طبقه بندی تقسیم بندی ارزش مشتری از طریق مدل RFM و نظریه RS

Data mining is a powerful new technique to help companies mining the patterns and trends in their customers data, then to drive improved customer relationships, and it is one of well-known tools given to customer relationship management (CRM). However, there are some drawbacks for data mining tool, such as neural networks has long training times and genetic algorithm is brute computing method. This study proposes a new procedure, joining quantitative value of RFM attributes and K-means algorithm into rough set theory (RS theory), to extract meaning rules, and it can effectively improve these drawbacks. Three purposes involved in this study in the following: (1) discretize continuous attributes to enhance the rough sets algorithm; (2) cluster customer value as output (customer loyalty) that is partitioned into 3, 5 and 7 classes based on subjective view, then see which class is the best in accuracy rate; and (3) find out the characteristic of customer in order to strengthen CRM. A practical collected C-company dataset in Taiwan’s electronic industry is employed in empirical case study to illustrate the proposed procedure. Referring to [Hughes, A. M. (1994). Strategic database marketing. Chicago: Probus Publishing Company], this study firstly utilizes RFM model to yield quantitative value as input attributes; next, uses K-means algorithm to cluster customer value; finally, employs rough sets (the LEM2 algorithm) to mine classification rules that help enterprises driving an excellent CRM. In analysis of the empirical results, the proposed procedure outperforms the methods listed in terms of accuracy rate regardless of 3, 5 and 7 classes on output, and generates understandable decision rules.

Due to the complication and diversification of business operation, information of company is essential and vital forces for advantage competition and going-concern. Particularly, the growing of information technology (IT) in rapid changing and competitive environment today motivates the activity of transaction, which increasingly facilities the markets competition. Based on this relationship, information serves as central to face the opportunities and challenges of day-to-day operation for companies. It is very difficult for companies that strengthen business’s competitive advantage if information only becomes to support the functions within company when facing to the heavy challenges coming from outsides surroundings. Thus, how to enhance the market competitive power for companies is an interesting issue because of the more the competitive power, the more the probability for going-concern. The key point gaining profit of companies is to integrate the upstream members of supply chain via an effective IT in order to reduce cost, and reinforce the downstream customer relationships via an excellent CRM in order to gain more profit. CRM becomes the focal point of company profits and more and more important for companies because customers are main resources of profits. Therefore, this study insists on that an excellent CRM with customers for companies is a critical for gaining more profit.

This study has proposed a procedure which joins RFM attributes and K-means algorithm into rough sets theory (the LEM2 algorithm) not only to enhance classification accuracy but also to extract classification rules for achieving an excellent CRM for enterprises. Additionally, it can effectively improve some drawbacks of data mining tools. To demonstrate the proposed procedure, this study employs a practical collected C-company dataset in Taiwan’s electronic industry, which include 401 instances, as experimental dataset. From Table 9, the proposed procedure outperforms the methods listed in terms of accuracy rate regardless of 3, 5 and 7 classes on output, and the output of proposed procedure is understandable decision rules. The output of the proposed procedure is a set of easily understandable decision rules which make C-company easier to interpret and know that which customer is more important and which customer is more contribution to revenue for enterprises. Furthermore, this proposed procedure based on RFM attributes and K-means algorithm can help C-company to classify objectively the segmentation of customers. Based on these excellent results of experiment, this study believes to aid C-company focusing the target customers and then gaining maximize profits with win–win situation for company–customer. With the findings in this empirical case study, we positively conclude that the proposed procedure is more efficient than the listed methods on classifying the segmentation of customer value via RFM attributes, K-means algorithm and RS theory. For future research, other types of datasets can be considered to assess this procedure such as financial industry or healthcare industry even services industry, or other models of customer value analysis (except in RFM model) can be used as attributes for classifying the segmentation of customer. In general, we hope that the proposed procedure can become generalization not specialization for all datasets.