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The insurance sector plays a crucial role in fostering sustainable economic development within a country. As the customer base grows, insurance companies must prioritize transitioning to data-driven strategies to cut costs and make more informed marketing choices in today’s digital era. This study proposes a new decision-making framework for precision marketing, based on a real case study from a Moroccan insurance company that aims to solve a practical problem. The proposed decision-making system consists of four components, with each component involving important steps. Firstly, data preparation was performed, consisting of four critical stages: data acquisition, data cleaning and filtering, feature selection, and oversampling. Secondly, top 20% and top 50% consumers are taken as examples to present their customer persona in detail. Based on the processed data, we analysed consumer consumption behaviors using four ML algorithms and made a performance comparison of the four algorithms. Additionally, we conducted feature selection methods to identify the most relevant features and evaluate the system’s performance. The aim of the proposed precision decision-making system is to assist managers in discerning the distinctive characteristics of potential customers and proposing tailored precision marketing strategies. This approach is expected to substantially reduce advertising expenses and enhance overall marketing efficiency . A case study using real-world data from a Moroccan insurance company was conducted to demonstrate the practical implementation of the proposed framework. The results of the study indicate that the proposed system yielded favourable outcomes.
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Stored only when both papers are in the corpus — 11 citations exist upstream.
Papers that cite the same references — the relational fallback while embeddings are still backfilling.
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