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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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    1. Vineet Goyal & Retsef Levi & Danny Segev, 2016. "Near-Optimal Algorithms for the Assortment Planning Problem Under Dynamic Substitution and Stochastic Demand," Operations Research, INFORMS, vol. 64(1), pages 219-235, February.
    2. Fernando Bernstein & A. Gürhan Kök & Lei Xie, 2015. "Dynamic Assortment Customization with Limited Inventories," Manufacturing & Service Operations Management, INFORMS, vol. 17(4), pages 538-553, October.
    3. Garrett van Ryzin & Siddharth Mahajan, 1999. "On the Relationship Between Inventory Costs and Variety Benefits in Retail Assortments," Management Science, INFORMS, vol. 45(11), pages 1496-1509, November.
    4. Paat Rusmevichientong & David Shmoys & Chaoxu Tong & Huseyin Topaloglu, 2014. "Assortment Optimization under the Multinomial Logit Model with Random Choice Parameters," Production and Operations Management, Production and Operations Management Society, vol. 23(11), pages 2023-2039, November.
    5. Negin Golrezaei & Hamid Nazerzadeh & Paat Rusmevichientong, 2014. "Real-Time Optimization of Personalized Assortments," Management Science, INFORMS, vol. 60(6), pages 1532-1551, June.
    6. Huseyin Topaloglu, 2009. "Using Lagrangian Relaxation to Compute Capacity-Dependent Bid Prices in Network Revenue Management," Operations Research, INFORMS, vol. 57(3), pages 637-649, June.
    7. Paat Rusmevichientong & Huseyin Topaloglu, 2012. "Robust Assortment Optimization in Revenue Management Under the Multinomial Logit Choice Model," Operations Research, INFORMS, vol. 60(4), pages 865-882, August.
    8. Gabriel Bitran & René Caldentey, 2003. "An Overview of Pricing Models for Revenue Management," Manufacturing & Service Operations Management, INFORMS, vol. 5(3), pages 203-229, August.
    9. Siddharth Mahajan & Garrett van Ryzin, 2001. "Stocking Retail Assortments Under Dynamic Consumer Substitution," Operations Research, INFORMS, vol. 49(3), pages 334-351, June.
    10. Guang Li & Paat Rusmevichientong & Huseyin Topaloglu, 2015. "The d -Level Nested Logit Model: Assortment and Price Optimization Problems," Operations Research, INFORMS, vol. 63(2), pages 325-342, April.
    11. Guillermo Gallego & Huseyin Topaloglu, 2014. "Constrained Assortment Optimization for the Nested Logit Model," Management Science, INFORMS, vol. 60(10), pages 2583-2601, October.
    12. Shipra Agrawal & Zizhuo Wang & Yinyu Ye, 2014. "A Dynamic Near-Optimal Algorithm for Online Linear Programming," Operations Research, INFORMS, vol. 62(4), pages 876-890, August.
    13. Serguei Netessine & Sergei Savin & Wenqiang Xiao, 2006. "Revenue Management Through Dynamic Cross Selling in E-Commerce Retailing," Operations Research, INFORMS, vol. 54(5), pages 893-913, October.
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    2. 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.
    3. 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.
    4. 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.
    5. Long He & Sheng Liu & Zuo‐Jun Max Shen, 2022. "Smart urban transport and logistics: A business analytics perspective," Production and Operations Management, Production and Operations Management Society, vol. 31(10), pages 3771-3787, October.
    6. 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.
    7. 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.

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