Customer lifetime value (LTV) is one of the most important key performance indicators for almost all retailers. How it’s defined is not as set in stone as you might think. With the addition of omnichannel loyalty software, businesses with offline points of sale now have the ability to understand who is buying what and how much. In this post, we explore how to better leverage omnichannel segmentation as a way to dial in on customer lifetime value and what these metrics mean for your marketing strategy.


Measuring Customer Lifetime Value is nothing new. In fact, it’s one of the most important metrics that large and small retailers alike, look at when assessing the relative growth of their

business. And for good reason. This metric is a virtual gold mine of information that tells us how well we’re doing with attracting, retaining, and upselling each customer that walks into our store, ergo how loyal our customers are.

There are, in truth, two versions of the math required to calculate your results; ‘customer lifetime value’ and ‘simple customer lifetime value’. Neither of them is “right” or “wrong”. Which one you use, simply depends on your propensity for a bit of arithmetic and how exact you want to be. The main difference between the two is that the simple CLV doesn’t account for year-over-year changes in revenue, acquisition costs, etc.

For our purposes (and to make sure you don’t fall asleep reading this), we’re going to assume that you’re using the ‘simple’ method, which looks something like this:

customer lifetime value formula

Of course, there are other important variables that can be factored in here. Those include metrics like churn, decay rates, et al. While larger mid- market and enterprise business may want to dig a little deeper using these measurements, most small and medium sized businesses are just looking for a running tally of whether their customer lifetime value is growing from one year to the next and a simple LTV metric provides just that.

customer lifetime value graph


Traditional models tend to present customer lifetime value as an aggregate data point – one which groups many different types of shoppers together, equally. Unfortunately, this methodology doesn’t tell us a whole lot about how LTV can (and almost always does) differ between varying buyer personas, as defined by factors like purchase history, shopping device, or location.

What would otherwise seem a highly relevant piece of data loses its value in the vacuum of context required to decipher it. Segmenting data points over multiple dimensions and demographics allows us to build a shopping experience that match each group’s expectations. And If there’s one thing we know, it’s that today’s shoppers demand highly relevant shopping experiences.

For Example:

Demographic Segmentation and LTV

Customer lifetime value, in and of itself, doesn’t tell us that women over 50 tend to have greater LTV than men under 25. There’s a lot we can do to move the needle with this information, including manipulation of our marketing budget to lean heavily on the more profitable of the two groups.

Demographic SKU Level Segmentation and CLV

Going one step deeper, if we know that women over 50 who buy staple SKU’s, such as milk, eggs, and bread, tend to have a 20% higher customer lifetime value than women over 50 who tend to buy discretionary products like spices, baking ingredients, and snacks, we can further narrow our marketing budget down to focus on capturing women over 50 who buy the items in the first product set, in order to more effectively move the needle on our bottom line.

To find out more about how bLoyal can help you build and grow your customer lifetime value, request a call with one of our experts!