AI feel you: customer experience assessment via chatbot interviews

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Abstract:

Purpose: While customer experience (CE) is recognized as a critical determinant of business success, both academics and managers are yet to find a means to gain a comprehensive understanding of CE cost-effectively. The authors argue that the application of relevant AI technology could help address this challenge. Employing interactively prompted narrative storytelling, and the authors investigate the effectiveness of sentiment analysis (SA) on extracting valuable CE insights from primary qualitative data generated via chatbot interviews. Design/methodology/approach: Drawing on a granular and semantically clear framework for studying CE feelings, an artificial intelligence (AI) augmented chatbot was designed. The chatbot interviewed a crowdsourced sample of consumers about their recalled service experience feelings. By combining free-text and closed-ended questions, the authors were able to compare extracted sentiment polarities against established measurement scales and empirically validate our novel approach. Findings: The authors demonstrate that SA can effectively extract CE feelings from primary chatbot data. This findings also suggest that further enhancement in accuracy can be achieved via improvements in the interplay between the chatbot interviewer and SA extraction algorithms. Research limitations/implications: The proposed customer-centric approach can help service companies to study and better understand CE feelings in a cost-effective and scalable manner. The AI-augmented chatbots can also help companies to foster immersive and engaging relationships with customers. This study focuses on feelings, warranting further research on AI's value in studying other CE elements. Originality/value: The unique inquisitive role of AI-infused chatbots in conducting interviews and analyzing data in realtime, offers considerable potential for studying CE and other subjective constructs.