At a first glance a cohort analysis might seem weird (at best). The visualization doesn’t look like any other visualization method you have learned, and the way to get meaning out of it all, is complex (at first).
Here’s a step-by-step guide to making a cohort analysis, with both the conceptual flow and practical tips:
Define Your Goal
Before touching any data, decide:
- What do you want to measure?
- Retention
- Revenue
- Engagement
- Why are you doing the analysis?
- Identify product stickiness
- Compare effects of marketing campaigns
- Measure lifetime value
Choose Your Cohort Criteria
Thecohorter.com can only track your criterias by looking at date and user_id (email or what you have defined as user_id).
In general or other tools, you need a common attribute to group users/items:
- Time-based cohorts: grouped by first interaction date (e.g., signup month).
- Behavior-based cohorts: grouped by a shared first event (e.g., first purchase, feature adoption).
- Segment-based cohorts: grouped by demographics, acquisition channel, or geography.
💡 Tip: Start with time-based cohorts if you’re new—it’s the most common and easiest to interpret.
Gather and Prepare Your Data
Essential fields in your dataset:
- User ID (or unique identifier)
- Cohort Date (e.g., signup month)
- Event Date (e.g., login, purchase)
- Metric (e.g., activity count, revenue)
Example raw data:
| User ID | Signup Date | Event Date | Revenue |
|---|---|---|---|
| U001 | 2025-01-15 | 2025-01-15 | 50 |
| U001 | 2025-01-15 | 2025-02-10 | 30 |
| U002 | 2025-02-01 | 2025-02-01 | 40 |
Assign Cohort Groups
TheCohorter.com cannot do the next point. If you really want to get the full benefit of a Cohort Analysis, spend the money on a tool (Power BI) or a developer that can setup a tool, that meets your need for datapoints.
However, for each user, determine their cohort group—often the month or week of their first event.
Example:
| User ID | Cohort Month |
|---|---|
| U001 | Jan 2025 |
| U002 | Feb 2025 |
Calculate Time Period Offsets
TheCohorter.com cannot do this right now – appologies.
Determine how far each event is from the cohort start date:
- Month 0 = the month they joined
- Month 1 = the following month
- And so on…
This lets you compare activity across cohorts over time.
Aggregate the Data
For each cohort and each time offset, calculate:
- Count of active users
- Sum of revenue
- % Retention = (active users this period ÷ active users in Month 0) × 100
Visualize the Cohort Table
A typical cohort retention table might look like this:
| Cohort Month | Month 0 | Month 1 | Month 2 | Month 3 |
|---|---|---|---|---|
| Jan 2025 | 100% | 60% | 45% | 30% |
| Feb 2025 | 100% | 50% | 35% | 20% |
💡 Color-coding retention values makes trends easy to spot.
Interpret the Results
- Look for drop-off points (when users stop engaging).
- Compare cohort performance to identify changes in behavior over time.
- Investigate external factors (marketing campaigns, product updates) that might explain patterns.
Get better understaning the data by visiting our Test your self page
Tools You Can Use
- Excel / Google Sheets → Pivot tables + conditional formatting
- Python → Pandas for data wrangling, Matplotlib/Seaborn for visualization
- BI Tools → Tableau, Power BI, Looker for automated dashboards
- Analytics Platforms → Mixpanel, Amplitude, Google Analytics (built-in cohort reports)