Assessment of Service Resolution with Dialogue State Tracking in Customer Support Chatbots

Abstract

The integration of automated conversational agents into customer support infrastructure has fundamentally transformed service delivery across digital platforms. However, despite significant advancements in natural language processing architectures, many commercial chatbots continue to exhibit substandard performance concerning end-to-end service resolution, frequently resulting in customer frustration and subsequent escalation to human agents. A primary driver of this failure is the inability of conversational agents to maintain contextual awareness over multi-turn interactions, an issue theoretically addressed by dialogue state tracking. This paper investigates the empirical impact of advanced dialogue state tracking mechanisms on actual service resolution rates through a randomized field experiment conducted in a real-world e-commerce customer support environment. Over a period of three months, customer interactions were randomly assigned to either a baseline chatbot utilizing standard intent classification or a treatment chatbot equipped with a dynamic dialogue state tracking architecture. The findings reveal a substantial and statistically significant improvement in first-contact resolution rates, alongside a notable reduction in average handle times for the treatment group. Furthermore, qualitative assessments of customer satisfaction underscore the critical importance of contextual memory in mitigating user friction. By bridging the gap between controlled academic benchmarks and noisy real-world deployment, this research provides robust empirical evidence validating dialogue state tracking as an essential component for achieving high-fidelity automated service resolution.