Competitive analysis of social media data in the banking industry. (2017)

• Record Type: Journal Article
• Authors: Afolabi, Ibukun Tolulope; Ezenwoke, Azubuike Ansalem; Ayo, Charles K.
• Is Part Of: International journal of internet marketing and advertising. Volume 11:Number 3(2017)
• Page Start: 183
• Page End: 201
• Publication Date: 2017
• Subjects: social media -- Twitter -- Facebook -- text mining -- banking -- competitive intelligence -- clustering -- sentiment analysis; Internet marketing -- Periodicals; Internet advertising -- Periodicals; 658.872
• Languages: English
• ISSNs: 1477-5212

Abstract

Recently, most companies interact more with their customers through the social media, particularly Facebook and Twitter. This has made large amount of textual data freely available on the internet for competitive intelligence analysis, which is helping reposition more and more companies for better profit. In order to carry out competitive intelligence, financial institutions need to take note of and analyse their competitor's social media sites. This paper, therefore, aims to help the banking industry in Nigeria understand how to perform a social media competitive analysis and transform social media data into knowledge, which will form the foundation for decision-making and internet marketing of such institutions. The study describes an in-depth case study which applies text mining to analyse unstructured text content on Facebook and Twitter sites of the five largest and leading financial institutions (banks) in Nigeria: Zenith Bank, First Bank, United Bank for Africa, Access Bank and GTBank. Analysing the social media content of these institutions will increase their competitive advantage and also lead to more profit for the banking institutions in question. The results obtained from this research showed that text mining is able to reveal uncommon and non-trivial trend for competitive advantage from social media data, and also provide specific recommendations to help banks maximise their competitive edge.