One-to-one modeling and simulation: a new approach in customer relationship management for grocery retail

The ever-increasing competition in retail industry puts pressure on retailers to deal with their customers more efficiently. Currently most companies use Customer Relationship Management (CRM) systems to maximize the customer satisfaction level by trying to understand more about their behaviors. However, one disadvantage of the current approaches is that they focus on the segmentation of customers into homogenous groups and they disregard examining the one-to-one relationship of each individual’s behavior toward each product. Therefore, individual behavior cannot be captured in detail. Modeling individual behavior for each product enables several strategies of pricing by keeping the customer satisfaction at the maximum level. One example is offering a personal discount on a particular item to a customer who is price sensitive to that particular product. Therefore, you can still sell other products at the nondiscounted level to this customer by keeping him satisfied. In this paper, individual pricing approach is discussed. The aim of this study is to develop a conceptual framework to analyze the feasibility of individual pricing. Customer behaviors can be modeled individually with respect to each product for a grocery store. Several factors can be used to determine these behaviors such as customer’s need, brand loyalty and price sensitivity. Each customer can be modeled as an adaptive agent using qualitative descriptions of behaviors (i.e., highly price sensitive). Then, the overall shopping behavior can be simulated using a multi-agent Monte-Carlo simulation. It is expected that with this approach, retailers will be able to determine better strategies to obtain more profits, better sales and better customer satisfaction.