Future of Work and Digital Management Journal 3 Serial Number 8 2025 06 01 Hybrid k-means and SVM machine learning for B2B customer segmentation:A case study banking for sustianable sales Hybrid k-means and SVM machine learning for B2B customer segmentation:A case study banking for sustianable sales 1 17 EN Journal Article 2025 02 11 <table> <tbody> <tr> <td> <p>The effectiveness of segmenting Business-to-Business (B2B) customers is necessary to sales strategies and optimize resources. Several clustering methods have been documented in the literature, although limited research has studied the application of machine learning methods for sustainable sales optimization. This study addresses this gap by applying K-means and X-means clustering and Support Vector Machine (SVM) classification, to analyze and segment Bank Mellat B2B customers. Two data analysis methods were used as measures of the customers' transactional and behavioral activities, which included purchase history, frequency of interactions, and service usage. The analysis resulted in two segments of customer identified in the dataset: Cluster 0 consists of customers characterized by low engagement who are younger, and customers with less financial activity; Cluster 1 consists of customers characterized by high levels of engagement and are older while having high account balance and loyalty. The clustering method had an 89% degree of accuracy and SVM had 90% degree of accuracy based the final clustering and was validated with sensitivity analysis. These measures provide improved analytics and enable targeted engagement for further sustainability engagement, such as customer digital engagement for Cluster 0, Cluster 1 with loyalty programs are two targeted engagement methods enhancing resource optimization and further engagement by reducing physically-based interactions. This hybrid approach ultimately provided better engagement analytics measuring both unsupervised and supervised learning analyzed within the static and dynamic outcomes of the bank's data while offering a better scalable solution for profitability after customer engagement and retention. Future research imports an additional measure of sustainability inquiry examining tonsuring sustainability metrics and impacts through machine learning (ML) modeling.</p> </td> </tr> </tbody> </table> B2B customer analysis clustering machine learning sustainable sales resource allocation sales strategies machine learning algorithms customer loyalty resource optimization customer groups https://www.journalfwdmj.com/index.php/fwdmj/article/download/82/77