Adaptive Framework for Evaluating Customer Communication Management Tools

Effective evaluation of Customer Communication Management (CCM) tools is critical in today’s fast-paced digital environment. Traditional evaluation methods, which rely on static thresholds and fixed performance metrics, are increasingly inadequate for capturing the dynamic nature of customer interactions and rapidly changing market conditions. In this paper, we propose a novel adaptive framework that integrates dynamic thresholding, machine learning, and continuous feedback integration to evaluate CCM tools in real time. Our approach recalibrates key performance indicators (KPIs) using live data streams and qualitative customer feedback, thereby delivering more accurate and actionable insights compared to conventional static models. Additionally, we present a scalable system architecture featuring an enhanced block diagram that illustrates the multi-layered structure of the proposed framework. Extensive experimental evaluations, comparative analyses, and discussions of practical implications are provided. This work lays a robust foundation for future research and real-world implementations in adaptive communication management evaluation.