MOTIVATION Organizations spend large capital to establish and maintain customer relationship. The

Many marketers agree that by reducing customer’s to competitors defection by only 5 per cent, companies can improve profits by anywhere from 25 per cent to 95 per cent. There is no question this will be a great advantage and could benefit any retailer. It is for this very reason why consumer’s relationship marketing and using tools such as loyalty scheme is

Data mining is another concept closely associated with large databases such as clinical data repositories and data warehouses. However data mining like several other IT concepts means different things to different people. Health care application vendors may use the term data mining when referring to the user interface of the data warehouse or data repository. They may refer to the ability to drill down into data as data mining for example. However more precisely used data mining refers to a sophisticated analysis tool that automatically dis­ covers patterns among data in a data store. Data mining is an advanced form of decision support. Unlike passive query tools the data mining analysis tool does not require the user to pose individual specific questions to the database. Instead this tool is programmed to look for and extract patterns, trends and rules. True data mining is currently used in the business community for market­ ing and predictive analysis (Stair & Reynolds, 2012). This analytical data mining is however not currently widespread in the health care community.

Having data is not valuable but using data is. Analytic insights are changing the way corporates strategize and also redefining customer expectations. Analytics is the new differentiator between success and failure in the cut throat e-commerce and internet services based industry. The huge proportions of data generated from the increasing number of smart phones, the social networks and the ever more penetrating internet are automating customer centric marketing and other services. The idea is to predict what a customer may want to buy even before the customer realizes what they need. The techniques to achieve these results are broadly classified as Predictive Analytics.

Data Mining is an analytical process that primarily involves searching through vast amounts of data to spot useful, but initially undiscovered, patterns. The data mining process typically involves three major steps—exploration, model building and validation and finally, deployment.

Kudler is looking for ways to increase sales and customer satisfaction. To achieve this goal Kudler will use data mining tools to predict future trends and behaviors to allow them to make proactive, knowledge-driven decisions. Kudler’s marketing director has access to information about all of its customers: their age, ethnicity, demographics, and shopping habits. The starting point will be a data warehouse containing a combination of internal data tracking all customers contact coupled with external market data

Data mining uses computer-based technology to evaluate data in a database and identify different trends. Effective data mining helps researchers predict economic trends and pinpoint sales prospects. Data mining is stored in data warehouses, which are sophisticated customer databases that allow managers to combine data from several different organization functions.

Customer Relationship Management (CRM) software can help VTBC manage customer data to determine buying habits, coordinate its marketing strategy, forecast product sales, and interact quickly with customers. Vermont Teddy Bear Company’s marketing strategy is aimed at gift sales for Valentine’s Day, Mother’s Day, and Christmas. Their marketing strategy is ineffective during off peak times. Vermont Teddy Bear Company needs conduct market research and revise their marketing campaign to capture a broader customer base than just holiday gift shoppers. There is a need for a data warehouse to store the collected data and for data mining. Data mining can provide information on customer buying habits, trends and target potential

For years, companies have been relying on market research, data tracking, and data warehousing to help create marketing strategies. Marketing managers rely on this data to spot opportunities and problems and try to stay ahead of the competition.

An effective Customer Relationship Management (CRM) program can be used to identify, retain, satisfy and obtain customers by using technology to optimize strategies for understanding customers’ needs to manage business interactions with current, former, and prospective customers. Additionally, CRM also enables companies to maximize internal, external, marketing and customer service operations to better address the needs of the customer building a better relationship with customers that a more profitable. (Ahmad & Buttle, 2001)

An example of how data mining is conducted and used to benefit business can be explained in the following scenario:

Data mining can aid direct marketers by providing them with useful and accurate trends about

With data mining, a retailer could use point-of-sale records of customer purchases to send targeted promotions based on an individual's purchase history. By mining demographic data from comment or warranty cards, the retailer could develop products and promotions to appeal to specific customer segments.

In this paper, it will figure the benefits of data mining to the businesses when employing on predictive analytics to understand the behavior of customers, association finding into products sold to customers, web mining to find business knowledge from Web customers, and clustering to find related customer information. It will assess the reliability of the data mining algorithms, and to decide if they can be trusted and predict the errors they are likely to produce. It will analyze privacy concerns raised by the collection of personal data for mining purposes. It will give at least three examples where businesses have used prognostic analysis to gain a competitive advantage and check the effectiveness of each business strategy.

The merging of the customer data from sales and the call center interactions has created the more informed interactions with the customer (Petersen, 2004). The concept rang with the user organizations and mergers and acquisitions created a host of software that the vendors claimed to have an integrated set of capabilities that became known as customer relationship management (Petersen, 2004). Companies wanted to learn more about each and every individual customer and use the information to effectively take care of and manage their relationships, and yet increased customer satisfaction and profit.

Therefore, by knowing the fact that 80% of business of an organization often comes from 20% of their customers (the Pareto law), it is important for organizations to identify a model to classify the customers as high value customers or not. Most of the past researches (Kim et al. 2006, Khajvand M. and Tarokh M. J. 2011) have performed customer segmentation based on the traditional Customer Relationship Management (CRM) variables. However, these variables might not be sufficient to identify the profile of High Value Customers since traditional CRM variables do not consider the online activities of customers and also the dispute that a customer might influence other