Price customization using price thresholds estimated from scanner panel data

This study explores a customized pricing strategy based on heterogeneous price thresholds estimated from scanner panel data and shows that a customized pricing strategy could be more efficient than a flat pricing strategy. We apply a heterogeneous brand choice model with price thresholds to the price customization problem and show that heterogeneous price thresholds provide valuable information for customizing prices to individual consumers. The expected incremental profits from customized discounting as well as customized price hike are evaluated using hierarchical Bayes modeling with the Markov chain Monte Carlo method.