Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal Return : Study of Management, Economic and Bussines
Soesilo, Daniel Gunawan
Unknown Affiliation
Economics, Econometrics & Finance

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Optimizing Sales Strategies in Financing Institutions Through Machine Learning–Based Next Best Offer Recommendation Soesilo, Daniel Gunawan; Susilo, Benny Setiawan; Setyobudi, Wahyu Tri
Return : Study of Management, Economic and Bussines Vol. 5 No. 3 (2026): Return: Study of Management, Economic and Business
Publisher : PT. Publikasiku Academic Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57096/return.v5i3.444

Abstract

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.
Co-Authors Setyobudi, Wahyu Tri Susilo, Benny Setiawan