Loss-Leader Pricing Strategies for Personalized Bundles Under Customer Choice

Abstract

This paper considers the pricing of multi-product request-for-quotes (RFQs) that are configured by a buyer based on a large number of products or services offered in a seller's product catalog. The buyer submits an RFQ for a desired bundle of line items in a bid configuration to a seller. The seller reviews the configuration and offers an approved price for each line item in the bundle. The buyer can selectively purchase any combination of products or services in the bid configuration at the seller's approved prices. In addition to the line item pricing approach, we propose a novel loss-leader model that uses machine learning to calibrate the buyer's preferences among correlated line items, and dynamically optimizes the prices of any configuration to maximize the seller's expected profit. The pricing strategies were implemented in a business-to-business (B2B) sales environment with a multinational technology company. Counterfactual analysis shows that loss-leader pricing can generate more than ten percent lift in gross profit over existing pricing practices.