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

Research output: Journal PublicationArticlepeer-review

131 Citations (Scopus)
35 Downloads (Pure)


This study aims to investigate the contributions of online promotional marketing and online reviews as predictors of consumer product demands. Using electronic data from, 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 A Big Data architecture was developed and Node.JS agents were deployed for scraping the 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.

Original languageEnglish
Pages (from-to)5142-5156
Number of pages15
JournalInternational Journal of Production Research
Issue number17
Publication statusPublished - 2 Sept 2017


  • Big Data
  • neural network
  • online marketplace
  • online reviews
  • product demands
  • promotional marketing

ASJC Scopus subject areas

  • Strategy and Management
  • Management Science and Operations Research
  • Industrial and Manufacturing Engineering


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