Study of a structure analysis method for customer loyalty based on text data

Kenta Mikawa*, Tsutomu Takahashi, Masayuki Goto

*この研究の対応する著者

研究成果: Article査読

1 被引用数 (Scopus)

抄録

Recently, marketing activities are gradually shifting from making new customers to keeping current customers because of the intensifying market competition. Hence, keeping customers is related to improving customer satisfaction and customer loyalty. Ultimately keeping a customer means improving of lifetime value. In addition, it has been found that there are many effects for the company. Therefore, companies aiming to improve their profits need to focus on the components of customer loyalty. However, it is difficult to specify those factors because the structure of customer loyalty is very complex and its composition differs from customer to customer. This research uses a methodology interview and free-ended questionnaire data to map the customer loyalty structure. In order to clarify constitutes customer loyalty, the method of morphological analysis as the way of natural language techniques is introduced. This is used because there is much free-ended text data that companies can accumulate easily as the result of developing information technologies. Using these data, text mining can be applied, which is one of the important ways of obtaining information for CRM(customer relationship management). Through analysis, using the concept of text mining and the introduction of knowledge of the field in the study of customer loyalty, we find the structure of loyalty and consider how to improve these assumptions. Principal component analysis using the structure extracted from the text type comments of customers and vector spaces made by the structure and word frequency in each text gives us various findings about customer loyalty.

本文言語English
ページ(範囲)182-192
ページ数11
ジャーナルJournal of Japan Industrial Management Association
58
3
出版ステータスPublished - 2007 10 29
外部発表はい

ASJC Scopus subject areas

  • 戦略と経営
  • 経営科学およびオペレーションズ リサーチ
  • 産業および生産工学
  • 応用数学

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