Selling personalized upgraded substitutes and co-purchases in online grocery retail

This is a talk given by Prof Gah-Yi Ban as part of the Online Seminar Series Machine Learning NeEDS Mathematical Optimization, https://congreso.us.es/mlneedsmo/

Abstract

In this paper, we investigate how an online grocery retailer can make personalized recommendations for upgraded substitutes and co-purchases as a customer adds items to their shopping basket. Of particular interest is balancing revenue maximization with long-term customer loyalty. We explore three different objectives to model this problem: (i) pure expected revenue maximization, (ii) maximization of a weighted average of the expected revenue and consumer surplus, and (iii) expected revenue maximization with a constraint on the variety of items in a customer’s shopping basket, which is a measure of how much the customer relies on the retailer to meet their needs. We devise fast and efficient algorithms to solve the resulting optimization models and test them out on a large-scale data set from an online grocery retailer. This is joint work with M. Hichame Benbitour (EM Normandie) and Boxiao Chen (UIC).

More information
Link to the talk: https://eu.bbcollab.com/guest/bba8aee8798e4371ba095d5c179d0744

Organisers

The organizers of the Online Seminar Series Machine Learning NeEDS Mathematical Optimization
Emilio Carrizosa, IMUS-Instituto de Matemáticas de la Universidad de Sevilla
Thomas Halskov, Copenhagen Business School
Kseniia Kurishchenko, Copenhagen Business School
Cristina Molero-Río, IMUS-Instituto de Matemáticas de la Universidad de Sevilla
Jasone Ramírez-Ayerbe, IMUS-Instituto de Matemáticas de la Universidad de Sevilla
Dolores Romero Morales, Copenhagen Business School