Why Customer Lifetime Value Has Outlived its Value

Press enter to search
Close search
Open Menu

Why Customer Lifetime Value Has Outlived its Value

By Kate Atty - 05/26/2016
Traditional views of customer lifetime focus on the dollar value of a customer relationship and are used to determine how much a marketer can spend to acquire a customer while still making a profit. Part of this equation is dependent on an understanding of the duration and intricacies of that relationship. Because of this, effective customer lifetime value (CLTV) models must rely on a holistic and accurate understanding of the customer lifecycle.

Generating reliable inputs to feed these models has proven to be one of the biggest challenges for marketers, mainly because of the fragmented nature of the modern customer journey. In fact, many CLTV calculations are simply inaccurate due to a lack of understanding of the customer lifecycle.
 
In 1988, the concept of lifetime value was first defined in the book Database Marketing by Robert Shaw and Merlin Stone. Early adopters began using LTV models in the early 90’s and soon after the buyer journey began to evolve with increasing speed and complexity. The introduction of Amazon and eBay in 1995, the rise of the dot com in the early 2000’s and widespread adoption of smart mobile devices today have reshaped the way people interact with brands and ultimately make purchasing decisions. All of this has led to a customer journey that looks very different than the one back in 1988.
 
The modern customer journey involves numerous digital and physical touchpoints, leading marketers to become increasingly focused on tracking customer data especially via digital platforms. To get a true picture of the customer journey, marketers must synchronize data sources among multiple internal and external databases including email, mobile, web, and point of sale (POS). Calculating LTV is possible without these inputs, but the projection is based on historical data and provides more of a benchmark for the marketer. In order to act on and manage lifetime value in real time, marketers must be able to aggregate these data sources at the customer level throughout the relationship.
 
Another predictive model used to understand a consumer’s value to a brand is RFM. RFM distinct from LTV in that it takes a snapshot of a consumer at a point in time, giving them a score based on three components:
  • Recency - how recently did a customer purchase?
  • Frequency - how often do they purchase?
  • Monetary - how much do they spend?
RFM provides flexibility for retailers to adjust the scoring model based on their needs. For example, an e-commerce retailer might put more emphasis on recency and frequency compared to a brick-and-mortar store, which prioritizes the monetary value because it’s their most reliable input.
 
RFM provides the basis for much of the segmentation used in marketing today. Marketers craft messages designed to motivate an increase in RFM for a given customer - perhaps to re-engage a lapsed customer, to encourage a return visit or increase basket size. Because of this, a customer may have many RFM scores in their lifetime, based on how they interact with a brand at a given point in time.
We have seen immense change in the customer journey in the past 25 years. This change has affected the way brands perceive the value of customers and how they spend their dollars to retain or acquire them. Looking ahead, the more a marketer knows about the behaviors and interactions of their customers, the more effective any model will be in both determining this value and taking action to optimize it. 

Kate Atty is Director of Marketing at Persio. She is responsible for implementing and driving the marketing strategy and oversees content creation and promotion, PR, messaging, branding, and lead generation efforts. Prior to joining Persio, she was Regional Marketing Manager for investment firm Alliance Bernstein, and oversaw the marketing strategy for three regional offices.