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Real-Time Optimization of Personalized Assortments

Author

Listed:
  • Negin Golrezaei

    (Marshall School of Business, University of Southern California, Los Angeles, California 90089)

  • Hamid Nazerzadeh

    (Marshall School of Business, University of Southern California, Los Angeles, California 90089)

  • Paat Rusmevichientong

    (Marshall School of Business, University of Southern California, Los Angeles, California 90089)

Abstract

Motivated by the availability of real-time data on customer characteristics, we consider the problem of personalizing the assortment of products for each arriving customer. Using actual sales data from an online retailer, we demonstrate that personalization based on each customer's location can lead to over 10% improvements in revenue compared to a policy that treats all customers the same. We propose a family of index-based policies that effectively coordinate the real-time assortment decisions with the back-end supply chain constraints. We allow the demand process to be arbitrary and prove that our algorithms achieve an optimal competitive ratio. In addition, we show that our algorithms perform even better if the demand is known to be stationary. Our approach is also flexible and can be combined with existing methods in the literature, resulting in a hybrid algorithm that brings out the advantages of other methods while maintaining the worst-case performance guarantees. This paper was accepted by Dimitris Bertsimas, special issue on business analytics .

Suggested Citation

  • Negin Golrezaei & Hamid Nazerzadeh & Paat Rusmevichientong, 2014. "Real-Time Optimization of Personalized Assortments," Management Science, INFORMS, vol. 60(6), pages 1532-1551, June.
    • Handle: RePEc:inm:ormnsc:v:60:y:2014:i:6:p:1532-1551
    DOI: 10.1287/mnsc.2014.1939
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    References listed on IDEAS

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