A Data-Driven Approach to Personalized Bundle Pricing and Recommendation

Manufacturing & Service Operations Management (M&SOM Journal), Forthcoming

41 Pages Posted: 14 Sep 2018

See all articles by Markus Ettl

Markus Ettl

IBM Research

Pavithra Harsha

IBM Research

Anna Papush

Massachusetts Institute of Technology (MIT), Operations Research Center

Georgia Perakis

Massachusetts Institute of Technology (MIT) - Sloan School of Management

Date Written: August 30, 2018

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. 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, in order to produce a real-time output for use in an online setting.

Academic and 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. 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 to 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 are dependent on product category and they are a result of providing relevant recommendations.

Keywords: pricing and revenue management, retailing, OM Practice, inventory theory and control, dynamic programming

Suggested Citation

Ettl, Markus and Harsha, Pavithra and Papush, Anna and Perakis, Georgia, A Data-Driven Approach to Personalized Bundle Pricing and Recommendation (August 30, 2018). Manufacturing & Service Operations Management (M&SOM Journal), Forthcoming . Available at SSRN: https://ssrn.com/abstract=3241517

Markus Ettl

IBM Research ( email )

T. J. Watson Research Center
1 New Orchard Road
Armonk, NY 10504-1722
United States

Pavithra Harsha

IBM Research ( email )

T. J. Watson Research Center
Yorktown Heights, NY 10598
United States

Anna Papush (Contact Author)

Massachusetts Institute of Technology (MIT), Operations Research Center ( email )

77 Massachusetts Avenue
Bldg. E 40-149
Cambridge, MA 02139
United States

Georgia Perakis

Massachusetts Institute of Technology (MIT) - Sloan School of Management ( email )

100 Main Street
E62-565
Cambridge, MA 02142
United States

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