Paper Info
Title
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.
Year
DOI
Venue
2017
10.1080/00207543.2015.1066519
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
Keywords
DocType
Volume
product demands, online reviews, promotional marketing, online marketplace, Big Data, neural network
Journal
55
Issue
ISSN
Citations 
17
0020-7543
14
PageRank 
References 
Authors
0.64
22
4
Name
Order
Citations
PageRank
Alain Yee-loong Chong163541.95
Eugene Ch'ng24913.23
Martin J. Liu3322.66
Boying Li4369.80