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

Abstract

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.