Identifying Service Opportunities Based on Outcome-Driven Innovation Framework and Deep Learning: A Case Study of Hotel Service

• Published in: Sustainability Vol. 13; no. 1; p. 391
• Main Authors: Nam, Sunghyun, Yoon, Sejun, Raghavan, Nagarajan, Park, Hyunseok
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.01.2021
• Subjects: Compact disc players; Customer services; Decomposition; Deep learning; Job classification; Music; Neural networks; Semantics; Subject specialists; Sustainability; Text categorization
• ISSN: 2071-1050; 2071-1050
• DOI: 10.3390/su13010391

This research proposes a data-driven systematic method to discover service opportunities in a specific service sector. Specifically, the method quantitatively identifies the important but unsatisfied customer needs by analyzing online review data. To represent customer needs in a structured form, the job-to-be-done-based customer outcomes are adopted from the outcome-driven innovation (ODI) framework. Therefore, job-to-be-done information is extracted from the review data and is transformed into customer outcomes. The outcomes having high service opportunities are selected by metrics for quantifying the importance and satisfaction score of the outcomes. This paper conducted an empirical study for hotel service using relevant review data. The results show that the method can identify customer needs in hotel service—e.g., maximizing safety to pay price/deposit, and maximizing possibility to avoid waiting at lobby—and objectively prioritize strategic directions for service innovation. Therefore, the proposed method can be used as an intelligent tool for the effective development of a business strategy.