Dynamic Price Personalization: Predicting the Optimum Price to Maximize Revenue

In an advanced digital society, retailers have been personalizing their product proposals and advertisements in order to capture consumer needs at the right timings. However, they have not yet conducted personalization in price, which is also an important element in marketing. The purpose of the research is to design a personalization framework of the optimum pricing. We developed a joint model of store visit and product purchase for all customers in a nested logit model with hierarchical Bayesian framework, then the individual optimum prices are dynamically back calculated with results obtained from the nested logit model. With scanner panel data of retail stores during 2019/1/2-12/31 in Tokyo, the optimum price for a ketchup product was estimated for 569 customers simultaneously using the Markov Chain Monte Carlo (MCMC) Metropolis-Hastings algorithm. We also demonstrated a general dynamic pricing model and price density on a daily basis as a bottom-up approach of combining the optimum prices from all customers. Personal pricing simulation results indicated to increase approximately 8.6% revenue than that from the actual pricing. The personal optimum price prediction proposed in this research is an example of resource-based process and introduced as one of most efficient utilization of Big Data in marketing.