Customer Complaints Analysis Using Text Mining and Outcome-Driven Innovation Method for Market-Oriented Product Development

Abstract

The rapid increase in the quantity of customer data has promoted the necessity to analyse these data. Recent progress in text mining has enabled analysis of unstructured text data such as customer suggestions, customer complaints and customer feedback. Much research has been attempted to use insights gained from text mining to identify customer needs to guide development of market-oriented products. However, the previous research has a drawback that identifies limited customer needs based on product features. To overcome the limitation, this paper presents application of text mining analysis of customer complaints to identify customers' true needs by using the Outcome-Driven Innovation (ODI) method. This paper provides a method to analyse customer complaints by using the concept of job. The ODI-based analysis contributes to identification of customer latent needs during the pre-execution and post-execution steps of product use by customers that previous methods cannot discover. To explain how the proposed method can identify customer requirements, we present a case study of stand-type air conditioners. The analysis identified two needs that experts had not identified but regarded as important. This research helps to identify requirements of all the points at which customers want to obtain help from the product.

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

“…Generating various but reliable outcomes from different job map steps with minimizing cost is one of the most important demands in the ODI framework. Even though previous studies [17,18] could not deal with this issue, this paper developed the method that fulfills the demand. Fourth, since the proposed method can automatically generate outcome statements, the method can assess potential opportunities for the outcomes without any expensive interview process.…”

“…However, since this research focuses on product opportunities using patent data, the analytic process structure and data are basically different from service-related research. Joung et al [18] applied ODI to detect customer complaints in end-user products. This research extracts keywords and functional information from webpages and classifying them into Universal job map steps using semantic similarity analysis.…”

Deductions from a Sub-Saharan African Bank’s Tweets: A sentiment analysis approach

“…In financial economics, information seeking is very useful for day-to-day activities with regards to pre and post-financial transactions particularly among banking institutions and other financial outlets in general. At the same time, this acquired information from the advent FinTech help particularly these banks to effectively and efficiently distribute resources in markets in which decisions are made under uncertainty (Joung et al, 2019). Therefore, financial decisions by banks must often consider future events, whether those be related to new products/services, individual stocks, portfolios, or the market as a whole so far as information seeking and knowledge management are concerned.…”

Strategies for Marketing Really New Products to the Mass Market: A Text Mining-Based Case Study of Virtual Reality Games

“…In a comprehensive review, Berger et al [37] discuss how text can be useful in marketing research. One major focus of previous studies using web extraction has been users' behavior and their opinions [38][39][40], both of which leave traces on the Internet, e.g., analyzing customers' complaints to generate insights on their needs and, thus, guide product development [41]. Researchers have demonstrated using text from search engines to forecast sales of new products [42].…”