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Manufacturers and service providers need new tools to leverage the value of the digital Voice-of-Customer (VoC). These unstructured and disorganized data need ad-hoc approaches for their analysis and interpretation. In this view, this article proposes an innovative methodology aiming at classifying the Key-Attributes (KA) of products and services that may influence customer (dis)satisfaction. The proposed methodology relies on the analysis of digital VoC to extract relevant information for classifying key-attributes. A novel tool called KA-VoC Map is at the basis of the proposed classification. The KA-VoC Map combines two dimensions of analysis: the extent and the way a Key-Attribute is discussed within the digital VoC. The methodology classifies KAs into six categories: obstacles, frictions, indifferent, sleeping beauties, promises, and delights. For each category, the most appropriate management strategy is also suggested. Finally, an empirical study is provided to illustrate the effectiveness of the proposed method.

KA-VoC Map: Classifying product Key-Attributes from digital Voice-of-Customer