Online Category Pricing at a Multichannel Grocery Retailer

As the importance of online grocery shopping increases, both pure-play online grocers and multichannel grocers are likely to be interested in setting optimal prices for their online stores. We use a unique household scanner panel dataset to investigate how a grocery retailer with both online and offline stores can set prices for its online store. We observe the same households shopping interchangeably at the online and offline stores of the same grocery chain and investigate their purchase behavior in specific product categories. We find that across three product categories, these households exhibit lower price sensitivities when shopping online than offline. In addition, a household's price sensitivity is inversely related to distance to the closest physical store. We compute the retailer's category profit-maximizing prices for the online store and explore several alternative pricing schemes that are consistent with the retailer's current price image. We find that the retailer can substantially increase its profits from online operations by fine-tuning its current pricing policy. Given the estimated inverse relationship between price sensitivity and store distance, we investigate online zone pricing as a price discrimination tool based on residential location and find that the retailer can further improve store profits. We examine the potential mechanism to assign zones and determine the optimal number of pricing zones