How do you measure customer lifetime value?
Customer lifetime value is the measure of a customer’s total or predicted spend with your brand. It’s one of the most critical metrics for understanding the impact of your business activities, but it’s also one of the most difficult to calculate. Here are 3 approaches to get you started.
You know by now that it’s easier to market to existing customers than new ones.
This upselling increases the value of a key metric that is both vital and hard to measure: Customer lifetime value.
Customer lifetime value helps you understand the health of your business and the quality of the customer experiences you offer, and this customer insight can help you increase your marketing ROI substantially. If you get it right, you can use customer segmentation tools to divide your audience by predicted lifetime value and focus your ad spend and other efforts on the highest-value segments.
The challenge, of course, is measuring it.
How do you measure customer lifetime value?
There are three basic approaches you can take to customer lifetime value analytics:
- Total spend. This is by far the easiest metric, and the most immediate, but it also only measures historical data. You calculate it simply by subtracting the total value of any refunds from the total value of a customer’s orders. It’s a quick way to establish the value of a specific customer and can help you start to segment, but it doesn’t have a predictive value.
- Predicted value. Calculate this by multiplying your customers’ average spend per year by the average customer lifetime. The latter tends to vary by industry. In some industries, most customers only buy once and customer lifetime value is less useful. A more complicated way to do it is to use machine learning to adjust for seasonality and growth, predicting future spend. Unfortunately, this requires pretty sophisticated IT resources.
- Predicted lifetime spend. You do this by adding the two items above. This means that it needs an accurate predicted value, resulting in the same issues of needing high levels of IT resources, machine learning, and data.
Unless you are satisfied with a quick and not very predictive measure, it’s clear that measuring customer lifetime value takes a lot of data. That data has to be recorded and measured over time, across shopping channels, and even between online and brick-and-mortar sales if you do both.
Generally, it’s easier to calculate lifetime value when you are handling larger purchases. For retail sales, it can be a little harder, and you need to know your customers’ average order value, purchase frequency per year, and lifespan.
Lifespan, here, means how long they are going to continue to purchase from your brand. For example, if you sell fashion and apparel, then the ideal lifespan of your customer is the part of their life through which they purchase that apparel. For kids’ clothing, this might be relatively easy—if you sell clothing for children aged 3 to 5 as a specialty, then your customer’s lifespan is how long they have children that age, and fairly short (although if you play things right they might come back later when their grandchildren hit that age). For adult clothing, it depends on fashions, trends, and the typical age of your customer.
Pro tip: Don’t make the mistake of over-relying on averages
Often, marketers make the mistake of using their average customer lifetime value to inform their activities. The lifetime value of your average customer might tell you something about the overall value of your brand, but it’s not a useful measure for actually increasing lifetime value.
Instead, you want to identify factors that increase lifetime value, whether they are demographic or based on customer behavior. You can do this by segmenting your customer database by lifetime value and analyzing the characteristics of the highest-value segments. Which factors keep those customers happy and coming back for more? Using this customer intelligence to inform campaigns can help you reduce churn.
Finally, you need to know what good customer lifetime value actually is for your industry. This depends on the cost of your product, how often people need it, etc. For example, a car dealership knows that they will be selling a high-value item to a customer every few years. The overall customer lifetime value is going to be different from a retail outlet that sells a variety of relatively low-priced products. The car dealership has fewer customers, so each one affects its bottom line more drastically.
This can be hard to determine, and is a place where looking at averages can in fact be helpful.
What kind of tools and software can help you measure customer lifetime value?
In order to properly measure customer lifetime value, you need a Customer Data Platform (CDP). This is a software system that consolidates all of your customer data and metrics from every channel, including data enrichment from third-party sources, into one place. Then it automatically calculates each customer’s predicted lifetime spend for you.
This means that you can quickly and easily segment your customers and then you can go a step further and start analyzing what makes for a high lifetime spend.
For example, perhaps you have a specific product that seems to be the “gateway drug” for your customers. You can easily see that customers who buy this particular item first are likely to stick around. Why is that? The simplistic response might be to lower the price of that item, but you may need to look at more data first. It’s more likely that you’re seeing a correlation: The kind of customers who buy that product are also the kind of customers that spend more.
Going back to the children’s clothing example, one obvious way to increase customer lifetime value is to target families who have more children. The lifespan of a family with one child (or a set of twins) is two years, but if they have four children two years apart, it becomes 8 years. However, you have to have the data to be sure. It could be that the large families are instead handing clothes down and not buying new clothes for the younger siblings as often. Ultimately, this data-driven retail approach will give you an unprecedented competitive advantage.
To find out more about customer-first, segment-level reporting using metrics like customer lifetime value, you should read our playbook “Track: Understand How Your Marketing Impacts Customer Behavior and Core Business KPIs.”
Lexer is the CDP of choice for leading brands like Quiksilver, Igloo, Nine West, Rip Curl, Supergoop!, and more. As the only CDP built for retail, we help the world’s most iconic brands drive incremental sales from improved customer engagement.