Investigating Impact of Published Content Properties on Customer Engagement in Social Media with Data Mining Approach (Case Study: Instagram Social Media)

Articles in Press

Document Type : Research Paper

Authors

1 عضو هیات علمی

2 1. MSc. Student, Faculty of Management and Economic, University of Shahid Bahonar, Kerman, Iran

3 Department of Management,. Faculty of Management and Economics. Shahid Bahonar University of Kerman. Kerman. Iran

10.22059/jibm.2024.364548.4647

Abstract

Abstract

Purpose: Purpose: With the ever-increasing role, social media plays in the growth of digital marketing as one of the most important yet unpredictable trends in marketing today, particular platforms such as Facebook and Instagram have developed into indispensable tools in the area of advertising and marketing. This indicates Instagram ranks second after Facebook in terms of leading social media platforms used by marketing Profs and brand owners. A large amount of advertising and marketing content produced in social networks has made business owners prepare this content in a way that is interesting for the target audience and thus increase the engagement rate on their business pages. On the other hand, due to the significant increase in the number of followers on Instagram over the past years, as well as the interest of users to follow businesses on this platform, the great importance of this network is unconcealed for marketing specialists. In this way, this study aims to investigate the effect of published content attributes on customer engagement in the Instagram.

Method: Considering the huge amount of content produced in social networks, one of the best methods to analyze and identify patterns in these networks are data mining techniques. To achieve this goal, visual and textual features of 490 content published in the commercial pages of cosmetics products- Cinere, Lisel, Schon and Bioderma brands- were collected. Then Clementine data mining toolkit and three methods of Association Rules, Apriori Algorithm and Decision Tree used to identify features affecting customer engagement in terms of likes, comments and conversations and evaluate their effectiveness.

Findings: The results of this study indicate effects of embedded visual and textual content features of published messages on customer engagement behavior. Based on these results, the use of persuasive textual features - such as Holiday Mention and Remarkable facts - as well as informative textual features - such as price and target audiences - affect customer engagement in social media. In addition, visual informative features - such as brand centrality and product centrality - affect customer engagement behavior to a certain degree.

Conclusion: Previous research on Instagram have mainly emphasized likes and comments as customer engagement behaviors. In addition to examine these two behaviors, the present research also evaluates the conversations raised in comments (conversations). Moreover, by considering all parts of the published content and defining thirteen textual features and two visual features the present research provides a deeper understanding of the effective content features that influence customer engagement behaviors and fill the existing gap based on how to create the published content in order to increase engagement. Findings provide business managers and brands, especially in the field of cosmetics and hygiene products, with adoption of an efficient and effective customer engagement strategy in the visual social networks.

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Journal of Business Management

Articles in Press, Accepted Manuscript
Available Online from 09 June 2024

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