Your Data is Screaming at You: Learning to Listen through Choice Modeling
Understanding how people choose is an age-old challenge. Consider the fundamental problem a retailer must solve in assorting a store: a large assortment will likely draw a bigger set of customers, but will be substantially more expensive to carry and will also likely introduce cannibalization between products. A smaller assortment might not carry these risks, but may fail to attract a sufficiently large group of customers.
In this talk, we will walk through an innovative new approach to machine learning that seeks to model and learn customer choice patterns and preferences from sparse transactional data. We will then discuss how this approach helps retailers build localized product assortments personalized to the foot traffic at each store, while simultaneously reducing assortment complexity and discovering new, surprising opportunities for growth.