Loss-Leader Pricing Strategies for Personalized Bundles under Customer Choice

dc.contributor.authorXue, Zhengliang
dc.contributor.authorSubramanian, Shiva
dc.contributor.authorEttl, Markus
dc.date.accessioned2023-12-26T18:37:17Z
dc.date.available2023-12-26T18:37:17Z
dc.date.issued2024-01-03
dc.identifier.doi10.24251/HICSS.2024.192
dc.identifier.isbn978-0-9981331-7-1
dc.identifier.other844f7281-9517-4626-bb53-a1de381cef02
dc.identifier.urihttps://hdl.handle.net/10125/106569
dc.language.isoeng
dc.relation.ispartofProceedings of the 57th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectService Analytics
dc.subjectbundling
dc.subjectcounterfactual
dc.subjectdata sparsity
dc.subjectpricing
dc.subjectwin probability
dc.titleLoss-Leader Pricing Strategies for Personalized Bundles under Customer Choice
dc.typeConference Paper
dc.type.dcmiText
dcterms.abstractThis 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.
dcterms.extent10 pages
prism.startingpage1518

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