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

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  • Alain Yee Loong Chong
  • Eugene Ch’ng
  • Martin J. Liu
  • Boying Li

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.

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  • Alain Yee Loong Chong & Eugene Ch’ng & Martin J. Liu & Boying Li, 2017. "Predicting consumer product demands via Big Data: the roles of online promotional marketing and online reviews," International Journal of Production Research, Taylor & Francis Journals, vol. 55(17), pages 5142-5156, September.
    • Handle: RePEc:taf:tprsxx:v:55:y:2017:i:17:p:5142-5156
    DOI: 10.1080/00207543.2015.1066519
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    1. Doern, Rachel R. & Fey, Carl F., 2006. "E-commerce developments and strategies for value creation: The case of Russia," Journal of World Business, Elsevier, vol. 41(4), pages 315-327, December.
    2. Payam Zarafshan & S. Ali A. Moosavian, 2011. "Rigid--flexible interactive dynamics modelling approach," Mathematical and Computer Modelling of Dynamical Systems, Taylor & Francis Journals, vol. 18(2), pages 175-199, July.
    3. Liu, Martin J. & Yannopoulou, Natalia & Bian, Xuemei & Elliott, Richard, 2015. "Authenticity Perceptions in the Chinese Marketplace," Journal of Business Research, Elsevier, vol. 68(1), pages 27-33.
    4. Garrett P. Sonnier & Leigh McAlister & Oliver J. Rutz, 2011. "A Dynamic Model of the Effect of Online Communications on Firm Sales," Marketing Science, INFORMS, vol. 30(4), pages 702-716, July.
    5. Dobrzykowski, David & Saboori Deilami, Vafa & Hong, Paul & Kim, Seung-Chul, 2014. "A structured analysis of operations and supply chain management research in healthcare (1982–2011)," International Journal of Production Economics, Elsevier, vol. 147(PB), pages 514-530.
    6. Gupta, Sunil & Cooper, Lee G, 1992. "The Discounting of Discounts and Promotion Thresholds," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 19(3), pages 401-411, December.
    7. Xianghua Lu & Sulin Ba & Lihua Huang & Yue Feng, 2013. "Promotional Marketing or Word-of-Mouth? Evidence from Online Restaurant Reviews," Information Systems Research, INFORMS, vol. 24(3), pages 596-612, September.
    8. Duan, Wenjing & Gu, Bin & Whinston, Andrew B., 2008. "The dynamics of online word-of-mouth and product sales—An empirical investigation of the movie industry," Journal of Retailing, Elsevier, vol. 84(2), pages 233-242.
    9. Chong, Alain Yee-Loong & Ooi, Keng-Boon & Sohal, Amrik, 2009. "The relationship between supply chain factors and adoption of e-Collaboration tools: An empirical examination," International Journal of Production Economics, Elsevier, vol. 122(1), pages 150-160, November.
    10. Seshadri Tirunillai & Gerard J. Tellis, 2012. "Does Chatter Really Matter? Dynamics of User-Generated Content and Stock Performance," Marketing Science, INFORMS, vol. 31(2), pages 198-215, March.
    11. Zhang, Jason Q. & Craciun, Georgiana & Shin, Dongwoo, 2010. "When does electronic word-of-mouth matter? A study of consumer product reviews," Journal of Business Research, Elsevier, vol. 63(12), pages 1336-1341, December.
    12. Chakravarty, Anindita & Liu, Yong & Mazumdar, Tridib, 2010. "The Differential Effects of Online Word-of-Mouth and Critics' Reviews on Pre-release Movie Evaluation," Journal of Interactive Marketing, Elsevier, vol. 24(3), pages 185-197.
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    25. Boccali, Filippo & Mariani, Marcello M. & Visani, Franco & Mora-Cruz, Alexandra, 2022. "Innovative value-based price assessment in data-rich environments: Leveraging online review analytics through Data Envelopment Analysis to empower managers and entrepreneurs," Technological Forecasting and Social Change, Elsevier, vol. 182(C).

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