In today’s world of nearly ubiquitous entertainment, subscription services reign supreme. Hoards of consumers are increasingly willing to pay $10 a month to stream endless music from Spotify, $8.75 a week to read unlimited digital content from The New York Times, and $8 a month to binge on exclusive and shared TV content from Hulu.  Even businesses now benefit from digital subscription services, such as SAP HANA’s cloud platform, which provides its customers with an in-memory database and mobile application building and delivery services in the cloud.

As these subscription-based services proliferate and the competition intensifies within every digital content subsector, the struggles firms face to get an edge on their peers become even more intense. In the video-streaming space, for example, the battle for exclusive premium content is at a fever pitch, as streamers are battling for sole rights to popular shows and high-profile talent to create new ones.  As such expensive content is pursued, firms can’t rely on creative instinct alone in justifying their portfolio decisions; it is essential that they find a way to ensure their content bundles will be preferred by their most lucrative customers and then map those customer preferences to a quantitative value of the content.

Similar to these decisions facing streamers of digital content, business-to-business firms like SAP HANA also need to decide how they direct their resources (in this case, the time and efforts of their sales force) toward the best customers who are most likely to offer lasting contributions to the profitability of their cloud services.  In attempting to determine the most lucrative prospective customers to target (and the lengths they should go to in order to retain those customers through new content and personalized service), a critical metric firms should consider is customer lifetime value.

Customer lifetime value, or CLV, refers to the single lump-sum value a firm can presently apply to future cash flows derived from a customer relationship. In simpler terms, it’s the dollar amount a customer contributes to a firm over a lifetime. Being predictive in nature, CLV is often considered more difficult to quantify than past or current customer profitability, as it forecasts future activity in the face of multiple unknowns.

Accurate CLV calculations can be achieved, however, with accurate data detailing past customer behavior. Armed with this information, managers can multiply the per-period cash margin from a single customer by a long-term multiplier that represents the value of the customer relationship’s expected length, an amount that is driven by both customer retention and discount rates, in order to achieve a CLV figure. While some companies might choose to calculate customer lifetime value over four or five-year horizons, as opposed to an indefinite timeframe, due to the possibility of disruptive competing technology or business models interfering after four or five years, this finite method can often exclude over a third of the infinite CLV’s value.

Assigning an accurate CLV to a customer relationship allows firms to optimize their marketing budgets in a number of ways. For one, CLV reveals the true value of a prospective customer and, in turn, the amount that the firm should be willing to pay to acquire that customer. When used for this popular purpose, the metric will often encourage firms to spend more on acquiring certain customers, whose value is more appropriately measured across their lifetimes or extended periods and not simply through the calculation of a single purchase (or set of purchases).

On the flip side of this coin, Venkatesan and Kumar’s research has proven  that an accurate predictive CLV also allows firms to better hone their retention marketing strategies, as knowing the value of any customer relationship will guide firms on the amount they should be willing to spend to avoid losing that customer. Customer relationship management initiatives and product enhancement could all increase the average customer retention rate and in turn deliver great financial rewards to the firm when calculating the positively impacted CLV across all of the impacted customers.

Even companies that do not operate through subscription-based relationships with their customers have a great deal of value to derive from using CLV as they approach their marketing decisions. In 2005, for instance, IBM entered into a pilot that used CLV as an indicator of customer profitability for around 35,000 of its customers. Using CLV as its guide (as opposed to a profitability metric used in the past that ignored the cost of serving each customer), IBM decided to reallocate marketing resources, ranging from direct mail and telesales to email and catalogs, for 14 percent of its customers, with the level of marketing investment remaining equal. This resource reallocation ultimately led to a revenue increase of about $20 million (a tenfold increase) and was projected to accrue over $1 billion if applied to the entire customer base.

As CLV continues to prove its value to firms with subscription-based services and beyond, it shouldn’t take a lifetime to see the value it adds to complex marketing decisions.

Rajkumar Venkatesan co-authored “A Customer Lifetime Value Framework for Customer Selection and Resource Allocation Strategy,” which appeared in the Journal of Marketing, with V. Kumar of Georgia State University’s J. Mack Robinson College of Business. The paper won the 2016 ISBM-David T. Wilson-Sheth Foundation Award for Long Term Impact in B2B Marketing.

About the Expert

Rajkumar Venkatesan

Ronald Trzcinski Professor of Business Administration

Venkatesan is an expert in customer relationship management, marketing metrics and analytics, and mobile marketing.

Venkatesan’s research focuses on developing customer-centric marketing strategies that provide measurable financial results. In his research, he aims to balance quantitative rigor and strategic relevance.

In 2012 Venkatesan published “Coupons Are Not Just for Cutting Prices” in Harvard Business Review. He also co-wrote “Measuring and Managing Returns From Retailer-Customized Coupon Campaigns,” published in the Journal of Marketing in 2012. He is co-author of the book Cutting-Edge Marketing Analytics: Real World Cases and Data Sets for Hands-on Learning.

B.E., Computer Science, University of Madras, India; Ph.D., Marketing, University of Houston