We know that CLV is a construct and metric that can enable firms to maximize customer profitability and augment firm and shareholder value. But how does this actually work in the real world? The following two case studies demonstrate the practical value of implementing CLV strategies, integrating extant marketing theory within each firm-specific context.
Case Study: IBM
The business problem:
IBM, a leading multinational, high-tech, B2B firm, faced challenges regarding the effectiveness of its customer contact strategy. Throughout the 1990s, they relied on a Customer Spending Score (CSS) to prioritize marketing communications to high-value customers. But this approach was soon abandoned, as it only focused on revenues and neglected the service costs. As an alternative, a CLV-based strategy was considered for scoring customers. In this regard, the following business challenges were identified (Kumar, V., Rajkumar Venkatesan, Timothy R. Bohling and Denise Beckmann (2008)):
• Which customers to select for targeting?
• Is there a way to determine the level of resources to be allocated to those customers?
• How can the selected customers be nurtured in order to increase future profitability?
Using data pertaining to IBM’s mid-market customers, a customer lifetime value (CLV) management framework was proposed and implemented in two stages. In the first stage, several models were developed to generate inputs for implementation. In the second stage, a field study was conducted based on recommendations from the models developed in the first stage. The CLV management framework is intended to guide the marketing activity directed towards customers each year.
How it works:
Stage 1 of the CLV framework comprised of the following 4 phases.
Defined, and developed a model to measure CLV for each customer.
Conducted a prediction exercise to compare the performance of customer’s rank ordered based on the traditionally used metrics with that of CLV.
To maximize CLV, an optimal contact strategy was developed to allocate communication resources (i.e., channels of communication such as telephone, catalog, email, and direct mail) to each customer.
Propensity models were built for each product category to identify the product to feature in addition to the CLV measure which helps to select the customers for targeting, and the optimization process which suggests the contact strategy.
Stage 2 of the CLV framework comprised of the following 2 phases.
Customers were split into two groups- (1) not contacted so far (Not Contacted until 2004 group), and (2) previously contacted (Contacted by 2004 group). Marketing contacts were then reallocated to align resources to the high CLV customers.
Customers with potential for higher CLV but not contacted so far – i.e. the Not Contacted until 2004 group -- were contacted in 2005 as per the recommendations of the CLV based approach. The performance of this group of customers was compared between the years 2004 and 2005 to illustrate the impact of the model recommendations. Further, the model recommendations were evaluated even for the intersection of the “Contacted by 2004” group and the Contacted in 2005 group to see if the suggested approach missed out on existing source of revenues. In other words, it was determined whether the recommendation that a certain customer be contacted in 2005, did in fact yield a higher revenue than the customers who were not contacted.
The results revealed that, on average, the revenue from the customers who were Not Contacted Until 2004 but contacted in 2005 increased by 10 times (across all customers) as shown in the figure below.
The net result was higher revenues, and higher value in 2005 (versus 2004) for the No Contact until 2004 but Contacted in 2005 group of customers. The total increase in revenues for the “No Contact until 2004 but Contacted in 2005” group of customers is about $19.2 million dollars. The following figure illustrates this finding.
The total revenue from the customers who were contacted in 2004 and 2005 (based on the model recommendations) was over 750 million dollars (after accounting for the direct marketing expenses). Therefore, the proposed model recommendations did not miss out on identifying existing sources of revenues but also identified new sources of revenues. The reallocation of marketing resources led to about a 3% increase in profits from 2004 to 2005 for the mid-market customers from contacting only 1% of the IBM customers. The impact of the CLV-based campaign on the profitability of mid-market customers has accelerated the adoption of the CLV management framework for other customer segments in IBM.
Kumar, V., Rajkumar Venkatesan, Timothy R. Bohling and Denise Beckmann (2008), “The Power of CLV: Managing Customer Lifetime Value at IBM,” Marketing Science, Vol. 27 (4), pp. 585-599.
Kumar, V., Denish Shah, and Rajkumar Venkatesan (2006), “Managing retailer profitability—one customer at a time!,” Journal of Retailing, 82 (4), 277-94.