Department of Industrial Engineering & Decision Analysis [IEDA Seminar] - Segmenting Consumer Location-Product Preferences for Assortment Localization

10:30am - 11:30am

When managing multiple stores in the same marketplace, retailers need to select store locations and localize product assortments to reflect the heterogeneous demand preferences across communities. This paper develops a dual Poisson Dynamic System with Multilayer Factorization (dPDS-MF) for panel data on product assortments and individual consumers’ purchases across store/vending locations. The dPDS-MF can help retailers automatically profile different consumer segments driven by store visiting preferences, measure the relationships across store locations, and estimate the product preferences for each consumer segment simultaneously. The dPDS-MF relies on a Bayesian nonparametric prior and can be efficiently trained for large-scale transactional data across hundreds of stores and SKUs, using our proposed MCMC inference algorithm. We apply the dPDS-MF in the retail vending market in major train stations in Japan. We demonstrate the face validity of the direct outputs from the dPDS-MF for improving vending location decisions as well as location-specific assortments. More importantly, we showcase how the dPDS-MF can be combined with a choice model to solve the optimal localized assortments efficiently and effectively. We show that compared with several benchmark strategies, including the nested-logit choice model, our proposed assortment strategy not only improves the expected revenue up-to 30% but also gives more meaningful localized assortment decisions.


Keywords: product assortments, consumer segmentation, topic modeling, choice modeling, optimization, big data, decision analytics

Event Format
Speakers / Performers:
Prof. Jia LIU
Department of Marketing; Department of Industrial Engineering and Decision Analytics; The Hong Kong University of Science & Technology

Jia Liu is an Associate Professor of Marketing and an (Affiliated) Associate Professor of Industrial Engineering and Decision Analytics (IEDA) at Hong Kong University of Science and Technology (HKUST). Her research interests have covered a broad range of areas, including consumer search, advertising, pricing, recommender system, user generated content, social network, big data analytics, product assortment, operation management, and AI. Jia has an extensive experience in working with various companies in different sectors. Her research has been published in several leading journals (e.g., Marketing Science, Journal of Marketing Research, Management Science, and Quantitative Marketing & Economics). Her research has won 2018 John Little award for best marketing paper published in Marketing Science or Management Science. She is the recipient of the 2023 MSI Young Scholar. Jia is on the editorial review board of both Marketing Science (since 2022) and the Journal of Marketing Research (since 2019). Prior joining HKUST in 2018, Jia was a Postdoc researcher at Microsoft Research (NYC). She holds a Ph.D. in Marketing from Columbia University, a M.S. in Statistics, and a B.S. in Mathematics.

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