
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.
Join companies like Zoom, DocuSign, and Twilio using our systematic pricing approach to increase revenue by 12-40% year-over-year.
In the rapidly evolving SaaS landscape, understanding customer behavior over time isn't just helpful—it's essential for sustainable growth. While many executives focus on topline metrics like total users or revenue, these aggregated figures often mask critical patterns that could determine your company's future. This is where cohort analysis becomes invaluable.
Cohort analysis is a method of analytical research that segments users into related groups (cohorts) based on shared characteristics or experiences within defined time frames. Rather than examining all user data as a single unit, cohort analysis tracks how specific groups behave over time.
For SaaS businesses, the most common approach is to group users based on when they first subscribed to your service. These "acquisition cohorts" allow you to compare how users who joined in January perform compared to those who joined in February, and so on.
According to data from Profitwell, companies that regularly implement cohort analysis in their decision-making processes experience 17% higher retention rates than those who don't.
When you're only looking at overall metrics, you miss critical signals. For example, your total MRR (Monthly Recurring Revenue) might be growing, but cohort analysis might reveal that users who signed up in recent months are churning faster than earlier cohorts. This early warning signal could indicate declining product quality or market fit issues.
Have your recent product changes actually improved retention? Without cohort analysis, it's impossible to tell. By comparing cohorts before and after significant product updates, you can quantifiably measure if your investments are paying off.
According to OpenView Partners' 2022 SaaS Benchmarks Report, companies that incorporate cohort-level metrics into their forecasting models reduce prediction errors by up to 30%. For SaaS executives, this translates to more efficient capital allocation and more accurate projections for board meetings and fundraising.
By tracking acquisition cohorts alongside their respective CAC (Customer Acquisition Cost), you can pinpoint which marketing channels bring in customers with the highest LTV (Lifetime Value). This allows for smarter allocation of your marketing budget.
The first step is defining meaningful cohorts. While time-based acquisition cohorts are most common, you might also segment by:
For each cohort, track metrics that align with your business questions:
Cohort data is inherently complex. Effective visualization is crucial for deriving insights. Common visualization methods include:
Retention Tables/Heatmaps
These show the percentage of users still active in subsequent periods, with color coding to highlight patterns. According to Amplitude's 2023 Product Analytics Report, heatmaps are the most effective visualization method for detecting early churn signals.
Survival Curves
These graph the percentage of users who remain over time, allowing you to compare different cohorts on the same timeline.
Cumulative Revenue Curves
These show how revenue accumulates from each cohort, helping you visualize ROI timelines and break-even points.
Cohort analysis should be performed regularly:
Dropbox famously used cohort analysis to identify that users who completed specific actions within their first week were 70% more likely to become long-term customers. By analyzing cohort behavior, they identified their "aha moment"—when users added at least one file to one folder and shared it with someone.
This insight led to a complete redesign of their onboarding process, guiding new users toward these critical actions. The result was a 10% improvement in retention across subsequent cohorts, ultimately contributing billions in additional LTV.
SaaS businesses often need 6-12 months to see true cohort patterns. Analyzing only the first few weeks may lead to premature conclusions.
Cohorts acquired during different seasons may behave differently. For example, B2B SaaS products often see different retention patterns for January cohorts versus June cohorts due to budget cycles.
Only looking at entire monthly cohorts may obscure important sub-patterns. When possible, segment cohorts further by customer type, pricing tier, or acquisition channel.
The most common mistake is conducting cohort analysis but failing to implement changes based on the insights gained.
Most SaaS businesses implement cohort analysis using:
Cohort analysis is most effective when it becomes a cross-functional discipline. Consider:
In today's competitive SaaS landscape, cohort analysis isn't just another metric—it's a fundamental way of thinking about your business. By understanding how different user groups behave over their lifecycle, you gain insights that aggregate data simply cannot provide.
Companies that master cohort analysis can identify problems earlier, optimize their resources more effectively, and ultimately build more sustainable businesses. As venture capitalist David Skok noted in his influential analysis of SaaS metrics, "The companies that win are those that understand their customers at the cohort level, not just in aggregate."
For SaaS executives looking to drive growth in an increasingly competitive landscape, implementing robust cohort analysis isn't just recommended—it's essential.
Join companies like Zoom, DocuSign, and Twilio using our systematic pricing approach to increase revenue by 12-40% year-over-year.