Location
Hilton Hawaiian Village, Honolulu, Hawaii
Event Website
https://hicss.hawaii.edu/
Start Date
3-1-2024 12:00 AM
End Date
6-1-2024 12:00 AM
Description
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 a bundle configuration at the seller’s approved prices. In addition to a 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.
Recommended Citation
Xue, Zhengliang; Subramanian, Shiva; and Ettl, Markus, "Loss-Leader Pricing Strategies for Personalized Bundles under Customer Choice" (2024). Hawaii International Conference on System Sciences 2024 (HICSS-57). 5.
https://aisel.aisnet.org/hicss-57/da/service_analytics/5
Loss-Leader Pricing Strategies for Personalized Bundles under Customer Choice
Hilton Hawaiian Village, Honolulu, Hawaii
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 a bundle configuration at the seller’s approved prices. In addition to a 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.
https://aisel.aisnet.org/hicss-57/da/service_analytics/5