AI- Enhanced Kano Model for Data-driven Customer Analytics

Potra Sabina Alina, Tărăbîc Alexandru-Vlad, Bogdanovici Lavinia and Sima Denisa ()
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Potra Sabina Alina: Research Centre in Engineering and Management, Management Department, Politehnica University Timișoara, Timisoara, Romania
Tărăbîc Alexandru-Vlad: Politehnica University Timișoara, Timisoara, Romania
Bogdanovici Lavinia: Politehnica University Timișoara, Timisoara, Romania
Sima Denisa: Research Centre in Engineering and Management, Politehnica University Timișoara, Timisoara, Romania

Proceedings of the International Conference on Business Excellence, 2025, vol. 19, issue 1, 1062-1071

Abstract: Initially created four decades ago, the Kano model has been extensively used to synchronize product attributes with consumer requirements. Being subjective in nature but offering a subconscious Voice of the Customer (VoC) for managerial decision making, it has been successfully implemented in broad areas of research and practical case studies. From product or service improvements to new design ideas, it proved useful and at the same time attracted criticism. Scholars have proposed over the years a great number of transformations and refinements to the Kano framework, integrating different quality tools with Kano or improving the evaluation stage of the model. Nevertheless, the confusion in use, the great specialized knowledge needed, or the number of statistical tests has discouraged practitioners to adhere to these new approaches. Artificial Intelligence (AI) has recently proven to be helpful in many domains if used properly. The present paper suggests a new methodology to enhance the Kano model results with the use of AI algorithms in a simple and user-friendly approach. In this manner, the AI-Kano method could open doors for a broader and more accurate usage in a variety of domains.

Keywords: Kano model; Artificial Intelligence; adaptive feedback; customer requirements; customer predictive models; Machine Learning (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:vrs:poicbe:v:19:y:2025:i:1:p:1062-1071:n:1007

DOI: 10.2478/picbe-2025-0084

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