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A Data-Driven Approach to Personalized Bundle Pricing and Recommendation

Author

Listed:
  • Markus Ettl

    (TJ Watson Research Center, Yorktown Heights, New York 10598)

  • Pavithra Harsha

    (TJ Watson Research Center, Yorktown Heights, New York 10598)

  • Anna Papush

    (Massachusetts Institute of Technology, Cambridge, Massachusetts 02139)

  • Georgia Perakis

    (Massachusetts Institute of Technology, Cambridge, Massachusetts 02139)

Abstract

Problem definition : The growing trend in online shopping has sparked the development of increasingly more sophisticated product recommendation systems. We construct a model that recommends a personalized discounted product bundle to an online shopper that considers the trade-off between profit maximization and inventory management, while selecting products that are relevant to the consumer’s preferences. Academic/practical relevance : We provide analytical performance guarantees that illustrate the complexity of the underlying problem, which combines assortment optimization with pricing. We implement our algorithms in two separate case studies on actual data from a large U.S. e-tailer and a premier global airline. Methodology : We focus on simultaneously balancing personalization through individualized functions of consumer propensity-to-buy, inventory management for long-run profitability, and tractability for practical business implementation. We develop two classes of approximation algorithms, multiplicative and additive, to produce a real-time output for use in an online setting. Results : Our computational results demonstrate significant lifts in expected revenues over current industry pricing strategies on the order of 2%–7% depending on the setting. We find that on average our best algorithm obtains 92% of the expected revenue of a full-knowledge clairvoyant strategy across all inventory settings, and in the best cases this improves to 98%. Managerial implications : We compare the algorithms and find that the multiplicative approach is relatively easier to implement and on average empirically obtains expected revenues within 1%–6% of the additive methods when both are compared with a full-knowledge strategy. Furthermore, we find that the greatest expected gains in revenue come from high-end consumers with lower price sensitivities, and that predicted improvements in sales volume depend on product category and are a result of providing relevant recommendations.

Suggested Citation

  • Markus Ettl & Pavithra Harsha & Anna Papush & Georgia Perakis, 2020. "A Data-Driven Approach to Personalized Bundle Pricing and Recommendation," Manufacturing & Service Operations Management, INFORMS, vol. 22(3), pages 461-480, May.
    • Handle: RePEc:inm:ormsom:v:22:y:2020:i:3:p:461-480
    DOI: 10.1287/msom.2018.0756
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    References listed on IDEAS

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    2. Xijia Liu & Xiaoshu Liu & Anna Shi & Chenwei Li, 2023. "Agricultural Products’ Bundled Pricing Based on Consumers’ Organic Preferences," Sustainability, MDPI, vol. 15(17), pages 1-14, September.
    3. Debjit Roy & Eirini Spiliotopoulou & Jelle de Vries, 2022. "Restaurant analytics: Emerging practice and research opportunities," Production and Operations Management, Production and Operations Management Society, vol. 31(10), pages 3687-3709, October.
    4. Kevin K. Wang & Michael D. Wittman & Thomas Fiig, 2023. "Dynamic offer creation for airline ancillaries using a Markov chain choice model," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 22(2), pages 103-121, April.
    5. Theja Tulabandhula & Deeksha Sinha & Saketh Reddy Karra & Prasoon Patidar, 2020. "Multi-Purchase Behavior: Modeling, Estimation and Optimization," Papers 2006.08055, arXiv.org, revised Aug 2023.
    6. Xia, Lan & Bechwati, Nada Nasr, 2021. "Maximizing what? The effect of maximizing mindset on the evaluation of product bundles," Journal of Business Research, Elsevier, vol. 128(C), pages 314-325.
    7. Tao, Jiawei & Dai, Hongyan & Chen, Weiwei & Jiang, Hai, 2023. "The value of personalized dispatch in O2O on-demand delivery services," European Journal of Operational Research, Elsevier, vol. 304(3), pages 1022-1035.

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