Customer segmentation is a key element for target marketing or market segmentation. Although there are quite a lot of ways available for segmentation today, most of them emphasize numeric calculation instead of commercial goals. In this study, we propose an improved segmentation method called transaction pattern based customer segmentation with neural network (TPCSNN) based on customer’s historical transaction patterns. First of all, it filters transaction data from database for records with typical patterns. Next, it reduces inter-group correlation coefficient and increases inner cluster density to achieve customer segmentation by iterative calculation. Then, it utilizes neural network to dig patterns of consumptive behaviors. The results can be used to segment new customers. By this way, customer segmentation can be implemented in very short time and costs little. Furthermore, the results of segmentation are also analyzed and explained in this study.
Consumer market changes rapidly without any settled logic. In most occasions, you can find all kinds of demands in it. Customers’ requirement will never be satisfied merely by one or two products. However, excessive products can be a burden or risk to the company’s operation (Guha et al., 2000, Hagan et al., 1996, Lee et al., 2000 and Schiffman and Kanuk, 2000). Therefore, in order to satisfy various customer requirements within company’s capacity, we need to split consumer market into several segmentations and find out appropriate marketing strategies for them (Changchien and Kuo, 2004, Changchien and Lu, 2001, Changchien et al., 2001, Dennis etal., 2003, Kotler, 1994 and Kotler and Armstrong, 1997).
The spirit of strategic marketing proposed by Kotler is STP: segmentation, targeting, and positioning (Kotler, 1994 and Kotler and Armstrong, 1997). Based on some customer diversities, the complicated market in the reality can be separated into several small markets with similar properties. Among them, the companies can find their target markets and the positions. Such strategy is a golden rule even in today’s business. In the recent years, Kotler proposed a new brand marketing mode create communicate deliver value target profit (CCDVTP). It tries to create new communication tunnels and deliver brand values. Then it conducts marketing with specific targets, and finally achieves profits. To stipulate for a marketing strategy, market segmentation is the first step (Kotler, 1994 and Kotler and Armstrong, 1997).
Today, there are a lot of market segmentation methods available, but most of them are based on the existing segmentation methods, such as K-means, density-based spatial clustering of applications with noise (DBSCAN) and so on (Aldenderfer and Blashfied, 1984, Bezdek and Pal, 1998, Ester et al., 1996, Filippone et al., 2008, Guha et al., 2000, Guldemir and Senguar, 2006, Gunter and Bunke, 2003, Kumar and Patel, 2007 and Liu and Samal, 2002). The users have to choose an appropriate clustering method based on the goals to be resolved or the characteristics of the database. After the decision, the users need to find suitable parameters for the clustering. It requires the users to be very familiar with the problem or the data characteristics to find the parameters and obtain the optimized result. As a matter of fact, this is a mission impossible for the normal companies. Therefore, in this study, we try to propose an easy and understandable method for the normal users, which can implement the segmentation rapidly and correctly. By this way, the business operation can be supported by the theory. Besides, most of the existing methods are not designed for business purpose. As a result, some adjustments to the data are required during the application. Such adjustment may make the results away from the target problems.
In 2004, ChangChien and Kuo proposed a customer segmentation method called transaction pattern based customer segmentation (TPCS) (Changchien & Kuo, 2004). Their customer segmentation method covers both marketing and business purposes. They utilize customers’ historical transaction data and group the customers by similar transaction patterns. Meanwhile, they put marketing and business purposes into consideration. However, their method is incapable of analyzing or explaining the segmentation results. In this study, we try to improve the TPCS method and add extraction mechanics for the transaction data to the original method, analyze the segmentation result. Furthermore, neural network technology is adopted in our improvement. It enables the users to obtain new customer segmentation quickly.
Here is a brief structure of this paper. Section 2 is a literature review, which introduces TPCS method and other relevant technologies. In Section 3, improved segmentation method proposed by us is introduced. In Section 4, we try to evaluate the improvements by simulation and actual data. The evaluation can be validated by the real system. Section 5 is a summary.
