Publication Type
Conference Proceeding Article
Version
acceptedVersion
Publication Date
1-2015
Abstract
Cable TV return path data made possible by current generation set-top boxes present a new opportunity to analyze household viewing behavior and recover household viewing preferences from it. This research develops a model of household viewing preference that supports quantifying a household's valuation for different categories of digital content within the constraints of the programs to which it subscribes. This study uses a data set of more than 1 million observations on households from a digital entertainment firm that offers basic and premium services. Our estimation is via a Bayesian hierarchical model that employs the Gibbs sampler. The results show that households have relatively homogeneous preferences for entertainment content, but they show heterogeneous preferences for content in the specific packages to which they subscribe. In addition, both HD and premium movies subscriptions have a differentiation effect on enhancing household preferences toward their most preferred content. The findings provide useful insights for understanding household preferences, and are intended to support promotion and content strategy adjustments to improve customer satisfaction.
Keywords
Digital television, Hierarchical systems, Set-top boxes, customer satisfaction
Discipline
Computer Sciences | E-Commerce | Management Information Systems
Research Areas
Information Systems and Management
Publication
48th Hawaii International Conference on System Sciences HICSS 2015: 5-8 January, Kauai: Proceedings
First Page
4276
Last Page
4284
ISBN
9781479973675
Identifier
10.1109/HICSS.2015.512
Publisher
IEEE Computer Society
City or Country
Los Alamitos, CA
Citation
LI, Jin; GUO, Zhiling; and KAUFFMAN, Robert J..
Recovering Household Preferences for Digital Entertainment. (2015). 48th Hawaii International Conference on System Sciences HICSS 2015: 5-8 January, Kauai: Proceedings. 4276-4284.
Available at: https://ink.library.smu.edu.sg/sis_research/2599
Copyright Owner and License
LARC
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.ieeecomputersociety.org/10.1109/HICSS.2015.512