Gaining competitive intelligence from social media data: Evidence from two largest retail chains in the world

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.

Citation

He, W., Shen, J., Tian, X., Li, Y., Akula, V., Yan, G. and Tao, R. (2015), "Gaining competitive intelligence from social media data: Evidence from two largest retail chains in the world", Industrial Management & Data Systems, Vol. 115 No. 9, pp. 1622-1636. https://doi.org/10.1108/IMDS-03-2015-0098