How Cohort Analysis Can Transform Your Customer Insights
Despite months of marketing efforts, your sales are still going down and customer engagement is declining. What is going wrong? What if there is a way that you could pinpoint exactly when and why your customers lose interest? Cohort Analysis might just be the tool you need. It can help to identify which group of customers is driving that decline, and what specific moment they disengage. Instead of looking at your customer base as a whole, cohort analysis allows you to track behavior over time for specific groups. It's the key to truly understanding your customer engagement and retention and making informed decisions to fix the problems before they get worse.
Why Cohort Analysis Goes Beyond Segmentation
Customer segmentation is the first step in breaking down your audience into more meaningful groups. However, while segmentation helps classify customers based on shared characteristics, it provides only a static view. Cohort analysis takes segmentation a step further by introducing a time-based perspective. You can think of segmentation as a snapshot in time, while cohort analysis is like watching a time-lapse video. This allows businesses to not only categorize customers but also track how those groups evolve and behave over time. The key difference is that segmentation gives you insights at a single point in time, while cohort analysis helps you understand how customer behavior changes. Cohort Analysis can reveal patterns of engagement or churn over specific periods. It complements segmentation by allowing businesses to track the performance of each customer segment across different time periods.
What is Cohort Analysis?
Team treehouse has provided a very good definition: "Cohorts are groups of customers that have a common characteristic within a specified time period, usually week, or month." Segmentation, described in a previous article, can be used to define cohorts. A common cohort characteristic is the date a person started purchasing or signing up for a service. Cohorts build on segmentation by adding a time-based perspective (e.g., groups of customers who made their first purchase in the same month). Cohorts can be created using segmentation criteria (e.g., by demographics or product type) for even deeper insights.
How to Conduct a Cohort Analysis
As pointed out by Team Treehouse, cohort analysis is a valuable tool for monitoring two key metrics: customer engagement and retention. By combining a relevant cohort attribute, such as the first purchase date, with one of your primary performance metrics, you can effectively track the ongoing engagement of each new cohort. Additionally, cohort analysis offers a straightforward way to assess how long customers remain loyal to your business. Figure 1 illustrates a potential approach to visualizing cohort analysis.
Each row is a cohort. Each column represents a time unit following the cohort's creation. The time unit can be day, week or month or year. In figure 1, the time unit is month. Month 1 is the first month following the acquisition month. In January 2022, 3546 customers made their first purchase with this company. Now they will always be with that cohort. Every time these customers return to make another purchase, the analysis tracks how many of them come back within different time frames. This provides valuable insights into the behavior patterns of that cohort.
You can think of a cohort analysis table like a classroom of students. Each row in the table represents a new class (or cohort) of students who started school at the same time. As time goes on, you track how many of those students are still coming to class (making repeat purchases, in our case).
- Looking vertically: You want to look at the classes from year to year. You notice that fewer students in each class each year. In cohort analysis, looking vertically tells you how well each group of customers is sticking around over time. For example, you look vertically from up to down in figure 1, you see that the repeat purchase rate has been going down for this company from one cohort to another. The first month repeat purchase rate has gone down from 28% in January 2022 to 6% in December 2022. There are lots of insights you could gain from this. For example, how have you targeted these customers and acquired them? Were the people who were acquired in April 2022, July through September 2022 through some kind of acquisition campaigns?
- Looking horizontally: After looking vertically to understand long-term trends, you can shift to the horizontal analysis to assess short-term cohort progression. This is like looking at how each class performs as they move from one grade to the next. Are some classes dropping out more than others after a certain year? In our case, for the January 2022 cohort, after 12 months in January 2023, only 3% of the initial 3546 customers have come back to make another purchase. So we see that the company is not doing a good job of retaining the customers.
- Looking diagonally: This is like seeing the whole classes at once, comparing students across all grades. The diagonal view gives you an overall snapshot of the latest behavior for every class. In our example, diagonal view shows how each group of customers (from different time periods) behaves in the same current month. It gives you a snapshot of recent engagement across all cohorts. For example, the last cell in each cohort represents everybody who made a purchase in the month of January 2023. It seems that there is some issue with all the cohorts. The return purchase rates for all of the cohorts are low. Some further investigation is needed.
Remember that cohort analysis makes it possible to see if engagement or retention is on the rise or falling. It provides an early signal that something could need closer examination. The retention curve data discussed in a previous article follows the structure illustrated in Figure 1.
How to Supercharge Cohort Analysis with Segmentation
Applying Segmentation to Cohorts can help gain deeper insights. For instance, a grocery store can segment their cohort by the products purchased (e.g., tracking pasta buyers vs. bread buyers across months). It may happen that the pasta buyers are the culprit of the decline in retention rate. Further investigation might reveal that it was due to a discontinuation of a certain brand of pasta from the store.
Companies use segmented cohorts to uncover more specific insights. For example, Amazon might segment customers based on Prime membership and track their retention rates over time to see which segments respond best to their marketing campaigns.
The more specific your segments, the better your cohort analysis will reveal meaningful patterns.
Another example is to segment a cohort by acquisition channel, you can track how customers from different campaigns behave over time and adjust marketing strategies accordingly. Figure 2 shows the repeat visit rate of your Jan 2024 Cohort. Which channel has the best customer retention rate? Which channel has the worst?
Conclusion
By pairing segmentation and cohort analysis, you gain powerful insights into customer behavior and retention. The ability to see when and why customers disengage gives you the opportunity to intervene early and optimize your strategies.
You can explore tools like Google Analytics or Mixpanel to begin tracking your customer cohorts today, and watch how these insights transform your business strategies. Stay tuned for more advanced techniques in future articles!
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