Extracting Dimensions of Consumer Satisfaction with Quality from Online Chatter: Strategic Brand Analysis of Big Data Using Latent Dirichlet Allocation

61 Pages Posted: 15 Mar 2014 Last revised: 20 Mar 2014

See all articles by Seshadri Tirunillai

Seshadri Tirunillai

University of Houston - C.T. Bauer College of Business

Gerard J. Tellis

University of Southern California - Marshall School of Business, Department of Marketing

Date Written: March 13, 2014

Abstract

Online chatter or User-Generated Content (UGC) constitutes an excellent emerging source for identifying dimensions of quality at a very high temporal frequency. This study proposes a unified framework for extracting the latent dimensions of consumer satisfaction with quality and ascertaining the valence, labels, validity, importance, dynamics, and heterogeneity of those dimensions using unsupervised Latent Dirichlet Allocation (LDA). Our sample of UGC consists of rich data on product reviews across fifteen firms in five markets, over four years. The results suggest that a few dimensions are enough to capture quality. These dimensions have good face and external validity. Dynamic analysis enables tracking the importance of dimensions over time. It also allows for dynamic mapping of competitive brand positions on those dimensions over time. For vertically differentiated markets such as cell phones and computers, objective dimensions dominate and are similar across markets, heterogeneity is low across dimensions, and stability is high across time. For horizontally differentiated markets, such as shoes and toys, subjective dimensions dominate but vary across markets, heterogeneity is high across dimensions, while stability is low over time.

Keywords: Consumer Satisfaction, Quality, Dimensions, Brand Mapping, Big Data, Latent Dirichlet Allocation, Online Chatter, User Generated Content

Suggested Citation

Tirunillai, Seshadri and Tellis, Gerard J., Extracting Dimensions of Consumer Satisfaction with Quality from Online Chatter: Strategic Brand Analysis of Big Data Using Latent Dirichlet Allocation (March 13, 2014). Available at SSRN: https://ssrn.com/abstract=2408855 or http://dx.doi.org/10.2139/ssrn.2408855

Seshadri Tirunillai

University of Houston - C.T. Bauer College of Business ( email )

Houston, TX 77204-6021
United States

Gerard J. Tellis (Contact Author)

University of Southern California - Marshall School of Business, Department of Marketing ( email )

Hoffman Hall 701
Los Angeles, CA 90089-0443
United States
213-740-5031 (Phone)
213-740-7828 (Fax)

HOME PAGE: http://gtellis.net

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