Rajkumar Venkatesan and V. Kumar (2004) A Customer Lifetime Value Framework for Customer Selection and Resource Allocation Strategy Rajkumar Venkatesan and V. Kumar (2004)

Objectives Analyze the usefulness of Customer Lifetime Value (CLV) as a metric for customer selection and resource allocation strategy. Developed and estimated an customer-level objective function, the goal of which is to measure CLV. Compared the customer selection by CLV compared with other commonly used metrics. Evaluated the benefits of designing marketing communications by maximizing CLV.

Framework for Measuring and Using CLV

CLV Objective Function CLVi- lifetime value of customer i. CMi,y = predicted contribution margin from customer i in purchase occasion y. Frequencyi= predicted purchase frequency for customer i. Ci,m,l = unit marketing cost for customer i in channel m in year l. xi,m,l = number of contacts to customer i in channel m in year l. r = discount rate. n= forecast horizon Ti = predicted number of purchases made by customer i until the end of the planning period

Purchase Frequency Model Antecedents of Frequency Expected Effect Upgrading + Cross-buying Bidirectional communication Returns Frequency of Web Based Contacts Relationship Benefits Frequency of rich communication modes Inverted U Frequency of standardized communication modes Intercontact Time Product Category  

Contribution Margin Model Antecedents of CM Expected Effect Lagged Contribution Margin + Total Marketing Efforts Total quantity purchased Size of the firm Industry Category  

Data and Estimation Data from large multinational software and hardware manufacturer. Cohort 1: first purchase occurs in 1997 (1316 observations). Cohort 2: first purchase occurs in 1998 (872 observations). Data till 2000 used for estimation and data for 2001 used as a holdout sample. Frequency model estimated using Markov chain Monte Carlo (MCMC) method. Contribution margin model estimated using panel data regression Endogeneity controlled for by using lagged variables.

Results Frequency Model CM Model

Customer Selection Strategy

Resource Allocation Strategy Genetic algorithm used to optimize objective function. Identified level of channel contacts which would maximize CLV depending on cost and responsiveness of customers. Estimated increase in profits by $20 million among 216 customers. Overall increase of $1billion across entire customer pool.

Conclusions Marketing communication across various channels affects CLV nonlinearly; CLV performs better than other commonly used customer-based metrics for customer selection such as PCR, PCV, and CLD; Managers can improve profits by designing marketing communications that maximize CLV.

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