IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v11y2018i1p40-d192287.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/11/1/40/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/11/1/40/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. Shawn Mankad & Hyunjeong Spring Han & Joel Goh & Srinagesh Gavirneni, 2016. "Understanding Online Hotel Reviews Through Automated Text Analysis," Post-Print hal-02311939, HAL.
    7. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jason Jihoon Ree & Cheolhyun Jeong & Hyunseok Park & Kwangsoo Kim, 2019. "Context–Problem Network and Quantitative Method of Patent Analysis: A Case Study of Wireless Energy Transmission Technology," Sustainability, MDPI, vol. 11(5), pages 1-18, March.
    2. Jie Hu & Shaobo Li & Jianjun Hu & Guanci Yang, 2018. "A Hierarchical Feature Extraction Model for Multi-Label Mechanical Patent Classification," Sustainability, MDPI, vol. 10(1), pages 1-22, January.
    3. Mafael, Alexander & Gottschalk, Sabrina A. & Kreis, Henning, 2016. "Examining Biased Assimilation of Brand-related Online Reviews," Journal of Interactive Marketing, Elsevier, vol. 36(C), pages 91-106.
    4. Chiehyeon Lim & Paul P. Maglio, 2018. "Data-Driven Understanding of Smart Service Systems Through Text Mining," Service Science, INFORMS, vol. 10(2), pages 154-180, June.
    5. Lee, Changhun & Lim, Chiehyeon, 2021. "From technological development to social advance: A review of Industry 4.0 through machine learning," Technological Forecasting and Social Change, Elsevier, vol. 167(C).
    6. Plat, Richard, 2009. "Stochastic portfolio specific mortality and the quantification of mortality basis risk," Insurance: Mathematics and Economics, Elsevier, vol. 45(1), pages 123-132, August.
    7. Kondylis, Athanassios & Whittaker, Joe, 2008. "Spectral preconditioning of Krylov spaces: Combining PLS and PC regression," Computational Statistics & Data Analysis, Elsevier, vol. 52(5), pages 2588-2603, January.
    8. Rosillo-Díaz, Elena & Muñoz-Rosas, Juan Francisco & Blanco-Encomienda, Francisco Javier, 2024. "Impact of heuristic–systematic cues on the purchase intention of the electronic commerce consumer through the perception of product quality," Journal of Retailing and Consumer Services, Elsevier, vol. 81(C).
    9. Ouyang, Yaofu & Li, Peng, 2018. "On the nexus of financial development, economic growth, and energy consumption in China: New perspective from a GMM panel VAR approach," Energy Economics, Elsevier, vol. 71(C), pages 238-252.
    10. Paschalis Arvanitidis & Athina Economou & Christos Kollias, 2016. "Terrorism’s effects on social capital in European countries," Public Choice, Springer, vol. 169(3), pages 231-250, December.
    11. Rizvi, Syed Kumail Abbas & Rahat, Birjees & Naqvi, Bushra & Umar, Muhammad, 2024. "Revolutionizing finance: The synergy of fintech, digital adoption, and innovation," Technological Forecasting and Social Change, Elsevier, vol. 200(C).
    12. M. Narciso, 2022. "The Unreliability of Online Review Mechanisms," Journal of Consumer Policy, Springer, vol. 45(3), pages 349-368, September.
    13. Teerachai Amnuaylojaroen & Pavinee Chanvichit, 2024. "Historical Analysis of the Effects of Drought on Rice and Maize Yields in Southeast Asia," Resources, MDPI, vol. 13(3), pages 1-18, March.
    14. Anahita Nodehi & Mousa Golalizadeh & Mehdi Maadooliat & Claudio Agostinelli, 2025. "Torus Probabilistic Principal Component Analysis," Journal of Classification, Springer;The Classification Society, vol. 42(2), pages 435-456, July.
    15. Kim, Kun & Park, Oun-joung & Yun, Seunghyun & Yun, Haejung, 2017. "What makes tourists feel negatively about tourism destinations? Application of hybrid text mining methodology to smart destination management," Technological Forecasting and Social Change, Elsevier, vol. 123(C), pages 362-369.
    16. Weili Duan & Bin He & Daniel Nover & Guishan Yang & Wen Chen & Huifang Meng & Shan Zou & Chuanming Liu, 2016. "Water Quality Assessment and Pollution Source Identification of the Eastern Poyang Lake Basin Using Multivariate Statistical Methods," Sustainability, MDPI, vol. 8(2), pages 1-15, January.
    17. Adele Ravagnani & Fabrizio Lillo & Paola Deriu & Piero Mazzarisi & Francesca Medda & Antonio Russo, 2024. "Dimensionality reduction techniques to support insider trading detection," Papers 2403.00707, arXiv.org, revised May 2024.
    18. Reinhold Decker, 2014. "Real-Time Analysis of Online Product Reviews by Means of Multi-Layer Feed-Forward Neural Networks," International Journal of Business and Social Research, MIR Center for Socio-Economic Research, vol. 4(11), pages 60-70, November.
    19. Tingting Song & Jinghua Huang & Yong Tan & Yifan Yu, 2019. "Using User- and Marketer-Generated Content for Box Office Revenue Prediction: Differences Between Microblogging and Third-Party Platforms," Service Science, INFORMS, vol. 30(1), pages 191-203, March.
    20. Azer, Jaylan & Anker, Thomas & Taheri, Babak & Tinsley, Ross, 2023. "Consumer-Driven racial stigmatization: The moderating role of race in online consumer-to-consumer reviews," Journal of Business Research, Elsevier, vol. 157(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:11:y:2018:i:1:p:40-:d:192287. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.