6 Big E-Commerce Secrets Cohort Analysis Can Reveal

🤔 Quick Question: How do you analyse customer data? Do you compare month-wise conversion rates? 📈

If this is how you analyse data, you are stuck in old ways, and it’s high time you take a leap. Analysing data like the one mentioned above does not tell you the secrets it has hidden underneath.

So, how do you go about it?

Well, cohort analysis is the answer. 

Let's dig deeper to see what data has to reveal about your e-commerce growth via cohort analysis. 🚀

What is cohort analysis?

Grouping your customers who subscribed to your brand in April 2023 is an example of a cohort. To put it more sophisticatedly, a cohort is a group of users that share a certain characteristic.

Cohort analysis refers to studying changes in consumer behaviour throughout the customer journey. 

For example, customers who subscribed to your brand in April will most likely behave differently from those who subscribed a few months before. 

Cohort analysis sheds light on how these cohorts behave over time, what makes them convert, or where they leave your brand. Cohort analysis tells more about your e-commerce growth and investigates your marketing performance in greater depth than ever before.

Here are the 6 secrets that cohort analysis can reveal about your brand 🤩

👉 The time needed to convert a subscriber to a customer 

If you think someone who has subscribed to your brand today will purchase from you the day after, then you are getting it all wrong. Long-term nurture is essential to convert your email subscribers into customers. The question that arises here is how you nurture these potential leads. 

Cohort Analysis comes in handy at this point. It lets you know your users from head to toe and plan your marketing campaigns accordingly. By using cohort analysis, you can compare which campaign types convert more subscribers into customers. 

The probability of a group of people who subscribed to your brand 6 months ago is way higher than those who subscribed a week ago. Cohort analysis will tell you how the average time-to-convert changes month-on-month and let you gauge the effectiveness of your campaigns. 

Start by grouping your subscribers according to the dates they first visited your website and analysing the time they took to make their first purchase. Compare this time frame with other cohorts and see the progress. 

👉 The time needed to convert a customer to a repeat customer

For improving a brand's sustainability, focusing on customer retention and getting repeat purchases is a must. An incredible customer experience, trust and loyalty are essential for converting one-time customers into repeat customers.

With cohort analysis, you can analyse the purchasing patterns of different cohorts and plan your marketing campaigns. For example, you could group customers by the week/month they were onboarded and then evaluate the revenue generated from that group over the following 6-12 months.

Deeply analysing a particular cohort will help you understand the shopping pattern among your customers. It might be the case that this cohort typically takes 3 or 4 months to come back and shop again. Insights like these can help you effectively align marketing campaigns and get them back for another shopping spree. 

👉 The right way to nurture different customer lifecycle stages

There are five stages of the shopper journey. It begins when a shopper has the need or desire for a product or (in most cases) when the impulsive shopping nerve hits. He then visits an e-commerce website, browses the collection and adds items to the cart. This takes him to the checkout page, where he makes the payment, and voila! Order is placed!

By building cohorts around what lifecycle stage your customers are in, you can elevate the experience at each stage and help your customers move down the purchase funnel. This will help you spot patterns in how customers interact with your brand. It also gives you an idea of how to pay more attention to specific stages of the customer journey to prevent customer churn.

For example, you can group customers by Lifecycle Stage and note the change in revenue over 12 months. The results might reflect that the first six months of active customers are the most valuable. Similarly, the analysis for at-risk customers might tell you that they tend to stop shopping entirely around the seventh month.

Analysing cohorts by life cycle stage helps solve the customer engagement puzzle. It answers questions like how an engaged customer's behaviour differs from that of a disengaged one and what strategies should be undertaken to spark interest.

👉 Implications from the differing purchasing habits of segments

How much do you know your customers? Do you know their names, ages, genders, professions, and interests? If yes, you have checked all the growth boxes. If not, this is where you need more. You can't succeed if you don’t know who you are selling your products to. 

Knowing your customers inside out is the key to providing them with the best experience and retaining them for a long time. This is where 360-degree customer profiling becomes crucial. Get all the data about your customers and create unique customer profiles. This helps you understand their needs and build meaningful relationships. 

By grouping customers location-wise, you can see whether there’s a big difference in customers' purchasing habits in different cities. This will help you to know location-specific elements about the lifetime value of customers.

Moreover, creating different cohorts for genders can help you determine the shopping habits or average CLV. You can use this information in framing your customer acquisition campaigns.

👉 Getting the most out of the channels that bring the most revenue

Omnichannel is the driving force of customer engagement. When you engage customers on multiple channels, the question arises, which channel brings in the most revenue? Well, cohort analysis answers it all!

Cohort analysis helps you group customers by the channels they use to interact with your brand. By this, you can know the channel that brings in the most revenue, generates more repeat purchases, etc. 

You can adjust your investments once you know which channels bring you the most valuable customers. 

👉 The best of the best: Seasonal shoppers or regular shoppers?

Festivals are the times when e-commerce sales are on the rise. Brands witness an uptick in new customers and more purchases from existing customers during this time. With cohort analysis, you can identify seasonal shoppers who shop around festivals but disappear for the rest of the month.

You can also look at the new customers acquired during this time and compare this percentage with customer acquisition figures for some other months. If there is a large gap, you can invest in marketing campaigns to keep them around.

Concluding Remark

Dividing customers into different cohorts and analysing how their behaviour changes over time is what cohort analysis means. It contributes to improved targeting and even better conversions. Knowing what works for one set of cohorts and what does not helps to plan well-targeted campaigns. Furthermore, a before and after image indicates what might be effective, allowing you to run A/B tests on new initiatives and continuously improve.