استخراج قوانین انجمن ارزش های مشتری از طریق یک روش داده کاوی با استفاده از مدل بهبود یافته : مطالعه تجربی
|کد مقاله||سال انتشار||تعداد صفحات مقاله انگلیسی||ترجمه فارسی|
|2596||2011||7 صفحه PDF||سفارش دهید|
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|شرح||تعرفه ترجمه||زمان تحویل||جمع هزینه|
|ترجمه تخصصی - سرعت عادی||هر کلمه 90 تومان||8 روز بعد از پرداخت||369,000 تومان|
|ترجمه تخصصی - سرعت فوری||هر کلمه 180 تومان||4 روز بعد از پرداخت||738,000 تومان|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Expert Systems with Applications, Volume 38, Issue 3, March 2011, Pages 1716–1722
This paper proposes a new procedure and an improved model to mine association rules of customer values. The market of online shopping industry in Taiwan is the research area. Research method adopts Ward’s method to partition online shopping market into three markets. Customer values are refined from an improved RFMDR model (based on RFM/RFMD model). Supervised Apriori algorithm is employed with customer values to create association rules. These effective rules are suggested to apply on a customized marketing function of a CRM system for enhancing their customer values to be higher grades.
The objective of this paper is to propose a new procedure for mining association rules of customer value. That is, to transfer and refine customer value to be type of rules. Through mined association rules, businesses can easily to perform customized marketing project, as well as be leaded in customer relationship management for enhancing customer values. The Jupiter Research indicated that 71% of Internet users will be engaged in shopping activities in 2010, online retailers will spend about 1440 billion US dollars on cost of trading. However, conclusion of this report was: online consumers need more discount and free shipping and reverse logistics fees (Jupiter Media, 2006). However, in accordance with estimates of MIC/Nelson Media Research online shopping market was approximately 8 billion US dollars in 2008, compare with 2007, its growth rate was 32.3%. The share of B2C market was approximately 4 billion US dollars; C2C market was approximately 3.3 billion US dollars. In addition, the expected market will reach to 10 billion US dollars in the end of 2009. In 2008, two categories of clothing (includes shoes, bags, and adornments) and cosmetics (includes cosmetics and facial/skin care products) for online shopping market were growth rapidly and whose Compound Annual Growth Rates were 88% and 49% (http://www.cyberone.tw, 2009). Thus, experienced consumers of online shopping are focused to be the research objects. The trends of Taiwan online shopping market are: trading (paying and receiving) in convenient chain stores, group purchasing, extending trial period, and free shipping and reverse shipping. Those factors of trend caused profit decreasing and trade cost increasing (Chiang, 2009). Nevertheless, how to enhance customer value will be a principal goal on electronic marketing strategy of Internet-Shopping websites.
نتیجه گیری انگلیسی
The paper proposes a new procedure for mining Customer Values, which can be transferred to be a form of association rules. This paper applies Ward’s method on market of online shopping in Taiwan, which is divided into three markets: Practical Oriented, Additional Cost, and Enjoy Shopping. Secondly, discrimination analysis is applied on the three markets and the differences of means for each market are under standard value. Finally, Supervised Apriori algorithm is applied on RFMDR variables to create five association rules. Customer values of online shopping markets are identified clearly via variable D and T in Table 4.3. Customer values are different between observations 104 and 105, their RFMD values are the same as: H-H-H-H, but R (return times) value of observation 105 is L, which is lower than observation 104. Thus, observation 104 has a better customer value than observation 105. However, RFM/RFMD model has improved by RFMDR model. Furthermore, according to Table 4.3, products of F × M for observations 107 and 501 are similar, and their D and R variable values are just different, therefore, R variable will be the judge for higher customer value. The higher one is observation 107.