Think Tank: The Case for Upgrading Customer Lifetime Value
In theory, we all know that long-term customers are far more valuable than leads or even freshly minted activations. Yet when the rubber meets the road, marketers time and time again focus on short-term acquisition and conversion gains over long-term growth.
As a result, marketers are turning their backs on customer lifetime value and it’s the most dangerous movement against the progression of modern marketing.
Customer lifetime value is the metric that supports profitability over pure growth. But a recent a global survey of 300 executives from retail and publishing organizations revealed that while 69 percent of retail executives feel that they have a significant understanding of the causes and effects of CLV, only 15 of management teams understand the impact of lifetime value on company revenue and growth.
To embrace CLV as the metric that matters most, leadership teams must refocus reporting cycles and update how CLV is both calculated and operationalized.
Reporting cycles require short-term focus
Any smart marketer can think of hundreds of tactics to improve conversions in the short term, but most of them boil down to some kind of sale or incentive. And with those incentives, all the careful planning around how to cultivate repeat buyers and breed loyalty is cast aside in favor of hitting short-term numbers. This happens quarter after quarter, year after year.
The holiday season is a classic example of where we go wrong. This is when most marketers pump a significant portion of annual marketing dollars into paid search and other acquisition tactics with the end goal of driving sales. But had those marketers invested more time and resources in nurturing the customers they acquired years ago to remain repeat and loyal customers, the gap that would need to be filled to hit sales targets would be significantly smaller, and top-of-funnel would require substantially less investment during the most important time of the year.
Another key finding from that recent study was that companies increasing investments in retention rated their performance in both retention and acquisition as above expectations. They beat their expected performance in retention by 100 percent and over performed in acquisition by 88 percent, whereas those companies increasing acquisition investments rated themselves as 100 percent over performance in acquisition and only 40 percent in retention.
What this means is that increasing focus on retention isn’t really just about retention; it’s about growth: stronger acquisition leading to a higher quantity of higher quality long-term customers.
To shift the conversation away from short-term success and toward long-term gains, marketers must connect the dots between lifetime value and profitability. All too often we are blindsided by something as simple a cost per acquisition metric with little regard for the long-term value of those cohorts. In other words, what happens when only two percent of the leads we acquire actually transact? In actuality, some of the marketing investments that feel most expensive today — and are often optimized right out of the mix because of seemingly high upfront conversion costs — could yield the strongest long-term customer value; direct mail is a long-lost example.
Remember, growth may win daily headlines, but profitability wins years of success (never mind business solvency).
Long-term math is harder
Marketers gravitate toward short-term measurement, like simple acquisition ROI calculation, because the math is short-term and straightforward — this many dollars out the door yesterday and this many dollars in sales today. Customer lifetime value has more unknowns, ranging from the future value of money to churn rates and even black swan events that could wipe out entire industries.
The academic definition of CLV is the present value of the future cash flows a customer will generate during her entire relationship (“lifetime”) with your brand. Pragmatically speaking though, CLV calculation is often just a manipulation of historical purchase data: marketers often simply multiply average order value and purchase frequency in a given period (in practice, typically a year or two of data versus the so-called “lifetime”) as well as expected margin. The most thoughtful marketers might even apply a discount rate to run a true discounted cash flow analysis.
Most frustration with CLV as an operational metric stems from calculations using these legacy CLV frameworks, especially those that rely on inflexible recency/frequency/monetary, or RFM, modeling. While RFM undoubtedly works for segmentation purposes, it is limiting for CLV because it is backward-looking and only takes into account a brand’s known buyers. For many online retailers, this group might constitute less than 20 percent of the overall customer file. Prior purchase behavior is a single dimension for determining future outcomes.
Work smarter, not harder
Predictive CLV models take into account years of data and look for patterns of engagement decay (or conversely, long-tail trends) across a wide variety of different time series — not just purchases. Consider the example of a high-ticket item that might command a longer buying cycle: relying on historical purchase data would present a bleak outlook for CLV. Instead, if we know that buyer has been reading reviews on the site, opening e-mails, etc., we could thereby predict the likelihood of purchase/value based on what the consideration-buying cycle looked like for customers in the past.
Lifetime value skeptics need to embrace newer CLV measures enabled by big data. Newer CLV models can include more than 50 different inputs for CLV, ranging from number of e-mails or push notifications received to number of site visits and click-stream behavior. The math is bigger than any individual or team can produce on their own, but machine learning technologies make it available at a fraction of the cost and a multiple of the speed.
More importantly, these models can predict CLV for the individual versus the segment or decile. And most importantly of all, when embedded in a marketing cloud, these CLV predictions enable marketers to optimize swiftly and effectively while always keeping a tangible pulse on the long-term implications of their optimization decisions. Above all, they empower marketers to think of the “future” as more than just a holiday season away.
We’re no longer operating in the dark days of inaccessible data and nascent technologies that once precluded a single customer view. With this, there is simply no reason why marketers should not be embracing their ability to upgrade their approach to calculating CLV and in connecting this metric to long-term revenue growth.
Lancellotti-Young is executive vice president of customer success at Sailthru.
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