A few months ago, we had written about the many ways in which cohort analysis can help businesses retain customers. In fact, as compared to other forms of data analytics, cohort analysis is one of the easy ways for a business to run an experiment or do A/B testing. Here’s how cohort analysis can boost your marketing efforts.
Take marketing activity specifically. Cohort analysis can become a primary tool to understand the quality and the efficacy of your business’ marketing efforts. Not to forget its ability to forecast the future.
Observing the traits of a group of people (cohort) sharing characteristics over a selected period can give you insights into their behavior, today, and in the future, by identifying common patterns. An example would be consumers who signed up for a digital catalog after the COVID-19 pandemic broke out. By using cohort analysis, a digital marketer can track such a group over time to understand it by identifying some common patterns or behaviors.
Cohort Analysis And The Business Of Ecommerce
Ecommerce is a highly competitive world. Brand recall is of paramount importance here, and so is brand loyalty. For both, cohort analysis can be of big help. Ecommerce marketers can use cohort analysis to understand the direction their brand is going in, so also their customers.
More specifically, here’s how this form of analysis can be helpful for e-commerce marketers:
Conversion rate: To comprehend how fast are your leads converting into customers.
Product/service popularity: Some products or services are bought more often than others. These may also be better at creating long-term customers. Marketing wisdom says that profit is generated by returning consumers than one-time buyers. That’s where cohort analysis can help an e-commerce business differentiate between their “one-time” product or service; does that do not sell more as compared to those that “fly off the shelf”. Once this differentiation is made, an e-retailer can start to push popular products or services harder, and ditch the under-performing ones.
Brand loyalty: It is a known fact that faithful customers spend over 50% more than new customers, on average. Again, marketing rules tell us that it is less expensive to hold on to loyal customers than it does to go out and find new ones.
How do you do that? Bring together a group of customers by their first date of doing business with your brand. After that, find out the revenue this particular cohort fetched for you in about, say a nine-month period. This form of cohort analysis will help you find out the customers from this group who purchased from your e-commerce business once, or multiple times, thus identifying the most profitable ones.
Difference Between Cohort Analysis And Customer Segmentation
We do see the words “cohort analysis” and “customer segmentation” being used interchangeably, but let us tell you they do not mean 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 more focused 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, those customers who signed on during Christmas this year. Another differentiator can be – when customer groups are not time-dependent, they can be called segments instead of cohorts. Other typical forms of cohorts besides time-based ones are behavior-based, and segment-based ones.
In cohort analysis, before going ahead, you need to decide 3 things: what to define the cohort by, which metric to use, and the period over which to measure.
(Express Analytics’ customer data platform Oyster offers cohort analysis where all the manual work like pulling raw customer data, and defining the cohorts are done for you.)
Here How Marketers Can Use Cohort Analysis
Before a marketing team starts to use cohort analysis, here are some basic questions it needs to ask:
- Do I want to know what’s working, and what’s failing in my marketing campaigns?
- Do I need insights to change my marketing strategy?
Then, it can embark on the cohort analysis journey by first, creating a cohort (s).
The latter could be of the below or any other:
Those who have signed up: Create a cohort with those who are first-time sign-ups and observe their subsequent actions.
Those who have re-purchased over time: Form a cohort of those clients who have repurchased over a certain period of time.
Those who have been retained: Find the customers your business acquired over a period of time, and then check how long they kept (or are) doing business with your brand.
These cohorts will allow you to understand one or more of the following:
- Just how well your business is at retaining new clients
- Whether your landing pages or sign up forms are working
- Why a specific cohort of customers stopped purchasing
- Which product categories are making people come back
- Which marketing channels are popular with your customers
Cohort analysis can be used by digital marketers to track your marketing campaign’s performance. Marketers can find out scientifically which of these are converting and which are not.
It can also be used to find out your consumer retention rate, and help you understand whether you need to put in more on retention itself.
Every stage of a customer lifecycle can be monitored to understand whether customers are being given enough attention at every step in the funnel or not?
Now, to help you understand how cohort analysis can help marketing here’s an example by our data analyst Pankaj Katkar. In this illustration, Pankaj has taken the “Online Retail Data Set” from the UCI Machine Learning Repository. This is a transaction data set containing all the transactions between 12/01/2010 and 12/09/2011 for a UK-based online retail firm. Using this data, he has done cohort analysis on the number of active users starting from a particular month. Here, we are doing cohort analysis based on the transaction done by the user in the month as it is a one-year dataset.
To download this example, fill this simple form.
In conclusion: Marketing analytics tells you what’s working and what’s not. Cohort analysis will tell you how to adjust your marketing activities, and which marketing activities your team needs to focus on. It helps you answer questions like what’s your users’ retention rate and when do users start to churn, among others.