Strategic retail marketing using DGP-based models

Abstract

Most retail businesses operate in a non-contractual settings, and this relationship with the customers poses difficulties in differentiating between the customers who have attrited voluntarily and those who are in the middle of their long cycle transaction behavior. Therefore, formulating an effective CRM strategy in retail poses a significant challenge. This chapter proposes a DGP (data generating process)- based predictive strategy with the past purchase transaction data, which would help the business to improve the overall marketing performance with minimum data requirement. In contrast to many existing RFM (recency, frequency, and monetary value)-based models, a set of model with strong underlying behavioral model is proposed, thereby providing a greater insight into the customer decisions. The approach basically predicts a customer's future purchase money value by combining the three key transaction factors, viz. recency, frequency, and monetary value which is further combined into a more powerful single predicted value (PRFM) for each customer. This represents an original contribution as many retailers are making decisions with RFM, but these are anchored on a static metric based on looking at past behavior and are not predictive in nature. Furthermore, when they do try to render RFM predictive, the methods are often ad hoc, and therefore, they are usually difficult to implement in practice. The final and most important characteristic of this model is the extensibility. Though the models depend only on three key customer attributes, R, F, and M, it can be easily extended to incorporate other customer attributes of interest by running the algorithm for each subsegment. Suggestions for future research include the adaptation of these techniques to all types of general-purpose revolving credit cards which are being issued by most banks and consumer finance companies.