استفاده از برنامه ریزی آرمانی برای افزایش کارایی کمپین های بازاریابی Using Goal Programming To Increase The Efficiency Of Marketing Campaigns

INTRODUCTION Due to the scarcity of resources, companies commonly face the problem of prioritizing the marketing activities in which the firm will invest and determining the levels of funding for those activities. Selecting the best set of marketing activities is not easy as there are numerous factors that must be accounted for. Organizations must select the most viable marketing activities to maximize the outcomes (e.g. improve customer relationships), and minimize any negative results (e.g. high costs). This requires identifying the most costbeneficial marketing campaigns. In order to effectively target marketing activities, it is assumed that different groups of customers want different kinds of services and products, and as a result market segmentation techniques and customer segmentation are widely used. One option in attempting to select the most effective marketing campaign is the RFM (Recency-FrequencyMonetary) approach. “Recency,” as defined by Fader, Hardie, and Lee (2005), is the time of a customer’s most recent purchase, while “frequency” is the number of past purchases. The literature offers varying definitions of “monetary value” (Fader et al., 2005; Blattberg, Malthouse, and Neslin, 2009; Rhee & McIntyre, 2009). These definitions include average spending per transaction (essentially equivalent to M/F), and the total amount spent by a customer on all purchases over a specified time period. The RFM framework allows for more effective marketing campaigns by categorizing customers into homogenous segments that allow for the design of promotion campaigns that are customized for the particular segment at issue. In this approach, values for R, F, and M are assigned to each customer and are then used to categorize customers to help determine the most effective types of promotions for that specific customer. For example, if a given customer segment shows a low value for recency and relatively high values for frequency and monetary, then this group of customers is typically approached with a “we want you back” marketing strategy. If a given customer segment shows a low value for monetary and high values for frequency and recency, then this group of customers is approached with a “cross selling” marketing strategy. One drawback is that the RFM approach assumes an unlimited marketing budget and complete access to all the organizations’ customers, even those who have low RFM scores; however, these assumptions are not realistic because organizations tend to operate under annual marketing budget constraints. In addition, the importance of the R, F, and M components in the RFM approach might not be the same. For example, a company might be mostly interested in the R component, making it a priority to bring back those customers who have taken their business to competitors and thereby placing frequency and monetary values as the second and third priorities, respectively. To properly address and account for budget constraints and marketing priorities, managers must gear their promotional spending strategies toward customers who will yield the greatest growth in cash flows and profits within the given constraints. Kotler and Armstrong (1996) define a profitable customer as “a person, household, or company whose revenues over time exceed, by an acceptable amount, the company costs of attracting, selling, and servicing that customer.” This excess is called customer lifetime value (CLV). CLV is the sum of cumulated cash flows, discounted using the weighted average cost of capital, of a customer over his/her entire lifetime with the company (Kumar, Ramani, & Bohling, 2004). In the context of customer relationship management, CLV becomes important because it is a metric to evaluate marketing decisions (Blattberg & Deighton, 1996). CLV provides a tool for firms to apply different types of marketing instruments toward different customers based upon their expected values, which may result in better return on the firm’s marketing investment. In marketing campaigns, managers also strive to find a balance between two types of errors: ignoring those customers who could have returned to create more revenue for the organization, and investing on those customers who are not yet ready to purchase. Venkatesan and Kumar (2004) refer to these errors as Type I and Type II errors. It is therefore important for marketing campaign decision-makers to understand the importance of these two error types and to adjust their decisions accordingly. In order to create effective marketing campaigns, companies use a vast amount of electronic records generated by online and offline purchases and use data analytics to design effective marketing campaigns and introduce personalized promotions for their customers. As a result, for any effort in determining the most effective campaign strategy, there will be a need for analytical tools that can help the decision makers in choosing the optimal strategy. This paper presents a goal programming model that balances Type I and Type II errors by identifying the RFM segments that should be reached and the RFM segments that are not worthy of pursuit because they lack profitability, do not follow priorities, or exceed marketing budget constraints. The model can help marketers determine whether to follow or cut back on their relationship with a given customer segment. A novel characteristic of this model is the inclusion of campaign priorities and budget constraints to determine which segment of customers should be deemed the optimal targets of a direct marketing campaign.