Gaining competitive intelligence from social media data: Evidence from two largest retail chains in the world. Issue 9 (19th October 2015)

• Record Type: Journal Article
• Authors: He, Wu; Shen, Jiancheng; Tian, Xin; Li, Yaohang; Akula, Vasudeva; Yan, Gongjun; Tao, Ran
• Editors: Xiaojun Wang, Professor Leroy White and Professor Xu Chen, Dr
• Is Part Of: Industrial management & data systems. Volume 115:Issue 9(2015)
• Page Start: 1622
• Page End: 1636
• Publication Date: 2015-10-19
• Subjects: Competitive intelligence -- Social media analytics; Industrial management -- Periodicals; Electronic data processing -- Periodicals; Business -- Periodicals; Industrial management -- Great Britain -- Periodicals; 658.05
• DOI: 10.1108/IMDS-03-2015-0098
• Languages: English
• ISSNs: 0263-5577

Abstract

Abstract : Purpose: – Social media analytics uses data mining platforms, tools and analytics techniques to collect, monitor and analyze massive amounts of social media data to extract useful patterns, gain insight into market requirements and enhance business intelligence. The purpose of this paper is to propose a framework for social media competitive intelligence to enhance business value and market intelligence. Design/methodology/approach: – The authors conducted a case study to collect and analyze a data set with nearly half million tweets related to two largest retail chains in the world: Walmart and Costco in the past three months during December 1, 2014-February 28, 2015. Findings: – The results of the case study revealed the value of analyzing social media mentions and conducting sentiment analysis and comparison on individual product level. In addition to analyzing the social media data-at-rest, the proposed framework and the case study results also indicate that there is a strong need for creating a social media data application that can conduct real-time social media competitive intelligence for social media data-in-motion. Originality/value: – So far there is little research to guide businesses for social media competitive intelligence. This paper proposes a novel framework for social media competitive intelligence to illustrate how organizations can leverage social media analytics to enhance business value through a case study.