AI Feel You: Customer Experience Assessment via Chatbot Interviews

Research output: Contribution to journalArticlepeer-review

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. We argue that the application of relevant artificial intelligence (AI) technology could help address this challenge. Employing interactively prompted narrative storytelling, we 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 we developed for studying CE feelings, an 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, we were able to compare extracted sentiment polarities against established measurement scales and empirically validate our novel approach.

Findings – We demonstrate that SA can effectively extract CE feelings from primary chatbot data. Our 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 foster immersive and engaging relationships with customers. Our 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.

Bibliographical metadata

Original languageEnglish
JournalJournal of Service Management
Publication statusAccepted/In press - 21 Jun 2020