Data without insight and application is just disconnected numbers and information. Accordingly, customer lifetime value (LTV) is just another figure unless marketers apply what they learn from it to their acquisition and retention strategies.
Formulas vary by brand and industry, but the basic concept of LTV is easy enough to articulate. LTV attempts to express the stream of revenue expected from all of a customer’s future purchases, with a discount factor for future revenue, to capture the time value of money. More sophisticated LTV measurements apply other adjustments, such as a discount reflecting the likelihood of churn, or an increase reflecting the likelihood of upselling or cross-selling a customer at a future date. “As you add in more elements, LTV becomes more accurate and actionable from a business perspective,” says Wilson Raj, global director of customer intelligence at SAS.
In fact, marketers can use LTV not only to determine how much to spend on customer acquisition, but also to optimize their spending by selecting the right acquisition channels, driving retention decisions, and refining their assumptions. Here’s how:
Selecting acquisition channels
Using LTV to guide channel selection for customer acquisition is a must because acquisition channels, in turn, can have a huge impact on LTV. The obvious contributor comes from cost per lead or per conversion, which can vary wildly between channels. The more subtle effect comes from the propensity to become a repeat customer, which some brands observe is significantly different depending on the point of initial contact. A customer obtained through a third-party daily deal site might be less likely to commit to future purchases than one obtained through organic search, for example.
This puts the LTV focus at the top of the funnel, guiding how acquisition budgets are not just determined, but are spent. Although these LTV models can be predictive in nature, they tend to focus on backward-looking data, based on the recency, frequency, and monetary (RFM) characteristics of historical customers acquired through those channels.
“You can add in hypotheses and simulated events to test out scenarios, like next year’s purchasing cycles being different,” says Jonathan Beckhardt, founder of DataScience.
Particularly for brands offering a product where renew-or-churn decisions are made on a regular basis, marketers can introduce predictive analytics based on signals received at and after conversion to understand whether a customer is likely to renew after the initial purchase, and even whether a customer will have the same needs or be facing the same slate of choices in the market.
Driving retention decisions
Marketers can also use LTV inform decisions about how best to nurture and retain customers, including which customers to focus on. These choices are more politically difficult, because an LTV model may recommend disregarding certain customers who produce healthy revenue today but the model shows will become less profitable in the future.
Financial services firms face these choices on a regular basis. Younger customers have few assets and need for financial products, but over a lifetime may become highly profitable. Older customers have assets and products already on the books, but have already made most of the major financial services purchases they will ever need in their lives.
LTV can help marketers from any industry determine on which customer segments to invest their retention dollars. Marketers can also use LTV calculations to take early action and cultivate brand engagement that tends to lead to loyalty and future purchases.
These forward-looking models succeed most when marketers also use them to find lookalike customers—prospects who have the characteristics for long-term profitability based on behaviors and traits of current high-value customers.
One caveat: Using LTV to make retention decisions regarding longer-term value can be a tough organizational sell because of pressures to realize revenue today, or at least before the end of the quarter. “You have to be able to tell a story longer than 90 days,” says Rob Heiser, CEO of Segmint. So, these decisions require stakeholders—and shareholders—who believe in the models.
Refining assumptions
LTV provides a momentary snapshot based on a set of assumptions. So, to make the most of LTV, marketers must commit to continually reevaluate their models and their assumptions, and change behavior accordingly. The mobile carrier industry provides a perfect example of a major strategic change brought about by reexamining lifetime value.
For years, mobile carrier acquisition strategies were driven by subsidized handsets, with the assumption that a customer’s long-term customer value would more than cover the discount on the phone. As regulatory changes made it easier for customers to change providers and carriers increasingly targeted each other’s customers with aggressive switching discounts, these LTV models proved unworkable. Today the subsidy strategy is all but dead, forced out by a revised understanding of true lifetime value in this space.
No predictive formula can control for every product innovation, competitive shock, or black swan event that completely reshuffles the deck or threatens the viability of a brand. But customer lifetime value does give marketers a tangible, robust way to express the true worth of their company in the only way that really matters over the long term: the ability to turn future potential into revenue. After all, as Customer Portfolios CMO Nick Godfrey asserts, “The value of a company today is the accumulated value of all its customers.”
