Using the data mining technique to predict successful customer engagement of marketing campaigns in social media
by Fatema Salim Al Rabaani; Aiman Moyaid Said; Sallam Osman Fageeri; AbdulRahman Khalifa AlAbdulsalam
International Journal of Business Intelligence and Data Mining (IJBIDM), Vol. 23, No. 2, 2023

Abstract: Marketing in social media platforms plays a vital role in enhancing the return of investment for start-up companies in the fashion industry. Predicting the level of customer engagement of the marketing campaign in social media reveals customers' preferences and public attention towards the marketing campaign. This research proposed using different data mining classifiers to predict the success of online marketing campaigns to reduce the efforts and allocated resources toward achieving the goal of marketing in the fashion industry. The research collected 8,151 marketing campaigns published on Instagram users account for the fashion industry in Oman, formed social-media-based metrics, and formulated data mining algorithms to predict the customer engagement level. The results show that the model induced by neural networks provides the highest accuracy, 96%, in predicting customer engagement levels. The results illustrated that the number of posts published is not essential to gaining additional interactions from the user.

Online publication date: Sun, 30-Jul-2023

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