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Competitive analysis of social media data in the banking industry

Afolabi, I. T. and Ezenwoke, Azubuike and Ayo, C. K. (2017) Competitive analysis of social media data in the banking industry. Int. J. Internet Marketing and Advertising, X (Y,XXXX).

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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.

Item Type: Article
Uncontrolled Keywords: social media; Twitter; Facebook; text mining; banking; competitive intelligence; clustering; sentiment analysis.
Subjects: Q Science > Q Science (General)
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Engineering, Science and Mathematics > School of Electronics and Computer Science
Depositing User: Mrs Hannah Akinwumi
Date Deposited: 19 Sep 2018 13:38
Last Modified: 03 Sep 2021 11:07
URI: http://eprints.covenantuniversity.edu.ng/id/eprint/11819

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