Excel Cohort Analysis in 10 Minutes

Key insights

• Cohort Analysis: Group users by a shared event (signup or first purchase) and track each group's behavior over time. This reveals retention patterns, churn, and which cohorts drive the most revenue.


• Data Setup: Prepare clean columns: User ID, cohort date, and activity date. Create a dedicated cohort_month lookup sheet and use VLOOKUP or INDEX/MATCH to tag every event with its cohort.


• Cohort Age: Add a cohort_age column that subtracts the cohort month from the activity month to get Month 0, Month 1, etc. Use this to build tables that count active users or revenue by cohort age.


• Retention Table: Convert event values into counts (e.g., signup=1, cancel=0) and pivot or SUMIFS to produce a retention matrix. Show percentages per cohort to compare how engagement changes over time.


• Key Metrics: Calculate retention rate, churn, total revenue per cohort, Customer Lifetime Value (CLV), and acquisition cost metrics. Use these to test profitability and prioritize acquisition channels.


• Dynamic Model & Limitations: Build a reusable Dynamic Model with input assumptions, conditional formatting, and charts for easy updates. Note limitations: cohort choice affects results, Excel can struggle with very large datasets, and cohort analysis may mask individual-level variation.

Lead: What the video covers

Kenji Farré (Kenji Explains) [MVP] published a practical tutorial that walks viewers through building a dynamic Cohort Analysis model in Excel. The video aims to help product and finance teams measure customer retention, revenue per cohort, and whether acquisition is profitable. Importantly, Farré frames the model as a reusable template that teams can adapt once and reuse repeatedly.

First, the video outlines the assumptions and data structure required to run the model, then it demonstrates the calculations and finally the visuals. Along the way, Farré highlights common startup use cases, noting how investors and analysts rely on cohort tables to assess retention trends. The tutorial also includes a downloadable workbook and example files for practice, which complement the step-by-step explanation.

Building the model: assumptions and setup

Farré begins by emphasizing clean, well-organized data as the foundation for any cohort study, so you should prepare user IDs, signup dates, and subsequent activity events. Then he shows how to assign each user to a cohort month, usually based on the first purchase or signup, and how to tag each event with a cohort_month and a computed cohort_age. These initial steps reduce errors and make later aggregation straightforward.

Next, the video walks through formulas and data lookups, such as using VLOOKUP or its alternatives to map cohort assignments across event rows and to calculate cohort age by month. Farré also recommends assigning numeric values to events so that signups, renewals, and cancellations can be aggregated easily. As a result, you get clean input tables that feed into retention and churn matrices without repeated manual work.

Key metrics explained

Once the cohorts are in place, the tutorial explains how to compute core business metrics including total revenue, customer lifetime value (CLV), customer acquisition cost (CAC), and overall LTV ratios. Farré demonstrates how to aggregate revenue by cohort and month, then compute per-customer and cumulative lifetime values, thereby revealing whether cohorts are becoming more or less valuable over time. Consequently, these metrics help teams test whether marketing and product changes improve unit economics.

Moreover, Farré clarifies how retention percentages and cohort revenue can tell different stories, so analysts must interpret both counts and monetary values together. For example, a cohort with modest retention but high average revenue per user may still be more valuable than one with higher retention but lower spend. Therefore, balancing retention rates with revenue-per-user is critical when making strategic decisions about acquisition and pricing.

Visualization and interpretation

After calculating the numbers, the video shifts to visual tools, showing how conditional formatting, heatmaps, and simple charts make cohort tables easier to read and act upon. Farré uses color gradients to highlight retention decay and peak revenue months, which helps readers spot patterns at a glance and communicate results to non-technical stakeholders. These visual cues accelerate insight without requiring advanced BI tools.

However, Farré also recommends caution when interpreting visuals, because formatting can emphasize noise as much as signal if cohorts are small or irregular. He suggests testing different cohort granularities—monthly versus weekly, for example—to find the level that balances clarity and statistical reliability. In practice, visualization choices must match sample sizes and the cadence of the business.

Tradeoffs, challenges and limitations

The video is candid about tradeoffs: a simple Excel cohort is quick and accessible, yet it can struggle with very large datasets and complex attribution scenarios. Excel models are easy to audit and adapt, but they can become slow or error-prone as event rows grow into the millions, which may push teams toward databases or BI platforms. Thus, teams must weigh immediacy and familiarity against scalability and automation.

Farré also points out modeling assumptions that can bias conclusions, such as how you define cohort start, deal with reactivations, and allocate revenue across periods. Moreover, estimating lifetime value requires assumptions about future behavior that can be optimistic or conservative, and small changes to those assumptions materially alter results. Ultimately, transparency about assumptions and sensitivity checks are essential to avoid misleading conclusions.

Conclusion: practical value for teams

Overall, the video by Kenji Farré offers a clear, practical path to build a working cohort analysis in Excel that teams can reuse and adapt. It combines technical steps with interpretive guidance, and it balances hands-on demonstration with honest discussion of limits and tradeoffs, making it useful for startup analysts and product managers alike.

In short, the tutorial delivers a strong starting point for anyone wanting to understand retention and unit economics without immediate investment in more advanced analytics tools. Nevertheless, teams should plan for scale and validation as their data grows, and they should document assumptions so stakeholders can evaluate the robustness of the findings.

Keywords

cohort analysis in excel, how to do cohort analysis in excel, excel cohort analysis template, cohort analysis pivot table excel, customer cohort analysis excel, retention cohort analysis excel, cohort analysis tutorial excel, cohort analysis dashboard excel