Predicting consumer product demands via Big Data: the roles of online promotional marketing and online reviews

Abstract

This study aims to investigate the contributions of online promotional marketing and online reviews as predictors of consumer product demands. Using electronic data from Amazon.com, we attempt to predict if online review variables such as valence and volume of reviews, the number of positive and negative reviews, and online promotional marketing variables such as discounts and free deliveries, can influence the demand of electronic products in Amazon.com. A Big Data architecture was developed and Node.JS agents were deployed for scraping the Amazon.com pages using asynchronous Input/Output calls. The completed Web crawling and scraping data-sets were then preprocessed for Neural Network analysis. Our results showed that variables from both online reviews and promotional marketing strategies are important predictors of product demands. Variables in online reviews in general were better predictors as compared to online marketing promotional variables. This study provides important implications for practitioners as they can better understand how online reviews and online promotional marketing can influence product demands. Our empirical contributions include the design of a Big Data architecture that incorporate Neural Network analysis which can used as a platform for future researchers to investigate how Big Data can be used to understand and predict online consumer product demands.

Citation

Chong, A. Y. L., Ch’ng, E., Liu, M. J., & Li, B. (2017). Predicting consumer product demands via Big Data: the roles of online promotional marketing and online reviews. International Journal of Production Research, 55(17), https://doi.org/10.1080/00207543.2015.1066519

• Journal Article Type: Article
• Acceptance Date: Jun 15, 2015
• Online Publication Date: Jul 24, 2015
• Publication Date: Sep 1, 2017
• Deposit Date: May 22, 2018
• Publicly Available Date: May 22, 2018
• Journal: International Journal of Production Research
• Print ISSN: 0020-7543
• Electronic ISSN: 0020-7543
• Publisher: Taylor & Francis Open
• Peer Reviewed: Peer Reviewed
• Volume: 55
• Issue: 17
• DOI: https://doi.org/10.1080/00207543.2015.1066519
• Keywords: Product demands; online reviews; promotional marketing; online marketplace; big data; neural network
• Public URL: https://nottingham-repository.worktribe.com/output/879866
• Publisher URL: https://www.tandfonline.com/doi/full/10.1080/00207543.2015.1066519
• Additional Information: This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of Production Research on 24 July 2015, available online: http://www.tandfonline.com/10.1080/00207543.2015.1066519