  In order to steer e-commerce users towards making a purchase, marketers rely
upon predictions of when users exit without purchasing. Previously, such
predictions were based upon hidden Markov models (HMMs) due to their ability of
modeling latent shopping phases with different user intents. In this work, we
develop a duration-dependent hidden Markov model. In contrast to traditional
HMMs, it explicitly models the duration of latent states and thereby allows
states to become "sticky". The proposed model is superior to prior HMMs in
detecting user exits: out of 100 user exits without purchase, it correctly
identifies an additional 18. This helps marketers in better managing the online
behavior of e-commerce customers. The reason for the superior performance of
our model is the duration dependence, which allows our model to recover latent
states that are characterized by a distorted sense of time. We finally provide
a theoretical explanation for this, which builds upon the concept of "flow".
