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‘Children of the HMM’: Modeling Longitudinal Customer Behavior at Hulu.Com

51 Pages Posted: 4 Aug 2011  

Eric M. Schwartz

University of Michigan, Stephen M. Ross School of Business

Eric Bradlow

University of Pennsylvania - Marketing Department

Peter Fader

University of Pennsylvania - Marketing Department

Yao Zhang

University of Pennsylvania - The Wharton School

Date Written: August 1, 2011

Abstract

Stand-alone marketing models are well-suited to deal with different behavioral features such as variation in transaction frequency (customer heterogeneity with latent classes), recency and attrition (“buy ‘till you die” models), and more general changes in customer transaction rates (hidden Markov models, HMMs). We unite these modeling approaches in a integrative framework as special cases or “children” of the HMM. We then selectively constrain the general model to assess the impact of each component on model performance. Instead of selecting latent-state models primarily using likelihood-based criteria, we favor a multi-faceted empirical evaluation using summaries of posterior predictive distributions; thus focusing model checking on managerially relevant features of the data, such as reach, frequency, and “streakiness.”

We apply our methods to daily viewing incidence data from Hulu.com, a leading U.S. online streaming video provider. We find that increasing model complexity can improve some aspects of model performance (as expected) but worsen others in non-obvious ways. For instance, only models allowing back-and-forth movements among latent states can capture streakiness (a pattern of growing importance given the increasing availability of digital media data); but as a trade-off, these models still perform worse than their simpler counterparts in both forecasting and capturing other audience measurement criteria. Finally, using machine-learning classification techniques, customers are grouped based on similar model fit and features of their past consumption patterns. This allows researchers and managers to portend the “winning model” prior to having to fit the models for all customers. We discuss the generality of the methods and findings for different mixes of patterns of customer behavior.

Keywords: Hidden Markov Models, Non-Stationarity, Customer Migration, Streakiness, Posterior Predictive Model Checking, Nested Models, Hierarchical Bayes, Machine Learning, Classification Trees

JEL Classification: C11, C15, C22, C23, C51, C52, C53, M31

Suggested Citation

Schwartz, Eric M. and Bradlow, Eric and Fader, Peter and Zhang, Yao, ‘Children of the HMM’: Modeling Longitudinal Customer Behavior at Hulu.Com (August 1, 2011). Available at SSRN: https://ssrn.com/abstract=1904562 or http://dx.doi.org/10.2139/ssrn.1904562

Eric M. Schwartz (Contact Author)

University of Michigan, Stephen M. Ross School of Business ( email )

701 Tappan Street
Ann Arbor, MI MI 48109
United States

Eric Bradlow

University of Pennsylvania - Marketing Department ( email )

700 Jon M. Huntsman Hall
3730 Walnut Street
Philadelphia, PA 19104-6340
United States
215-898-8255 (Phone)

Peter Fader

University of Pennsylvania - Marketing Department ( email )

700 Jon M. Huntsman Hall
3730 Walnut Street
Philadelphia, PA 19104-6340
United States

Yao Zhang

University of Pennsylvania - The Wharton School ( email )

3641 Locust Walk
Philadelphia, PA 19104-6365
United States

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