Revealing the dual importance and Kano type of attributes through customer review analytics

Abstract

Identifying which attributes of a product are important to customers and clarifying how the attributes affect customer satisfaction are critical for a firm to survive and succeed in the market. To assist in characterizing the impacts of various attributes and prioritizing the attributes for design and marketing purposes, this paper proposes a novel review-analytics framework, called importance-Kano (I-Kano) analysis. I-Kano analysis holistically assesses the impacts of various attributes from three different perspectives that potentially may conflict with each other, i.e., appearance (stated importance), significance (derived importance), and Kano type. By fusing term-frequency and sentiment analyses of online reviews with conjoint analysis, the I-Kano analysis simultaneously identifies the dual importance (appearance and significance) and Kano type of an attribute. As the final deliverable of the I-Kano analysis, a new visualization scheme, called the I-Kano matrix, is proposed, which is the first attempt to integrate the dual importance and Kano type of multiple attributes in a single chart. The I-Kano matrix facilitates an intuitive interpretation of the multidimensional impacts of various attributes and supports the aggregation and comparison of the results from different market segments. Through the I-Kano analysis, the attributes of great importance in a market segment, which are useful for developing products and planning marketing promotions, can be identified. In addition, the I-Kano analysis can identify the segments of the market in which a certain attribute has greater relative importance, which is helpful in the design differentiation, targeting, and differentiated marketing of products. To demonstrate and validate the I-Kano analysis, an illustrative case study is described with an example of online hotel reviews. © 2022 Elsevier Ltd