Topic Modeling Emerging Trends for Business Intelligence in Marketing: With Text Mining and Latent Dirichlet Allocation

Subject Areas : Marketing

Rouhollah Bagheri ¹ (Department of Management, Faculty of Administrative and Economic Sciences, Ferdowsi University of Mashhad, Mashhad, Iran.)
Nahid Entezarian ² (Assistant Professor, Department of Management, Faculty of Administrative and Economic Sciences, Ferdowsi University of Mashhad, Mashhad, Iran.)

Received: 2023-11-07 Accepted : 2023-12-19 Published : 2024-02-04

Keywords: Text Mining, Latent Dirichlet allocation, Business Intelligence, topic modeling, Marketing,

Abstract :

This paper examines recent literature in the quest to uncover emerging patterns in the use of business intelligence in marketing. We conducted searches in pertinent academic journals and identified 1044 articles published between 2000 and 2023. To sift through this substantial body of work, we employed text mining techniques to extract pertinent terms in the realms of business intelligence and marketing. Additionally, we applied latent Dirichlet allocation modeling to categorize the articles into various pertinent topics. This analysis was performed within the domains of marketing and business intelligence. This approach enabled us to discover connections between terms and topics, which in turn allowed us to generate hypotheses regarding future research directions. To validate these hypotheses, we gathered and closely examined relevant articles. By pinpointing current research areas, this study underscores potential avenues for future investigation. The findings reveal that the predominant trend in business intelligence applications for marketing is the utilization of business intelligence systems, with a particular emphasis on marketing planning to enhance marketing strategies. Additionally, there is considerable interest in areas such as pricing models for marketing, enhancing brand value through effective social media marketing, employing predictive algorithms for customer data analysis, and harnessing big data for marketing analytics.

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