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Text Mining as a Supporting Process for VoC Clarification

Author

Listed:
  • Aysun Kapucugil İkiz
  • Güzin Özdağoğlu

Abstract

In product development, the foremost issue is to identify "what" the customers’ expectations would be from the product. As a promising approach to the product development, Quality Function Deployment also gives crucial importance to the collection and analysis of Voice of the Customer (VoC) to deduce true customer needs. Data sources of VoC include surveys, interviews, focus groups, gemba visits as well as customer reviews which can be collected through call centers, internet homepages, blogs, and microblogs in social networks. Customers’ verbatim or reviews obtained from these resources require more detailed extraction to define them as the positive restatement of problems, opportunities or image issues independent of the product or the solution. Basically, this clarification process is a content analysis in which the developers usually seek to extract and classify the spoken-unspoken customer needs from VoC. This labor-intensive manual approach brings subjectivity to the analysis and can take so much time in the case of having condensed and large-volume text data. During the past decade, the field of text mining has enabled to solve these kinds of problems efficiently by unlocking hidden information and developing new knowledge; exploring new horizons; and improving the research process and quality. This paper utilizes a particular algorithm of text clustering, a recently popular field of interest in text mining, to analyze VoC and shows how text mining can also support the clarification process for better extraction of customer needs. Practical implications are presented through analysis of online customer reviews for a product.

Suggested Citation

  • Aysun Kapucugil İkiz & Güzin Özdağoğlu, 2015. "Text Mining as a Supporting Process for VoC Clarification," Alphanumeric Journal, Bahadir Fatih Yildirim, vol. 3(1), pages 25-40, June.
    • Handle: RePEc:anm:alpnmr:v:3:y:2015:i:1:p:25-40
    DOI: http://dx.doi.org/10.17093/aj.2015.3.1.5000105108
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    1. K. Coussement & D. van den Poel, 2008. "Integrating the voice of customers through call center emails into a decision support system for churn prediction," Post-Print hal-00788086, HAL.
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    More about this item

    Keywords

    Quality Function Deployment (QFD); Text Clustering; Text Mining; Voice of the Customer (VoC);
    All these keywords.

    JEL classification:

    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • C88 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Other Computer Software
    • M11 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration - - - Production Management
    • M15 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration - - - IT Management
    • M31 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - Marketing

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