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

Detecting User Exits from Online Behavior: A Duration-Dependent Latent State Model