Using Rfm Data to Optimize Direct Marketing Campaigns: A Linear Programming Approach

Article excerpt

ABSTRACT

The direct marketing framework that incorporates the recency, frequency, and monetary value (RFM) of customers' previous purchases is a useful analytical tool for companies that want to fine-tune their market segmentation strategies, design more effective database programs, improve customer relationship management, and allocate marketing resources more efficiently. The current research offers an optimization model that helps determine whether a company should continue or curtail its marketing spending on select customer segments given various budget constraints. The proposed linear programming model identifies the customer segments (based on RFM profile) that should be targeted in order to maximize profitability. At the same time, the method helps identify those RFM segments which are not worthy of pursuing either due to unprofitability or due to an insufficient campaign budget. The model is illustrated with a numerical example.

Keywords: RFM; direct marketing; linear programming; customer lifetime value

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INTRODUCTION

An organization's long-term viability requires a focus on the profitability of each customer within that organization (Forbes, 2007). Customer lifetime value (CLV), the net present value of cash flows expected during a customer's tenure with a firm, can therefore be a valuable marketing metric to evaluate (Blattberg, Malthouse, & Neslin, 2009; Pfeifer & Carraway, 2000; Venkatesan, Kumar, & Bohling 2007). CLV is also often used as the basis of customer relationship management (CRM) decisions, including service level delivery (Zeithaml, Bitner, & Gremler, 2009; Zeithaml, Rust, & Lemon, 2001; Jackson, 2007). For example, customer profitability might be used to determine whether a service policy exception is made for a key account or whether a credit card customer's credit limit or interest rates are increased (Aeron, Bhaskar, Sundararajan, Kumar, & Moorthy, 2008). At its core, CLV guides a firm's acquisition and retention strategies (Blattberg et al., 2009).

Estimating CLV accurately can be difficult, however, and is sometimes beyond the ability of many firms (Stahl, Matzler, & Hinterhuber, 2003; Vogel, Evanschitzky, & Ramaseshan, 2008). Even the predictions of the winning model from a recent CLV modeling competition were inaccurate by more than 500 percent, for example (Blattberg et al., 2009). An approach is needed, therefore, that allows fairly simple prediction of a customer's long-term profitability potential while simultaneously providing marketers with effective CRM decision input.

One variant of the CLV estimation models is the RFM framework used in direct marketing in which the probability of customers' future purchases is based on the recency (R), frequency (F), and monetary value (M) of their previous transactions. These RFM probabilities are then used to categorize customers according to their profit potential. Customers with the highest profit potential would then be the possible targets of a company's direct marketing campaign. The RFM approach offers a potential solution to the problems associated with predicting CLV and gives direct marketers input on customer profitability and relationship management issues.

The research presented here offers a linear programming (LP) approach that combines data provided by RFM analysis alongside budgeting data for a given campaign. The model can help direct marketers determine whether to continue or curtail their relationship with a given RFM customer segment. A novel characteristic of this model is the budget constraints. Theoretically, when a company has an unlimited marketing budget, managers can afford to reach all their customers, even those who have low RFM scores. This approach would minimize Type I error, which occurs when a company does not contact a customer who could have potentially provided additional revenue and profits. …