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Personalized dynamic pricing with machine learning: High-dimensional features and heterogeneous elasticity

Publication ,  Journal Article
Ban, GY; Keskin, NB
Published in: Management Science
September 1, 2021

We consider a seller who can dynamically adjust the price of a product at the individual customer level, by utilizing information about customers’ characteristics encoded as a d-dimensional feature vector. We assume a personalized demand model, parameters of which depend on s out of the d features. The seller initially does not know the relationship between the customer features and the product demand but learns this through sales observations over a selling horizon of T periods. We prove that the seller’s expected regret, that is, the revenue loss against a clairvoyant who knows the underlying demand relationship, is at least of order s√- T under any admissible policy. We then design a near-optimal pricing policy for a semiclairvoyant seller (who knows which s of the d features are in the demand model) who achieves an expected regret of order s√- T log T. We extend this policy to a more realistic setting, where the seller does not know the true demand predictors, and show that this policy has an expected regret of order s√- T (log d + log T), which is also near-optimal. Finally, we test our theory on simulated data and on a data set from an online auto loan company in the United States. On both data sets, our experimentation-based pricing policy is superior to intuitive and/or widely-practiced customized pricing methods, such as myopic pricing and segment-then-optimize policies. Furthermore, our policy improves upon the loan company’s historical pricing decisions by 47% in expected revenue over a six-month period.

Duke Scholars

Published In

Management Science

DOI

EISSN

1526-5501

ISSN

0025-1909

Publication Date

September 1, 2021

Volume

67

Issue

9

Start / End Page

5549 / 5568

Related Subject Headings

  • Operations Research
  • 46 Information and computing sciences
  • 38 Economics
  • 35 Commerce, management, tourism and services
  • 15 Commerce, Management, Tourism and Services
  • 08 Information and Computing Sciences
 

Citation

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Ban, G. Y., & Keskin, N. B. (2021). Personalized dynamic pricing with machine learning: High-dimensional features and heterogeneous elasticity. Management Science, 67(9), 5549–5568. https://doi.org/10.1287/mnsc.2020.3680
Ban, G. Y., and N. B. Keskin. “Personalized dynamic pricing with machine learning: High-dimensional features and heterogeneous elasticity.” Management Science 67, no. 9 (September 1, 2021): 5549–68. https://doi.org/10.1287/mnsc.2020.3680.
Ban, G. Y., and N. B. Keskin. “Personalized dynamic pricing with machine learning: High-dimensional features and heterogeneous elasticity.” Management Science, vol. 67, no. 9, Sept. 2021, pp. 5549–68. Scopus, doi:10.1287/mnsc.2020.3680.

Published In

Management Science

DOI

EISSN

1526-5501

ISSN

0025-1909

Publication Date

September 1, 2021

Volume

67

Issue

9

Start / End Page

5549 / 5568

Related Subject Headings

  • Operations Research
  • 46 Information and computing sciences
  • 38 Economics
  • 35 Commerce, management, tourism and services
  • 15 Commerce, Management, Tourism and Services
  • 08 Information and Computing Sciences