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Customer relationship management (CRM) analytics is a way that companies discover and use information about customers to help make business decisions. The information is collected and analyzed using special computer software. CRM analytics often relies on Web mining to gather information. It then uses classic data mining parameters to analyze that information. Using this analysis, a company can learn what its customers really want, how best to personalize services and which customers are likely to bring in the most profit for the company.
In a world where it is possible to shop and buy online without ever leaving the comfort of home, many companies are using the Internet to find out more about their customers. CRM analytics has a couple of goals. First, companies want to group customers by interest to target market to their specific needs. Second, companies want to know which customers are likely to make the most profit for the company over time, so the company knows which customers are worth targeting.
They do this using Web mining. Web mining is a system of techniques that allow companies to look for patterns in customer behavior; there are three web mining techniques commonly used in CRM analytics. Structure mining examines how shoppers use the Web site. This technique shows, for instance, on what links or advertisements customers are clicking. Content mining uses data collected by search engines, and the third technique called usage mining uses information from online forms and other customer responses.
Once the information has been gathered using these techniques, it is inputted into a multidimensional database. This type of database allows data to be viewed and organized based on different characteristics. For example, if a user asks a multidimensional database, "What are the top three countries in the world?" it might allow the user to organize the corresponding information based on population, gross domestic product or some other parameter.
In CRM analytics, the information in the database is analyzed using data mining techniques. Data mining looks for patterns that might help the company predict what customers want, what they are most likely to buy, and what sorts of deals or promotions to offer them. For example, using data mining, a company might find that people who buy video games often come back to the site later to buy science fiction books. The company then knows that if a customer buys a video game, it will probably be profitable to send the customer a 10 percent off coupon for science fiction books as well.
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