Optimizing Sales Strategies in Financing Institutions Through Machine Learning–Based Next Best Offer Recommendation - Daniel Gunawan Soesilo; Benny Setiawan Susilo; Wahyu Tri Setyobudi (Return : Study of Management, Economic and Bussines, 2026)
Digital transformation in the financial services industry makes companies use more data for decision-making. One example is the use of a Machine Learning–based Next Best Offer (NBO-ML) system to help increase sales performance. This study looks at how Data Quality, Model Interpretability, Organizational Readiness, and Privacy Concerns affect the performance of the NBO-ML system and how this system enhances Sales Effectiveness. This research uses a quantitative method and collects data from internal employees who are involved in developing and using the NBO-ML system. The results show that Model Interpretability and Organizational Readiness are very important for improving NBO-ML performance. This means the model must be clear and easy to understand, and the organization must be ready to adopt AI technology. On the other hand, Data Quality and Privacy Concerns do not directly affect system performance, suggesting that these factors may operate in different ways. The performance of the NBO-ML system strongly influences Sales Effectiveness and acts as a bridge between technological factors and business outcomes. Overall, this study shows that explainable models and Organizational Readiness are critical for deriving business value from machine learning–based recommendation systems in the financial services industry.