Measuring and Improving AI Performance in Conversational Shopping Assistants

Generative AI has transformed the domain of conversational AI, opening doors for a new breed of e-commerce shopping assistants. Developments in Generative AI have the potential to improve customer experience through more natural, dynamic and context-aware dialog exchange. However, the current implementations have critical flaws — limited access to real-time data and not enough contextual knowledge, resulting in difficulties in attaining trust and satisfaction from the user. Addressing these gaps is key to unlocking the potential of generative AI in building seamless shopping experiences. Using the Natural Conversation Framework (NCF), this paper evaluates the performance gaps and recommends how generative AI can create world-class shopping assistants. By combining conversational UX principles with a holistic measurement framework, this study provides a structured way to improve reliability, personalization, and conversational depth. This study incorporates a blend of technical and user-centric metrics, such as product accuracy, latency, user engagement, and satisfaction; this provides us with a holistic view of a conversation AI system’s performance. Beyond mapping challenges, this study elaborates on the path towards scalable human-centered conversational agents. This study also illustrates how to design shopping assistants with interaction patterns that are scaffolding for a centralized technical architecture to drive long-term engagement and trust. This work has practical implications for researchers and builders aspiring to harness generative AI for e-commerce applications.