Environment and Social Psychology

Revolutionizing Customer Engagement: The Synergy of Big Data Analytics and AI-Driven Chatbots

Samrena Jabeen

Faculty of Business Studies, Arab Open University, A’ali 18211, Bahrain

Habil Slade

Faculty of Business Studies, Arab Open University, A’ali 18211, Bahrain

Mohammed Alkashami

Faculty of Business Studies, Arab Open University, A’ali 18211, Bahrain

Mahmood Akbar

Faculty of Business Studies, Arab Open University, A’ali 18211, Bahrain

Abdur Rehman Riaz

Hertfordshire Business School University of Hertfordshire, Hatfield AL10 9,United Kingdom

DOI: https://doi.org/10.59429/esp.v10i11.3589

Keywords: Big Data analytics; AI-powered chatbots; Customer Engagement; PRISMA; algorithms; social networking sites

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Abstract

Objective: Organizations increasingly struggle to leverage the synergistic potential of big data analytics and AI-powered chatbots for customer engagement, despite growing adoption of these technologies independently. This systematic review investigates how the integration of big data analytics and AI-driven chatbots revolutionizes customer engagement strategies and identifies the mechanisms through which their combined application creates superior engagement outcomes compared to individual technology implementations.

Methods: Following PRISMA 2020 guidelines, we conducted a comprehensive systematic review of peer-reviewed literature from Scopus database spanning 2014-2023. Using structured Boolean search strategies combining "big data analytics," "AI-powered chatbots," and "customer engagement" terms, we identified 290 initial articles. After rigorous screening by three independent reviewers applying predefined inclusion/exclusion criteria, 106 studies were selected for analysis. Data extraction and thematic analysis were performed using R Studio and bibliometric techniques to identify synergy mechanisms and engagement outcomes.

Results: The analysis reveals three primary synergy mechanisms: data-driven personalization (where big data insights enhance chatbot customization), conversational analytics (chatbot interactions refine analytical models), and predictive engagement (combined forecasting enables proactive customer service). Organizations implementing integrated approaches achieved 40-60% greater customer engagement improvements compared to single-technology implementations. Social networking platforms emerged as critical enablers, facilitating real-time sentiment analysis, behavioral prediction, and automated response generation. The research identified significant growth in academic attention, with publication rates increasing 39.5% annually and artificial intelligence being the most referenced concept across 29 studies.

Conclusion: This review establishes the first comprehensive framework for understanding big data analytics and AI chatbot synergy in customer engagement contexts. The findings contribute theoretically by extending customer engagement theory through technology integration concepts and practically by providing evidence-based implementation strategies for organizations. The identified synergy mechanisms offer actionable insights for businesses seeking competitive advantage through technological convergence, while highlighting critical areas for future empirical validation and cross-industry research.

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