Local banks often face difficulties in maximizing customer lifetime value and delivering personalized offerings, leading to suboptimal marketing efforts. To overcome these challenges, we designed a machine learning solution for Customer Value Management (CVM) focused on enhancing customer segmentation, targeting, and product sales optimization.
The project involved designing an end-to-end machine learning workflow to improve customer engagement and identify high-value prospects. As a technical advisor and supervisor, we guided the development team to ensure alignment with business objectives and technical feasibility. A comprehensive customer segmentation report was produced, categorizing customers into "willing to buy" and "willing to pay" segments to inform targeted marketing strategies. Segmentation insights further enabled the creation of optimized product offerings, boosting both customer acquisition and retention.
This machine learning-driven solution empowered the bank to precisely target high-value customer segments, resulting in increased sales, enhanced marketing efficiency, and stronger customer loyalty.
