Cohort-based analysis is a method that groups users by shared characteristics or behaviors over a specific time period, offering insights that traditional analytics often miss.

Key Metrics to Track:

• Retention rates (gross and net)

• Feature adoption (time-to-first-value, usage depth)

• Revenue insights (ARPU, CLV)

• Engagement levels (e.g., DAU/MAU)

Top Use Cases for Cohort-Based Adoption Analysis

1) Finding At-Risk Customers

Cohort drop-off analysis helps identify user groups that are disengaging, allowing teams to step in before it’s too late. By monitoring engagement trends across different cohorts, customer success teams can detect early warning signs and respond with tailored outreach.

For instance, retention curves can reveal which cohorts are experiencing sharp drop-offs, often signaling challenges with onboarding or product fit. With this information, teams can create risk scoring systems to prioritize support for accounts most likely to churn. These insights not only guide proactive customer engagement but also drive improvements in onboarding and feature adoption strategies.

2) Improving Onboarding

Early-stage cohort data sheds light on where users are struggling during onboarding. By comparing metrics like milestone progression and time-to-value, teams can pinpoint areas of friction and refine messaging or workflows. Additionally, analyzing early feature adoption patterns can highlight which core features to emphasize to improve retention in the long run.

3) Boosting Feature Adoption

Cohort tracking can identify groups that are falling behind in adopting key features, enabling teams to craft targeted strategies to increase usage. Segmenting users by factors like signup date, company size, or industry provides deeper insights into feature preferences across different cohorts. This analysis also helps determine the best timing for introducing new features, ensuring smoother rollouts and better adoption rates.

4) Refining Pricing Strategies and Customer Profiles

Analyzing cohorts based on pricing plans or industries uncovers retention trends and opportunities to adjust pricing models.

Tracking Average Revenue Per User (ARPU) across cohorts reveals which customer segments respond best to specific pricing tiers. This information can guide upselling efforts or highlight pricing models that may not align with customer behavior. Industry-specific cohort analysis often uncovers unique usage patterns, offering valuable insights for refining pricing strategies.

Cohort analysis also improves revenue forecasting accuracy, helping teams make smarter decisions about upselling and pricing adjustments. Beyond pricing, these insights provide a clearer picture of revenue trends and product performance.

5) Forecasting Revenue and Evaluating Product Impact

Cohort data is invaluable for predicting revenue growth and assessing product impact. By studying how past cohorts expanded their usage over time, revenue operations teams can create more reliable growth projections.

Customer Lifetime Value (CLV) calculations become much more accurate when based on cohort-specific data rather than generalized averages. This allows teams to allocate acquisition budgets and customer success resources more effectively.

Additionally, cohort analysis aids in measuring product impact by comparing adoption rates and retention improvements across different segments. These insights play a critical role in shaping future product roadmaps and identifying upsell opportunities by tracking patterns in expansion revenue.

Best Practices for Cohort Analysis in SaaS

1) Picking the Right Cohort Criteria

To get the most out of cohort analysis, start by selecting criteria that align with your goals. While signup dates are a popular choice, many SaaS teams take it a step further by incorporating behavioral and demographic data.

Time-based cohorts are great for spotting seasonal patterns or evaluating the effects of product updates. For example, monthly cohorts strike a balance between offering enough data for meaningful insights and being actionable. On the other hand, weekly cohorts can provide finer detail, especially for fast-growing companies.

Feature-based cohorts group users by their interactions with specific product features. This approach helps identify which features encourage long-term retention and which might contribute to churn. Many companies focus on feature adoption during the early stages of the customer lifecycle since this period often predicts future engagement.

Demographic criteria, like company size or industry, can uncover usage trends that guide customer success efforts or product development. However, avoid slicing the data into too many segments. Stick to a few criteria that are directly tied to the customer journey to keep your analysis focused and impactful.

2) Key Metrics and Timeframes to Track

Retention rates are central to cohort analysis. Metrics like gross retention (the percentage of customers who stay) and net retention (which factors in expansion revenue) provide a comprehensive view of cohort health. For consumer-focused products, weekly tracking often works best. For platforms with longer sales cycles, monthly intervals are more practical.

Feature adoption metrics are also critical. These include measures like time-to-first-value and feature usage depth, which compare how effectively different cohorts navigate onboarding and engage with key product features.

