Agent-based modeling and simulation of store performance for personalized pricing

In this paper, a simulation-based approach of optimizing a grocery store's performance is discussed. Currently, most of the grocery stores provide special discounts to their customers under different loyalty card programs. We believe that a more determined approach such as personalized pricing could enable retailers to optimize their store performance. The objective of this paper is to determine the feasibility of personalized pricing to optimize store performance and compare it with the traditional product-centered approach. Each customer is modeled as an agent and his/her shopping behavior is obtained from transaction data using factors such as customer's product consumption rate, brand loyalty and price sensitivity. Then, the overall shopping behavior is simulated and the store performance is optimized. The results showed that personalized pricing outperforms the traditional product-centered approach significantly. It is expected that successful implementation of this work will impact grocery retail significantly by increasing the customer satisfaction and profits.