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Using survival prediction techniques to learn consumer-specific reservation price distributions

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Listed:
  • Ping Jin
  • Humza Haider
  • Russell Greiner
  • Sarah Wei
  • Gerald Häubl

Abstract

A consumer’s “reservation price” (RP) is the highest price that s/he is willing to pay for one unit of a specified product or service. It is an essential concept in many applications, including personalized pricing, auction and negotiation. While consumers will not volunteer their RPs, we may be able to predict these values, based on each consumer’s specific information, using a model learned from earlier consumer transactions. Here, we view each such (non)transaction as a censored observation, which motivates us to use techniques from survival analysis/prediction, to produce models that can generate a consumer-specific RP distribution, based on features of each new consumer. To validate this framework of RP, we run experiments on realistic data, with four survival prediction methods. These models performed very well (under three different criteria) on the task of estimating consumer-specific RP distributions, which shows that our RP framework can be effective.

Suggested Citation

  • Ping Jin & Humza Haider & Russell Greiner & Sarah Wei & Gerald Häubl, 2021. "Using survival prediction techniques to learn consumer-specific reservation price distributions," PLOS ONE, Public Library of Science, vol. 16(4), pages 1-26, April.
    • Handle: RePEc:plo:pone00:0249182
    DOI: 10.1371/journal.pone.0249182
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    References listed on IDEAS

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    1. Greg Shaffer & Z. John Zhang, 2002. "Competitive One-to-One Promotions," Management Science, INFORMS, vol. 48(9), pages 1143-1160, September.
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