An Intelligent Decision Support System Architecture for Key Account Manager Lifecycle Optimization Using Integrated HRIS-CRM and Machine Learning
Received: 14 January 2026 | Revised: 11 February 2026, 17 February 2026, 21 February 2026, and 26 February 2026 | Accepted: 28 February 2026 | Online: 4 April 2026
Corresponding author: Andri Fabriansyah
Abstract
Key Account Manager (KAM) lifecycle management requires a Decision Support System (DSS) that can integrate workforce and customer performance data to support strategic decision-making. This study develops an intelligent DSS architecture that integrates Human Resource Information Systems (HRIS) and Customer Relationship Management (CRM) data to support lifecycle-based KAM performance management. The proposed framework encompasses system architecture design, end-to-end data integration, and regression-based machine learning analytics using Random Forest and XGBoost models on preprocessed HRIS-CRM data. The regression models generate standardized continuous KAM performance scores that capture performance variability across lifecycle stages, enabling adaptive managerial decision-making. By embedding predictive analytics into structured decision workflows, the proposed DSS bridges analytical modeling and operational implementation. The findings indicate the practical applicability of a regression-based, lifecycle-aware DSS framework for guiding data-driven managerial decisions in complex B2B environments, consistent with the established methodological advantages of ensemble learning in predictive organizational analytics.
Keywords:
Key Account Manager (KAM), Decision Support System (DSS), Human Resource Information System-Customer Relationship Management (HRIS-CRM) integration, machine learning, performance risk prediction, regression-based analyticsDownloads
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Copyright (c) 2026 Andri Fabriansyah, Aghus Sofwan, Kardison Lumban Batu, Sarah Nazly Nuraya
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