10h30 - 10h55
Preference-Based Customer Segmentation for Assortment Planning
In the data science team at JDA, we developed a preference-based customer segmentation tool that helps in the assortment planning problem, during which a retailer select what products to put in each store. For that, we analyze products’ attributes to discover customers’ motivation and preferences, and then find patterns and similarities between different purchases to group shoppers together.
10h55 - 11h20
Data-Driven Distributionally-Robust Facility Location Problems
We consider two classical facility location problems and show how they can be robustified against the distributional ambiguity that arise when using data. We also show that one can improve the worst-case expected performance by “randomizing” over different subsets of locations and devise an efficient algorithm to identify such solutions.
11h20 - 11h45
Learning consumer preferences for data-driven large-scale assortment optimization
We propose a new representation for rank-based choice models that generalizes classical representations. The model allows for subsets of products on which the consumer does not have a strict preference and enables us to efficiently train the consumer preferences. This is exemplified on large artificial and industrial data sets.