A multi-layer dynamic model for customer experience analytics

Today, data collected by service providers can track an individual user's experience in detail, at flow or packet level in real time. However, we still lack analytics methods that can translate this information into a comprehensive and ever-evolving representation of the user experience. In this paper, we provide a layered dynamic model that addresses the problem of how to relate low-level network performance metrics to a user's perception of network service and their subsequent actions. Using time-stamped observations from networks, devices, and customer care, we build probabilistic models to link network performance to an inferred state of customer satisfaction, and then to explicit and implicit customer disengagement events. We provide inference algorithms for the model parameters, and report test results on synthesized datasets based on real, but incomplete, observations. We discuss how popular anonymization techniques such as data masking, encryption, k-anonymization, and differential privacy can be used to protect sensitive and private user data without impacting the user experience inference.