Inverse Reinforcement Learning for Marketing
This work addresses dynamic consumer demand modeling for marketers, but it appears incremental as it adapts an existing IRL method to a specific domain.
The authors tackled the problem of learning customer preferences from observed behavior in marketing by proposing an Inverse Reinforcement Learning (IRL) approach, which resulted in a tractable model that reduces to low-dimensional convex optimization and demonstrated through simulations that observational noise can be mistaken for consumer heterogeneity.
Learning customer preferences from an observed behaviour is an important topic in the marketing literature. Structural models typically model forward-looking customers or firms as utility-maximizing agents whose utility is estimated using methods of Stochastic Optimal Control. We suggest an alternative approach to study dynamic consumer demand, based on Inverse Reinforcement Learning (IRL). We develop a version of the Maximum Entropy IRL that leads to a highly tractable model formulation that amounts to low-dimensional convex optimization in the search for optimal model parameters. Using simulations of consumer demand, we show that observational noise for identical customers can be easily confused with an apparent consumer heterogeneity.