Think back to the last time you had a terrible consumer experience. Maybe it was bad enough that you even took the time to fill out a customer satisfaction survey. Perhaps you expected all those low marks to translate to some outreach — maybe a coupon or new perk or some other actionable olive branch. But instead you got . crickets?
The radio silence might simply be the result of poor customer service, but there’s a good chance it’s because your scores aren’t great either. Many companies now calculate what’s called customer lifetime value (CLV) — a metric that aims to quantify how much net profit a customer will likely offer a company. How much you’re “worth” in part dictates how much effort a company is willing to extend to keep you happy.
Customer lifetime value (CLV) is a projection of the net profit a customer will provide a company. The basic formula expresses CLV as a product of customer margin and retention rate, relative to discount adjustments. But emerging technologies, like Buy Til You Die statistical models, machine learning models and deep learning systems offer increasingly sophisticated refinements in order to provide both short- and long-term CLV projections.
A customer’s CLV score impacts how and how much that customer will be marketed to — a high-CLV customer is worth more expense than a low-CLV customer. But it can also affect things like customer service response time and whether or not a given customer is extended a special offer. A high CLV score essentially slaps a big fat asterisk on “the customer is always right.” And, according to Peter Fader (left), a marketing professor at The Wharton School of the University of Pennsylvania and one of the foundational architects of CLV, it should.
As he sees it, companies that bend over backwards to satisfy every unhappy customer are doing it wrong. They need to prioritize. Companies often have a bad habit of becoming obsessed with net promoter scores, trying to determine “what ails the detractors, then fix it for them,” he said. “[But] those customers suck.”
“We’re spending all this money trying to turn ugly ducklings into beautiful swans, and that just doesn’t work,” he added. “Instead, if we have visibility on what we think a customer is going to be worth, and then use that to drive decisions — about who to give the free stuff to, or whose call to answer first — we make better decisions and make more money.”
One basic CLV formula for subscription-based businesses divides a customer’s average monthly sales by the company’s churn rate. So a customer who pays $9 per month for a streaming service that sees 3 percent churn would have a CLV of $300.
The most common simple CLV formula is:
CLV = Margin x Retention Rate / 1 + Discount Rate - Retention Rate
Margin: How much profit a sale generates minus variable costs to deliver the product or service.
Retention Rate: The percentage of customers who don’t churn within a given time period.
Discount Rate: How much future revenue will be worth based on current borrowing rates. Some advice says default to 10 percent until a financial expert can provide more clarity, while others consider that an optimistic figure. Your mileage may vary.
The classic formula above can be helpful for basic, early stage assessments, but it starts to look pretty primitive for when you consider complicating factors. For one, retention rates are obviously never constant. And, for non-contractual businesses, determining whether a customer has truly churned or is just between purchases is tough. It also fails to incorporate the smorgasbord of behavioral signals produced whenever a customer interacts with a product or channel.
Needless to say, more advanced options ought to be considered. But before we do that, let’s take a closer look at some of the fundamental underpinnings.
Customer lifetime value calculations have become dizzyingly sophisticated, often powered by machine learning and sometimes deep learning. But the metric comes from less glamorous stock. An early influence was the old-school direct marketers of the 1970s and 1980s. Think direct mail campaigns and, later, infomercials.
“Even though they’d sell schlocky stuff on late-night TV, the perspectives they had were revolutionary,” Fader said. Those marketers spent a lot of time poring over purchase patterns, a sort of training-wheels version of the data mining we have today. That led to the RFM method — recency, frequency and monetary value. Seen together, the method holds, when a customer last made a purchase, how many purchases they have made in total and their average purchase amount are a major indicator of future purchases.
“We’re spending all this money trying to turn ugly ducklings into beautiful swans, and that just doesn’t work.”
Then, in the mid-’80s, Dave Schmittlein (now dean of the MIT Sloan School of Management) and two colleagues derived a mathematical method to turn “backwards looking” RFM data into “forward looking” estimates of future CLV. This was the birth of the seminal Pareto/NBD model.
A simple way of explaining the Pareto/NBD framework without getting lost in the probability weeds is to think of it as if people have two coins to flip. One coin determines if a customer continues to consider making a purchase, or walks away for good, and the second determines if they decide to buy or not buy if they are still considering. The framework also accounts for variance in buying frequency: some will keep making purchases over time; others will be one and done.