Revenue insights, such as Average Revenue Per User (ARPU) and Customer Lifetime Value (CLV), add another layer of understanding. By analyzing revenue on a cohort basis, you can spot opportunities for upselling or fine-tune your pricing strategies in ways that company-wide averages simply can't reveal.

Engagement metrics, like session frequency or activity levels (e.g., Daily Active Users versus Monthly Active Users), help identify which cohorts are deeply connected to your product and which might be at risk of churning.

3) Displaying Cohort Trends

Retention curves are a go-to method for visualizing cohort performance. These line charts plot retention rates on the y-axis and time on the x-axis, with separate lines for each cohort. Adding color-coding based on time periods or segmentation types can make patterns even easier to spot.

Heat maps are another powerful tool. They display retention data for multiple cohorts at once, with rows representing different cohorts and columns showing time periods. Color intensity indicates retention rates, making it simple to see which cohorts perform well and where drop-offs occur.

For a deeper dive, layered visuals can show relationships between metrics. For instance, overlaying feature adoption data on retention curves might reveal which product interactions contribute to better long-term outcomes.

Analyzing Cohorts with Userlens

Userlens allows you to segment users effectively, see adoption trends easily, and pinpoint growth and retention opportunities.

Compared to other product analytics tools, Userlens makes it convenient to see account-level data instead of just individual users' statistics.

Creating and Customizing Cohorts

Effective cohort creation starts with smart segmentation. With Userlens, you can group companies or users into a cohort based on criteria like signup date, feature adoption milestones, or company size. You can then export these lists to send tailored communications through your existing marketing or customer success tools.

For example, you could create a cohort of users who recently signed up but haven’t completed a key setup step. From there, you might send personalized onboarding emails or provide in-app guidance to help them move forward.

Tracking Feature-Level Usage and Trends

Userlens offers detailed feature-level usage tracking, enabling you to compare how various cohorts engage with specific parts of your platform. You'll just have to define which events make up which feature, and Userlens will take it from there.

Seeing Engagement with Activity Dots and Health Status

Userlens offers an intuitive, visual approach to track engagement. Color-coded activity dots show when and how active users have been, making it easy to spot engagement trends at a glance. You can select if you want to see daily, weekly or monthly activity dots (or all of the above).

Additionally, the platform includes a health score system. You can define activity categories, and AI automatically assigns these categories to your chosen accounts. This system helps customer success teams quickly identify at-risk segments or high-potential opportunities, ensuring their efforts are focused where they’re most needed.

Conclusion

Cohort-based analysis takes raw user data and turns it into insights you can actually use. These numbers help companies figure out exactly when users start losing interest, so they can tweak their strategies to keep them engaged.

Cohort tracking also provides a clear lens on how features are performing. If a new feature boosts retention for a specific group of users, teams can use that data to confidently decide where to focus their resources next.

From refining onboarding processes to adjusting pricing based on usage trends, cohort insights make it easier to step in at the right time. The result? Smarter customer success strategies that save time and deliver better outcomes.

FAQs

How does cohort-based analysis enhance the onboarding experience for new SaaS users?

Cohort-based analysis offers SaaS companies a powerful way to improve the onboarding process by examining how specific groups of users engage with their platform over time. By grouping users - like those who signed up during the same week or month - companies can uncover shared behaviors, identify where users tend to lose interest, and adjust onboarding steps to tackle these issues.

This method allows teams to customize onboarding strategies for different user groups. For example, if a particular cohort shows minimal activity after their first login, companies can step in with targeted actions like interactive tutorials or personalized follow-ups to spark renewed interest.

What metrics should I track with cohort analysis to forecast revenue growth?

To predict revenue growth using cohort analysis, focus on essential metrics like Customer Lifetime Value (LTV), Average Revenue Per User (ARPU), and churn rate. These numbers give you a deeper understanding of how different customer groups drive revenue and reveal areas where growth is possible.

For example, LTV uncovers the long-term value customers bring within a specific cohort, while ARPU highlights the average revenue generated per user. Keeping an eye on the churn rate helps pinpoint retention issues that could affect future earnings. By analyzing these metrics together, you gain a comprehensive view of cohort performance and can better estimate future revenue trends.