
Frameworks, core principles and top case studies for SaaS pricing, learnt and refined over 28+ years of SaaS-monetization experience.
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Join companies like Zoom, DocuSign, and Twilio using our systematic pricing approach to increase revenue by 12-40% year-over-year.
In the fast-paced world of SaaS, understanding customer behavior isn't just useful—it's essential for survival. While traditional metrics like MRR and CAC provide a snapshot of your business, they don't tell the complete story of how your customers evolve over time. This is where cohort analysis becomes invaluable.
Cohort analysis is a behavioral analytics methodology that groups users into "cohorts" based on shared characteristics, typically their acquisition date, and then tracks their behavior over time. Unlike aggregate metrics that blend all users together, cohort analysis allows you to isolate specific groups and observe how their behaviors change throughout their lifecycle with your product.
For example, rather than looking at overall churn, you can examine how churn rates differ between customers who signed up in January versus those who signed up in February. This time-based segmentation reveals patterns that might otherwise remain hidden in your data.
According to OpenView Partners' 2021 SaaS Benchmarks Report, companies that regularly practice cohort analysis detect critical business shifts on average 4-6 months earlier than those relying solely on aggregate metrics. This early detection capability can be the difference between proactive strategy adjustments and reactive crisis management.
When you implement a new onboarding flow or customer success program, cohort analysis allows you to precisely measure its impact by comparing before-and-after cohorts. This creates a direct line of sight between initiatives and outcomes.
A study by ProfitWell found that 70% of SaaS companies misinterpret their retention trends when viewing only aggregate data. Cohort analysis breaks through this confusion by showing exactly when and why customers typically disengage from your product.
By understanding which features drive long-term engagement for specific cohorts, you can prioritize your product roadmap based on actual usage patterns rather than assumptions or the loudest customer voices.
Retention cohorts track what percentage of users remain active over time. This analysis typically presents as a grid where each row represents a cohort (e.g., users who joined in March 2023), and each column represents a time period (e.g., Month 1, Month 2, etc.).
The resulting "retention curve" often reveals critical insights:
According to Amplitude Analytics, best-in-class SaaS products aim for 80%+ retention after the first month and 40%+ by month twelve.
Revenue cohorts analyze how customer spending evolves over time. This is particularly important for:
Research from ChartMogul indicates that SaaS companies with successful land-and-expand strategies see cohort revenue increase by 20-30% from initial value within the first year, while struggling companies typically see flat or declining cohort values.
These cohorts track how usage patterns evolve, helping you identify:
Start with specific questions you want to answer:
While time-based cohorts (grouping by signup date) are most common, consider other cohort frameworks:
For each cohort analysis, identify the key metrics that matter:
The appropriate time interval depends on your product's usage patterns:
According to Mixpanel's Benchmark Report, weekly cohort analysis strikes the optimal balance between signal and noise for most SaaS products.
Cohort analysis can generate complex data sets. Effective visualization is critical:
The most sophisticated cohort analysis is worthless without action. Establish a process to:
Focusing exclusively on recent cohorts while ignoring older ones can lead to overemphasizing short-term trends. Maintain perspective by regularly reviewing your longest-standing cohorts.
Different customer segments often display radically different cohort behaviors. Enterprise customers typically have different retention patterns than SMB customers. Segment your cohorts when sample sizes permit.
When you see improvements in newer cohorts, resist the urge to immediately credit recent product changes. Control for variables like seasonality, market conditions, and changes in acquisition sources.
Gainsight research found that companies often become overwhelmed by cohort data complexity. Start simple with basic retention cohorts before adding sophistication.
In an increasingly competitive SaaS landscape, the ability to understand and act on cohort-level insights separates market leaders from the pack. Companies that master cohort analysis gain an evidence-based foundation for decision-making that transcends gut feelings and isolated anecdotes.
By implementing rigorous cohort analysis practices, you'll not only improve retention and LTV metrics but also develop a deeper understanding of your customers' journey—allowing you to create experiences that truly resonate with each new cohort that enters your ecosystem.
The most successful SaaS companies don't just measure their current state; they understand how and why their customers evolve over time. In this respect, cohort analysis isn't just a measurement technique—it's a fundamental business philosophy that keeps you firmly connected to your customers' changing needs and behaviors.
Join companies like Zoom, DocuSign, and Twilio using our systematic pricing approach to increase revenue by 12-40% year-over-year.