Boosting insurance sales performance with marketing strategy: the power of artificial intelligence, chatbot engagement and machine learning
Faisal Iddris et al.
What the paper says
Purpose This study examines how marketing strategy influences insurance sales performance by integrating artificial intelligence (AI) chatbot engagement and machine learning into the analysis. It aims to uncover how digital technologies transform the effectiveness of marketing strategy in Ghana's insurance sector, an underexplored context in emerging economies. Design/methodology/approach A quantitative, cross-sectional survey was conducted among 246 employees of licensed insurance companies in Ghana. Data were collected using structured questionnaires, which were analyzed with Structural Equation Modelling (SEM) to test the direct, mediating and moderating relationships among the variables. Findings Results reveal that marketing strategy alone does not significantly predict insurance sales performance. However, AI chatbot engagement fully mediates this relationship, demonstrating that marketing strategy becomes effective only when implemented through technology-driven customer engagement. Moreover, machine learning positively moderates the link between AI chatbot engagement and sales performance, indicating that higher levels of machine learning capability strengthen the impact of chatbot interactions on sales outcomes. Practical implications The study provides a strategic roadmap for insurance firms to embed AI chatbots and machine learning into their marketing systems. Managers are encouraged to develop digital competencies and data-driven cultures that enhance customer experience, personalization and decision-making in competitive insurance markets. Originality/value This research is among the first to integrate AI chatbot engagement and machine learning within the Resource-Based Theory framework to explain marketing performance dynamics in an African insurance context. It advances theoretical understanding by conceptualizing AI technologies as strategic resources that convert marketing capabilities into measurable performance gains, offering novel and context-specific insights for digital transformation in emerging markets.
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.50 × 0.4 = 0.20 |
| M · momentum | 0.50 × 0.15 = 0.07 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.