and support teams.
Tools for Cohort Analysis
Several analytics platforms offer cohort analysis capabilities:
- Purpose-built analytics tools:
- Amplitude
- Mixpanel
- Heap
- General analytics platforms:
- Google Analytics 4
- Adobe Analytics
- Customer data platforms:
- Segment
- mParticle
- BI and visualization tools:
- Looker
- Tableau
- Power BI
- Custom SQL analysis:
- For companies with data teams, custom SQL queries against your data warehouse often provide the most flexibility
Conclusion
Cohort analysis is not merely a technical exercise—it's a strategic imperative for SaaS executives seeking to build sustainable growth engines. By comparing the behaviors of different customer segments over time, leaders gain invaluable insights that aggregate metrics simply cannot provide.
The most successful SaaS companies have institutionalized cohort analysis as a core component of their decision-making processes. According to OpenView Partners' expansion SaaS benchmark report, companies that regularly perform cohort analysis are 26% more likely to be in the top quartile for growth in their category.
As you implement cohort analysis in your organization, remember that its true value comes not from the analysis itself but from the actions it inspires—whether optimizing onboarding to mirror the paths taken by your most successful customers, adjusting pricing models based on expansion patterns, or focusing retention efforts on the most vulnerable points in the customer journey.
In an increasingly competitive SaaS landscape, cohort analysis provides the nuanced understanding required to make better strategic decisions and ultimately deliver more value to your customers.