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

Author

Listed:
  • Junegak Joung

    (Department of Industrial and Management Engineering, Pohang University of Science and Technology, 77 Cheongam-ro, Nam-gu, Pohang 790-784, Korea)

  • Kiwook Jung

    (Department of Industrial and Management Engineering, Pohang University of Science and Technology, 77 Cheongam-ro, Nam-gu, Pohang 790-784, Korea)

  • Sanghyun Ko

    (Korea Institute of Defense Analyses, 37 Hoegi-ro, Dongdaemun-gu, Seoul 02455, Korea)

  • Kwangsoo Kim

    (Department of Industrial and Management Engineering, Pohang University of Science and Technology, 77 Cheongam-ro, Nam-gu, Pohang 790-784, Korea)

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.

Suggested Citation

  • Junegak Joung & Kiwook Jung & Sanghyun Ko & Kwangsoo Kim, 2018. "Customer Complaints Analysis Using Text Mining and Outcome-Driven Innovation Method for Market-Oriented Product Development," Sustainability, MDPI, vol. 11(1), pages 1-14, December.
    • Handle: RePEc:gam:jsusta:v:11:y:2018:i:1:p:40-:d:192287
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    References listed on IDEAS

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    1. Shawn Mankad & Hyunjeong Spring Han & Joel Goh & Srinagesh Gavirneni, 2016. "Understanding Online Hotel Reviews Through Automated Text Analysis," Post-Print hal-02311939, HAL.
    2. King, Robert Allen & Racherla, Pradeep & Bush, Victoria D., 2014. "What We Know and Don't Know About Online Word-of-Mouth: A Review and Synthesis of the Literature," Journal of Interactive Marketing, Elsevier, vol. 28(3), pages 167-183.
    3. Joohyung Lim & Sungchul Choi & Chiehyeon Lim & Kwangsoo Kim, 2017. "SAO-Based Semantic Mining of Patents for Semi-Automatic Construction of a Customer Job Map," Sustainability, MDPI, vol. 9(8), pages 1-17, August.
    4. Hyejong Min & Junghwan Yun & Youngjung Geum, 2018. "Analyzing Dynamic Change in Customer Requirements: An Approach Using Review-Based Kano Analysis," Sustainability, MDPI, vol. 10(3), pages 1-17, March.
    5. Joung, Junegak & Kim, Kwangsoo, 2017. "Monitoring emerging technologies for technology planning using technical keyword based analysis from patent data," Technological Forecasting and Social Change, Elsevier, vol. 114(C), pages 281-292.
    6. Li, Baibing & Martin, Elaine B. & Morris, A. Julian, 2002. "On principal component analysis in L1," Computational Statistics & Data Analysis, Elsevier, vol. 40(3), pages 471-474, September.
    7. Decker, Reinhold & Trusov, Michael, 2010. "Estimating aggregate consumer preferences from online product reviews," International Journal of Research in Marketing, Elsevier, vol. 27(4), pages 293-307.
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