Predictive Modeling of Employee–Customer Interaction Outcomes Through AI-Enabled E-CRM and HR Analytics

Abstract

Global Capability Centres (GCCs) operating in knowledge-based sectors face two related challenges, namely employee attrition and customer churn. This research addresses a gap by connecting human resource (HR) analytics and electronic customer relationship management (E-CRM) data using artificial intelligence (AI) to examine outcomes of employee–customer interaction. Based on the Service-Profit Chain and Social Exchange Theory, the research proposes a conceptual framework in which AI serves as a bridge between employee measures (engagement, training, and workload) and customer measures (response time and complaints). A quantitative predictive design utilizing primary data is conducted on the R&D and Automotive Divisions of two GCCs, with 250 employees and 600 customer response data. Employees with greater engagement and more hours of training tend to experience lower attrition rates and more positive customer interactions, while these interactions are associated with shorter response times, improved complaint resolution, and increased customer loyalty. Hypothesis tests reveal significant relationships, for example, engagement has a negative relationship with attrition (p < 0.001). The findings highlight that aligning the employee experience with customer service experience through AI analytics has the potential to enhance retention and loyalty. The paper concludes by outlining the strategic implications for HR and CRM managers, as well as potential directions for further research in integrated people– customer analytics.