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MÜŞTERİ SESİNİN AYRIŞTIRILMASINI DESTEKLEYEN BIR SÜREÇ OLARAK METİN MADENCİLİĞİ

TEXT MINING AS A SUPPORTING PROCESS FOR VoC CLARIFICATION

Journal Name:

  • Alphanumeric Journal

Publication Year:

  • 2015

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Keywords (Original Language):

Author NameUniversity of AuthorFaculty of Author
Abstract (2. Language): 
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 “true” customer needs. Practical implications are presented through analysis of online customer reviews for a product.
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Abstract (Original Language): 
Ürün geliştirmede en başta gelen konu, müşterilerin üründen beklentilerinin ne olacağını belirlemektir. Ürün geliştirme için gelecek vaadeden bir yaklaşım olarak, Kalite Fonksiyon Göçerimi de, gerçek müşteri ihtiyaçlarını ortaya çıkarmak için Müşteri Sesinin toplanmasına ve analizine oldukça önem vermektedir. Müşteri Sesinin veri kaynaklarını anketler, mülakatlar, odak grupları, gemba ziyaretlerinin yanı sıra çağrı merkezlerinden, internet sayfalarından, web günlüklerinden (blog) ve sosyal ağlardaki mikro web günlüklerinden toplanabilen müşteri yorumları oluşturmaktadır. Bu kaynaklardan elde edilen müşteri ifadeleri veya yorumlarının, ürün ya da çözümden bağımsız problem, fırsat veya imaja yönelik konular bazında yeniden olumlu ifadeler şeklinde tanımlamak için daha detaylı ayrıştırılması gerekmektedir. Temel olarak, bu ayrıştırma süreci, geliştiricilerin genellikle müşteri sesinden dile getirilen ve getirilmeyen müşteri ihtiyaçlarını çıkarmaya ve sınıflandırmaya çalıştıkları bir içerik analizidir. Bu emek-yoğun manuel yaklaşım, analize öznellik getirmekte ve yoğun ve büyük hacimde metin verilerin varlığı durumunda çok fazla zaman alabilmektedir. Son on yılda, metin madenciliği alanı gizli bilgileri açığa çıkararak ve yeni bilgi geliştirerek, yeni ufuklar keşfederek, araştırma sürecini ve kalitesini iyileştirerek bu tür problemlerin etkin bir şekilde çözümüne olanak sağlamaktadır. Bu çalışma, müşteri sesini analiz etmek için, metin madenciliğinin son yıllarda popüler ilgi alanı haline gelen metin sınıflandırmaya yönelik özel bir algoritma kullanmakta ve “gerçek” müşteri ihtiyaçlarını daha doğru bir şekilde belirlemek için metin madenciliğinin ayrıştırma sürecini nasıl destekleyebileceğini göstermektedir. Uygulama açısından etkileri, bir ürüne ilişkin online müşteri yorumlarının analiziyle sunulmaktadır.
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38 Aysun KAPUCUGİL İKİZ, Güzin ÖZDAĞOĞLU / Alphanumeric Journal, 3(1) (2015) 025–040
Alphanumeric Journal
The Journal of Operations Research, Statistics, Econometrics and Management Information Systems
ISSN 2148-2225
httt://www.alphanumericjournal.com/
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