What is Customer Lifetime Value (CLV)?
In today's acquisition driven marketing environment, many marketers have lost sight of one of the biggest predictors of retail success... Customer Lifetime Value. Let's start by offering a simple definition of Customer Lifetime Value, or "CLV":
Customer Lifetime Value (CLV) is defined as the total dollars flowing from a customer over the entire relationship with that customer.
Here's a simple example with a customer that has made multiple purchases over a period of time.
Aug 2012: $ 50 Feb 2013: $ 55 Mar 2014: $ 65 ============== CLV: $170
One could say this customer has a Customer Lifetime Value of $170, or a CLV of $170. Often, marketers will try to box in CLV to a time period to create some consistency. We have many customers who look at CLV as a 3-year metric. In this case, assuming Aug 2012 was the start of the relationship, one could assess that this customer has a 3 Year CLV of $170.
This CLV calculation now presents you with some interesting data and the opportunity to take action.
If you discover that the 3 year CLV of a customer who buys a certain product (like coffee, for example) is $300, then the $170 about 18 months into a relationship as shown above would appear to be on pace. If the 3 year CLV of a customer was actually more like $1000, and you were at $170, then you've got a problem. (This, BTW, is where Retention Automation comes into play... your Retention Automation platform should recognize this churning or underperforming customer, and engage a series of win-back campaigns to get them performing in line with expectations).
Projected or Predictive Customer Lifetime Value (CLV)
Predicting what a customer is going to spend over their lifetime requires significant statistical analysis. The good news is that technology has advanced to where your Retention Automation platform can do that work seamlessly for you.
Many retailers start (appropriately) with understanding what historical CLV for a given customer looks like. For example, if you purchase a TV as your first purchase, one can look at everyone that bought that TV and assume that their CLV will map to that. Everyone that buys a pair of socks will have a different profile. There will be marginal error here, but most retailers have little idea what their CLV is at all, versus by product or by cohort, so let's not try to boil the whole ocean at once. Start by getting comfortable with looking at customers through the lens of what they will be worth to you over a 3 year period or a lifetime of purchases, and making decisions in that framework, and you will be greatly advanced from where you are today.
Once you've gotten your first step down, you progress to regression analysis, and then finally to cluster analysis and anomaly detection. These become incredibly interesting and powerful as you ratchet up accuracy and push down marginal error. These are also intensive and expensive, but in optimized situations and in situations at scale, these can be incredibly productive.
Learn more about how Customer Lifetime Value is the New Black in Retail in this slideshare.
It Starts with Getting the Data
At Windsor Circle, we took a different approach. We have enterprise-class integrations to all leading commerce platforms that allows us to instantly load 3-5 years of purchase history, so our customers instantly see CLV for various segments and personas based on purchase history and others factors.
If you'd like to learn more about Customer Lifetime Value (CLV), or see the CLV of your various customer segments, feel free to contact us for more information or get started with a free trial of retention analytics for 60 days.