A recommender system to avoid customer churn: A case study
Introduction
With wireless formats becoming more and more mature, wireless equipment, such as Access Point (AP), wireless network cards, etc., are getting less expensive, and mobile computing devices, such as notebooks and PDAs, becoming more popular. According to the Dell’Oro Marketing Survey Report, the market of the wireless local area network (WLAN) grew steadily about 40% year by year, from 2003 to 2006. In 2006, the net value of the global wireless networking already broke 100B dollars. The WLAN, as an extension and supplement to wired networking, will continue to be in great demand for the next generation.
When a wireless network company changes the strategy from product-based to customer-based, the customer relationship management (CRM) becomes very important. As the 80/20 rule goes, eighty percent of benefit comes from twenty percent of customers, so it is essential that companies understand customer need and develop suitable CRM strategies in order to ensure customer retention, loyalty, and satisfaction. For this purpose, companies have to collect information about customers and analyze their behavior patterns, and the ability to integrate and utilize such information effectively, therefore, is crucial to their CRM performance.
However, we have to realize customer’s behavior before we can do an excellent CRM job. To understand customer’s behavior, we need to collect all kinds of information about customers and analyze the behavior patterns of the customers to realize customer’s behavior. That would help us to adopt appropriate strategy to prevent customer loss. In fact, a regular trading system accumulated enormous amount of valuable data. Therefore, how to integrate and utilize those data would be a critical task. Data mart was the solution created especially for this issue (Demarest, 1994). Data mart is the subject to store all kinds of internal and external data for future analysis.
A recommender system is one that recommends useful information or suggests strategies that users might apply to achieve their goals. The system gives suggestions based on a given event, such as an error, or on observations of the user’s overall behavior. A simple example is a research engine that, when no results found for a query, suggests alternate keywords or queries that may achieve better results (Diamond Bullet, 2004). Recommender systems are widely used in the fields of E-commence, movies, music, books, and Web pages successfully.
Mooney and Roy (2000) suggest a content-based book recommending system that utilizes information extraction and a machine-learning algorithm for text categorization. Miller, Albert, Lam, Konstan, and Riedl (2003) propose the recommender system Movie Lens, which builds on and extends a movie recommendation (http://movielens.umn.edu) that provides movie, DVD, and VHS video recommendations, along with a search capability. Chen and Chen (2001) design the Music Recommender System (MRS), which enables a website to provide personalized music recommendation service based on music data grouping and user interest. The Group Lens recommender system helps users browse among articles in Usenet news (Konstan et al., 1997, Resnick et al., 1994). Ringo allows users to get music recommendations online and connect with other music fans (Shardanand & Maes, 1995). Again, more successful applications can be mentioned at online food stores (Svensson, Laaksolahti, Höök, & Waern, 2000), online book stores such as Amazon.com (Linden, Smith, & York, 2003), etc.
Section snippets
Preliminary
Decision tree is a Data Mining technology used for classification and prediction. The tree graph represents the decision tree. First, one data enters root node of the tree, and then it is decided where child node will go. The process as above will repeat until the data reach to the leaf node.
The famous algorithms of decision tree are C4.5 (Interactive Dichotometer 3, Quinlan, 1983), CART (Classification and Regression Tree, Breiman, Friedman, Olshen, & Stone, 1984), and CHAID (Chi-square
Proposing a recommender system
Our system uses Visual BASIC on an IBM PC and includes four major modules: data mart, field selection, classification, and recommendation. Fig. 1 is a simplified architecture of the system. In the proposed system, users can gain recommendations from the system. Since the process is application-oriented, different applications may need different classification approaches as appropriate. For present purposes, the Decision Tree is employed as our classification function.
Experimental result
The experiment begins with an understanding the goals and needs of the business, transforms the knowledge obtained to questions, and then designs a preliminary plan for the goals. Our concern at this step is to get a deep understanding of the subject of our experiment, which is a wireless networking company. Specifically, the purpose is to learn about its source of operational income, methods of metering and collecting fees, the wiring areas, density and coverage rates in different areas,
Conclusion
We have used data mining to dig out useful information from the huge data about a wireless network company. The information so obtained has been analyzed, using the decision tree algorithm, so that we are able to come out with new marketing and promotion strategies. When we take actions, we will have to interact with customers and record all related feedbacks. Such information can help us reevaluate and improve our model. So there exists a constant need to understand and study the various
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