Pricing personalized bundles: A new approach and an empirical study

Zhengliang Xue, Zizhuo Wang, Markus Ettl

Research output: Contribution to journalArticlepeer-review

25 Scopus citations

Abstract

This paper studies the pricing strategies for personalized product bundles. In such problems, a seller provides a variety of products for which customers can construct a personalized bundle and send a request for quote (RFQ) to the seller. The seller, after reviewing the RFQ, has to determine a price based on which the customer either purchases the whole bundle or nothing. Such problems are faced by many companies in practice, and they are very difficult because of the potential unlimited possible configurations of the bundle and the correlations among the individual products. In this paper, we propose a novel top-down and bottom-up approach to solve this problem. In the top-down step, we decompose the bundle into each component and calibrate a value score for each component. In the bottom-up step, we aggregate the components back to the bundle, define important features of the bundle, and segment different RFQs by those bundle features as well as customer attributes. Then we estimate a utility function for each segment based on historical sales data and derive an optimal price for each incoming RFQ. We show that such a model overcomes the aforementioned difficulties and can be implemented efficiently. We test our approach using empirical data from a major information technology service provider and the test result shows that the proposed approach can improve the effectiveness of pricing significantly.

Original languageEnglish (US)
Pages (from-to)51-68
Number of pages18
JournalManufacturing and Service Operations Management
Volume18
Issue number1
DOIs
StatePublished - Dec 1 2016

Bibliographical note

Publisher Copyright:
© 2016 INFORMS.

Keywords

  • Data analysis
  • Personalized bundle
  • Pricing
  • Utility model

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