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

By Asllani, Arben; Halstead, Diane | Academy of Marketing Studies Journal, October 2011 | Go to article overview

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


Asllani, Arben, Halstead, Diane, Academy of Marketing Studies Journal


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. Such a strategy is clearly not practical, however, because organizations typically operate under annual marketing budget constraints.

Such a strategy maximizes Type II error as well. A Type II error occurs when the company reaches a customer who is not yet ready to purchase (Venkatesan & Kumar, 2004). The LP model proposed here establishes a balance between these two errors by identifying both those RFM segments that should be reached and those RFM segments which are not worthy of pursuing because they are not profitable or because of marketing budget constraints. When faced with budget limitations, marketing managers are forced to prioritize their promotional spending strategies toward customers who will provide the highest growth in cash flows and profits. A contribution of this research is that RFM data is incorporated into an LP approach into a single model for all customers who are potential targets of a direct marketing campaign.

The paper is organized as follows. First, a brief overview of CLV and RFM models is provided. The next section discusses the modeling framework and presents the mathematical formulation of customer relationships.

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