In this chapter, we discuss how to optimize a grocery store’s performance using personalized pricing and evolutionary computation. Currently most of the grocery stores provide special discounts to their customers under different loyalty card programs. However, since each individual’s shopping behavior is not taken into consideration, these discounts do not help optimize the store performance. We believe that a more determined approach such as individual pricing could enable retailers to optimize their store performance by giving special discounts to each customer. The objective here is to determine the feasibility of individual pricing to optimize the store performance and compare it against the traditional product-centered approach. Each customer is modeled as an agent and his/her shopping behavior is obtained from transaction data. Then, the overall shopping behavior is simulated and the store performance is optimized using Monte-Carlo simulations and evolutionary computation. The results showed that individual pricing outperforms the traditional product-centered approach significantly. We believe that the successful implementation of the proposed research will impact the grocery retail significantly by increasing customer satisfaction and profits.
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Baydar, C. (2008). Optimization of Store Performance Using Personalized Pricing. In: Yu, T., Davis, L., Baydar, C., Roy, R. (eds) Evolutionary Computation in Practice. Studies in Computational Intelligence, vol 88. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75771-9_7
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DOI: https://doi.org/10.1007/978-3-540-75771-9_7
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