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What are the Different Types of Data Mining Technology?

Article Details
  • Written By: Jason C. Chavis
  • Edited By: Bronwyn Harris
  • Last Modified Date: 15 July 2017
  • Copyright Protected:
    2003-2017
    Conjecture Corporation
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There are many different types of data mining technology used in the process of retrieving information from raw data. Each of these types of technology are used for a variety of reasons including marketing, security and general information gathering. Data mining technology is commonly used to test samples of data rather than whole swaths of content, allowing analysts to verify and validate patterns within the blocks of information. Many companies specialize in developing these data mining tools for specific businesses or general use.

One common data mining technique is used by the insurance industry to determine standard rates of risk for its customers. The property and casualty industry suffers from a dwindling reserve of profits as the market fails to support the old business model traditionally used by insurance companies. In order to ensure profitable returns, the companies use a data mining tool to check each claim as it comes in as to whether it is a likely case of fraud. This saves the industry vast amounts of money each year.

Retailers and customer service businesses use a data mining technology that attempts to identify the attributes of its best customers. By associating certain advertisements and the structure of the retail environment with the best customer models, they can ensure these consumers get the best possible experience. In addition, the data mining technology is designed to get the number of these profitable customers to increase using these same techniques. Additional information can provide the companies with information about identifying the ultimate response of the customer base to changes and marketing approaches. This helps drive the overall strategy of the company, while also increasing profitability.

A data mining technique known as attrition modeling works for all types of industries to identify customers that are likely to move to other suppliers or retailers. This data mining technology optimizes information that allows for the best way to build loyalty with a customer base and avoid potential losses on a proactive scale. Using information culled from the existing customers currently doing business with a company, the technology provides data on those most likely to accept up-selling and cross-selling to build additional revenue. It will also target those customers that traditionally jump from supplier to supplier for various reasons, allowing the business the potential to either work with those clients or let them go. Regardless of the use for data mining technology, these techniques aid in financial growth and accountability.

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