By Suraj Dubey
Today, it’s 5 times costlier to acquire a new customer than it is to retain an existing one. In crowded markets where users are often overwhelmed with choice, creating customer stickiness is everything.
In fact, companies with low customer stickiness eventually run out of customers and slip into a downward spiral of negative returns.
Luckily, customer retention is no longer an esoteric art that is based entirely on instinct.
Exponential increase in computing power, advanced analytics, and progress in behavioural science have made it possible for companies to use data-driven techniques to understand how to retain customers. Cohort analysis is one such technique.
What is a Cohort Analysis?
To put it simply, a cohort is a group of users who share a common characteristic over a period of time.
Cohort analysis is the study of the common characteristics of these users over a specific period.
Say a group of users sign up for your app in the month of November. Cohort analysis will help you understand how many customers continue to be active users in the days/weeks/months that follow.
There are two main types of cohorts used in cohort analysis. The first is acquisition cohorts, which divide users based on when they signed up for your product.
The second is behaviour cohorts, that divide users based on the activities they undertake on the app during a given period of time (eg. users who share photos using Google Photo links on a given day).
Using Cohort Analysis to Measure Customer Retention
Cohort analysis is a data-driven way of figuring out whether your product is actually able to inspire that all-important customer loyalty.
Cohort analysis allows you to perform certain key tasks, which collectively help in maximizing customer retention. These include:
- Identifying the features, activities, and changes that retain customers
- Figuring out where customers are dropping off and altering user flows accordingly
- Proactively planning customer engagement activities based on feature adoption
- Putting in place a data-driven, personalized recommendation engine which is completely organic and non-intrusive
- Predicting future user behaviour with present data
Building Your Cohort Analysis Around Key Metrics
One of the common problems marketers run into while analyzing customer retention is the overwhelming amount of information involved. Cohort analysis breaks through the noise to identify the few metrics that really matter. These include :
- Repeat Rate – the share of customers who transact with your business repeatedly versus those who terminate with a single purchase
- Orders Per Customer
- Time Between Orders
- Average Order Value (AOV).
Even among these, you might have to pick 1 or 2 metrics that are most relevant to your product
Leveraging Insights From Cohort Analysis
Cohort analysis is likely to generate a whole bunch of insights into your customer behaviour.
The best way to leverage this information is to design separate campaigns with specific outcomes for each insight and then bring them all together to boost customer retention.
Tweaking the user journey, planning personalized reactivation mails, creating targeted offers, and introducing customized loyalty programs are all examples of targeted customer retention strategies.
Former Netscape CEO Jim Barksdale once said, “If we have data, let’s look at data. If all we have are opinions, let’s go with mine.”
In today’s day and age, opinions, no matter how experienced or well-informed, have to be backed by solid data-driven insights. Cohort analysis is one of the most useful techniques to help you get there.
Suraj is a marketing tech enthusiast. His varied career includes a stint with Ogilvy, where he helped companies like IBM, SAB Miller, Unilever, and The Himalaya Group create brand narratives. At MoEngage, he’s driving the Accelerator Program custom-built for app & web-based consumer startups to help them get exclusive access to cutting-edge marketing technologies. He loves to chat about marketing, technology, music, and motorcycles.