This chapter investigates the relationship between artificial intelligence algorithms and advertising performance and customer engagement in Zimbabwe. Four AI algorithm types were examined as independent variables: Natural language processing (NLP), machine learning (ML), decision trees (DT), and data visualization (DV). Statistical analysis revealed significant positive relationships between all four algorithms and the dependent variable of advertising performance and customer engagement, with t-values exceeding 1.96. Among the algorithms, data visualization showed the strongest influence (t=7.952), followed by decision trees (t=5.401), machine learning (t=2.698), and natural language processing (t=2.069). The findings suggest integrating these AI algorithms into advertising strategies can improve effectiveness and customer interaction. This study provides empirical evidence for the positive impacts of leveraging AI to advance precision marketing in Zimbabwe. The results offer valuable insights for developing more targeted and effective advertising strategies.
Top1. Introduction
In today's digital business environment, targeted advertising and customer engagement have become critical for companies to gain competitive advantage and drive growth (Bharadiya, 2023; Grandhi et al., 2021). Artificial intelligence (AI) is transforming marketing by enabling more personalized and predictive consumer targeting and engagement (Vidhya et al., 2023). By analyzing extensive data on demographics, behaviors, preferences and social connections, AI systems can precisely identify micro-segments of target audiences and tailor product offerings, pricing, promotions, ad content and channels for each individual (Choudhury et al., 2023; Rathore, 2020). Zimbabwean firms have increasingly adopted AI-powered platforms and tools to harvest customer insights and interact with consumers in a customized manner based on their attributes and predicted responses (Mathenjwa, 2023; Mbangula, 2022). For instance, banks like CBZ use AI chatbots while retailers like OK Zimbabwe deploy machine learning algorithms to recommend relevant products to customers (Mhlanga, 2021).
While AI-driven precision marketing shows great promise, its adoption remains limited in Zimbabwe compared to global trends (Butcher et al., 2021; Mudongo, 2021). The country still relies heavily on traditional mass marketing tactics with minimal personalization and consumer insights (Nyatsambo, 2021). Several constraints impede the shift to more advanced, AI-enabled targeted advertising including costs, skills gaps, data limitations, legacy systems, privacy concerns, and organizational resistance (Chiwaridzo, 2023; Pires et al., 2023). Most Zimbabwean companies focus AI on back-end efficiencies rather than customer engagement and lack expertise in strategic deployment for precision advertising (Arner et al., 2021).
Though interest is rising, existing literature does not provide sufficient empirical evidence, cost-benefit analyses, and qualitative insights on real-world AI adoption for customer targeting and engagement in the Zimbabwean context. Surveys, case studies, and practitioner interviews examining the extent of implementation, systematic impacts, and pragmatic success factors across industries, company sizes, and consumer segments could guide organizational strategy (Mbunge et al., 2021; Mhlanga et al., 2023; Shambira, 2020). This significant research gap needs to be addressed for Zimbabwean firms to effectively leverage AI in transforming data-driven, personalized marketing and gaining competitive advantage.
Therefore, this paper aims to develop a conceptual framework and employ quantitative methodology to investigate the multifaceted relationship between major AI techniques like natural language processing, machine learning, decision trees, and data visualization and targeted advertising performance in Zimbabwe. The findings will provide practical insights into how organizations can strategically deploy AI to optimize customer engagement outcomes and reorient their marketing approaches.