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We introduce a new importance-performance analysis (IPA) methodology while making use of direct service experience perceptions represented by online reviews with numerical ratings. The proposed IPA, which we call the text analytics-based IPA (TAIPA), allows the real-time calculation of importance using the probability distribution of word frequency via the latent Dirichlet allocation (LDA) application to online reviews, and of performance using numerical rating values. The importance is also adjusted with the help of a sentiment analysis of online reviews to provide more precise measurements for service experience perceptions. To ensure an evaluation of the entire service process, we employ service encounters, in which service experiences occur and thus most customer perceptions are created, as a set of attributes composed of LDA topics that contain direct perceptions of service experiences. We investigate statistical correlations between TAIPA calculations and typical benchmarks of firm performance in the air-transport industry to verify how effective the proposed TAIPA is with respect to the degree that customer satisfaction is represented. As a primary result, TAIPA is more effective than comparison targets in that it shows stronger correlations with firm performance. TAIPA is specialized in determining which service step (i.e., a one-to-one relationship with a service encounter) needs to be improved. Moreover, TAIPA is flexible in considering multiple competitors.
Nam et al. (2019) studied this question.