Before getting into cohort analysis and its benefits, one must note that businesses devote a considerable chunk of their resources to finding new customers. Still, sometimes they lose sight of their existing ones.
Enterprises often take their eyes off the customer churn metric. But it is essential to track the churn rate continuously. How many customers is your business losing over what period of time? This will help you figure out the answer to the crucial question: why the attrition rate?
The answer will then point you toward customer retention. You will be able to determine how to retain your existing customers. But before that, one needs to understand that retention has different meanings for different businesses.
For an e-commerce firm, it’s simply its buyers, but for a website, it could be visitors.
Analytical Techniques
Several analytical techniques exist to understand what will keep your customers coming back, thereby boosting customer retention. One of them is cohort analysis. This form of analysis involves the tracking of the performance of cohorts over time.
What is Cohort Analysis?
Cohort analysis is a subset of behavioral analytics. It is a subset of segmentation, although both are used interchangeably quite often.
Cohort analysis can be used for two primary purposes: to find out the success of a one-time campaign and to benchmark user engagement.
Unlike segmentation, in cohort analysis, you divide a larger group into smaller, related groups based on different attribute types. But calling a cohort and segmenting the same is not right.
A segment is not time- or event-based, but a cohort is a group of people observed over a period of time.
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Here’s an example: Women above 50 years of age form a segment, but 50-year-old women who are chain smokers, smoking about two packs a day, form a cohort.
Also, unlike in segmentation, in cohort analysis, data analysts formulate a hypothesis, then observe the people in the cohort over a period of time to draw a conclusion.
So, to take the example forward, the hypothesis is: do women over 50 who are chain smokers, by smoking two packs a day, get cancer faster compared to women below 50 who smoke the same number of cigarettes?
There’s a standard formula to help calculate customer retention rate (CRR):
CRR = ((E-N)/S) X 100
E: The number of customers at the end of the selected time period.
N: The number of customers acquired during that period.
S: The number of customers at the beginning (or start) of the period.
To arrive at an accurate picture of retained customers, subtract the number of customers acquired during the period from the number remaining at the end of the period.
Divide the result by the number of customers at the start to find the percentage of customers retained from the beginning. This will give you the CRR.
It’s obvious then that the higher your business’s CRR, the higher your customer loyalty. If your CRR is poor, it is also apparent that your business needs to take the necessary corrective steps.
Where does Cohort Analysis Come into All This?
To understand your business's long-term health, cohort analysis helps companies identify seasonality and the customer lifecycle.
Sometimes, cohort and customer segmentation are used interchangeably, but they do not refer to the same thing.
Customers can be segmented into groups based on certain shared commonalities, the most basic being demography.
The RFM model, Recency, Frequency, and Monetary analysis, is a popular segmentation method.
A cohort, on the other hand, is a slightly narrower group of customers having the same characteristic. It’s akin to putting “similar” clients in a bucket.
A typical cohort is mostly a time-sensitive grouping. For example, customers who signed up during a particular festive season and perhaps continue to shop only during the festival.
Another thumb rule to differentiate is: when customer groups are not time-dependent, they can be called segments instead of cohorts.
Other typical cohort types besides time-based ones are behavior-based and segment-based.
An analysis of cohorts means scrutinizing their performance over time.
Take the example of period-specific buyers, i.e., those who purchased during the just-concluded festive season.
You can use cohort retention analysis to understand the value of these users to the cohorts your business acquired in the previous bout of festival shopping. You can also run a customer cohort analysis to compare shopping patterns across cohorts during the X festival with the same period last year.
Prospect new customers with the lowest churn and the highest LTV >>> Read more
Typical Questions that Cohort Analyses Answer:
- What’s your customer lifetime value?
- Do seasonal users during big retail moments, like Christmas, behave differently from those during routine periods?
- Were this year’s Black Friday customers buy more (and so are better) than earlier ones?
Cohort analysis can be around acquisition cohorts or behavioral cohorts. In the first, the cohorts consist of what the consumers acquired. In contrast, in the second case, it is governed by their activity, e.g., all of them clicked on a particular section when they visited your website.
To sum up, your customer data can be better analyzed using cohort analysis, regardless of your business's industry. You can identify products or services that still have the potential to drive faster sales.
You can even use it to identify gaps in your marketing communications and determine the best way to address a specific cohort.
In product marketing, it can be used to measure a product feature's adoption rate and churn rate.
References:
How To Calculate Customer Retention Rate — A Practical Approach
