Cohort Modeling Template and Visualizations - Up to 5 Years of Historicals

I've implemented this SaaS cohort modeling approach with numerous clients. Now, with this introductory template, you too can analyze up to 60 months of historical data. Key outputs feature visuals of total monthly customer spending by cohort in a 'layer cake' format and average customer retention, complete with a best-fit curve to predict customer retention rates.

$75.00 USD

After purchase, the template will be immediately available to download. This is also included in the SaaS financial models bundle.

cohort modeling

Update Video (added a dashboard and more metrics)

My goal was to make this really easy to use. Since we are dealing with historical data, and many systems that companies use have different formatting, I had to create something that was as simple as possible to enter data into, but also robust enough to perform all relevant analysis with. 

You can complete the modeling exercise with three simple columns:

  • CustomerID
  • Purchase / Transaction Date
  • Purchase Amount
  • A fourth column is used, which is a formula to dynamically calculate the cohort month for each customer ID (minifs() function works like a charm).
Template Features:
  • UPDATE: Added a dashboard, customer lifetime value, and churn / spend visuals.
  • Analyze up to 60 months of historical data.
  • Produce average monthly retention curve.
  • Produce layer-cake visualization (cohort spend analysis, which is basically a stacked area chart where each area represents a cohort and you can visually see the revenue generated over time from each and how that compares to the rest)
  • Produce average monthly churn (% and #)
  • Produce average retention of each monthly cohort.
If you wanted to get more advanced and add product categories or other data points for each row, you can do that (just don't overwrite column D). You can then duplicate the cohort summary tabs and adjust the formula to only look at data that meets the product category ID you enter or whatever meta data criteria you are tracking. This can be done for a marketing campaign or something that you want to call out and compare against the total customers. I am available for custom financial modeling work if you need help with this or anything else.

Why is Historical Cohort Modeling Important for Any Recurring Revenue Business?

Cohort analysis is a powerful tool that categorizes customers based on shared attributes or experiences, such as sign-up dates or feature adoption. Through this, businesses can trace patterns in customer behavior, discerning how different groups interact with their product over time. A key application of this is in addressing churn, a prevalent issue in the SaaS sector. By identifying when and why users depart, proactive measures can be instituted to enhance retention.

Such analyses are pivotal when rolling out new features or updates. They reveal the impact on user engagement and can indicate whether any significant behavioral shifts stem from these changes. The insights derived are multifaceted. They inform marketing and sales strategies by spotlighting profitable cohorts, enabling targeted campaigns. Furthermore, these analyses are instrumental in financial forecasting, enabling more precise revenue predictions. For example, if a cohort typically upgrades after a set period, such patterns can be integrated into revenue projections.

Additionally, cohort analysis can unearth potential product or service deficiencies by spotlighting underperforming groups. This data-driven approach also allows for enhanced personalization in marketing and product recommendations. A deeper dive might reveal variances in customer lifecycles, such as differences between enterprise and small business users. Such insights are invaluable for tailored product and marketing strategies. Crucially, while immediate metrics like new sign-ups provide a snapshot, understanding long-term customer engagement offers a comprehensive view of business health. Lastly, regular cohort studies serve as performance benchmarks, facilitating ongoing enhancement and gauging the efficacy of new strategies.

A few SaaS financial models that use similar underlying logic to produce projections include: