To create a thriving business, you need a handle on retention metrics.

This post serves as an overview of retention, and how eCommerce stores can measure their success.

The key to successfully tracking retention is defining what retention means for your company.

For eCommerce stores, the most common and simplest form of retention is based around repeat purchases. But this gives a singular view on retention. 

To have a more nuanced view, stores can define multiple retention definitions, and use them to better segment customers. We've put together a few guides on customer segmentation techniques you can use below.

The core retention metrics is "customer retention rate". Customer retention rate measures what percentage of customers continue to buy over a given period of time.

We wrote a full guide on retention rates, and I encourage you to check it out here.

In brief, we calculate retention rate by defining what a retained customer is and what time frame we are interested in.

The final equation looks like this:
(Customers Ending - Customers New) / Customers Starting

Customer retention rates show how a customer group is behaving in a moment in time.

Customer retention curves build on rates by plotting them over time. This results in a "curve", which allows eCommerce stores to assess efforts at bringing customers back.

To create a retention curve, simply compute a customer group's retention rate at specific moments in time and chart them in a line graph as shown below.

There are three general "shapes" your retention curves can have. 

Cohort analysis brings much more detail than retention rates or retention curves can.

As outlined in our article, "How to use Cohort Analysis to Increase Sales, Ad Spend, + More", cohort analysis is a behavioral segmentation technique focused on tracking different user behaviors over time.

While retention rates give you a moment in time understanding of your entire business, cohort analysis lets you see how specific segments of your customer base are interacting with your store.

Typically, cohort analysis is displayed in a table format, like this.

The next step in understanding your customer's retention is to layer in more complex behaviors The first of these is purchase frequency.

Most brands define retention as whether a customer bought within a certain timeframe. For most businesses, that is every 30, 60, or 90 days.

However, this treats a customer who makes a single purchase in 90 days the same as one who makes five purchases.

This is where purchase frequency comes in. Recall our discussion in RFM Analysis where we detail how purchase frequency is a key indicator in which types of offers they will respond to.

Below, Target crafts a personalized email campaign to increase purchase frequency. Note how the offer, a $30 gift card, gives customers a clear incentive to come back.

Traditional lifetime value metrics have a number of challenges.

It is often impossible for eCommerce businesses to have enough data, it assummes your product mix, customer behavior, and acquisition channels won't change, and doesn't take into account cash flow concerns.

For these reasons, we suggest implementing payback periods as a fundamental customer retention metric.

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Establishing a payback period lets you have concrete data around your metric. It also has the benefit of focusing your attention on the most pivotal moments a customer has with you - the first 30 days after purchase.

Barilliance helps eCommerce stores improve retention metrics by

If you would like to see how Barilliance can help you create personalized retention marketing campaigns, request a demo below.