Applications of Data Mining

It is important to keep in mind that in spite of all the dazzling technologies, data mining has to be driven by strong business needs in order to justify the expenditure of time and money. One of the common business drivers for engaging in data mining is to gain market share. This can be accomplished by either introducing new products or by taking away market share from competitors . In either case, a data mining application can help you decide how best to achieve your goals.

There are many types of data mining applications. The five most common ones are briefly described below.

• Market management

  - Cross-selling : Identify cross-selling opportunities among existing customers, who are likely to buy as a result of direct mail campaigns and promotions (and thus minimize selling costs).
  
  

  - Defecting customers: Determine which customers are likely to switch brands by using vulnerability analysis that creates a predictive model of behavior, so that the company can craft strategies to retain these customers.
  
  

  - Promotions and campaigns: Distinguish natural groupings in the market, such as key sales periods for given items, by performing market segmentation analysis as a way to fine-tune promotions and campaigns.
  
  

  - Prospecting: Classify groups of prospects in order to find ways to perform target marketing for each group .
  
  

  - Market basket analysis: Evaluate which items people buy together during a visit to a supermarket or store, using market basket analysis on point-of-sale data. Then use the information to group products in store displays, to adjust inventory, and to price and promote items.
  
  

• Fraud detection

  - Credit card fraud: Isolate credit card fraud by identifying meaningful patterns of transactions as well as deviations from those patterns. Use this model to predict an applicant 's trustworthiness .
  
  

  - Calling card fraud: Determine telephone calling card situations that look suspicious and are likely to indicate fraud.
  
  

  - Insurance fraud: Analyze large data pools of insurance claims to identify possible fraud related to health insurance, car insurance, or property and casualty insurance.
  
  

• Risk management

  - Credit risk: Assess the credit risk of potential loan applicants based on a predictive model of the database that looks for reasons and patterns affecting risk.
  
  

  - Quality control: Find patterns of quality problems on assembly lines to help reduce the number of products returned due to substandard quality.
  
  

• Financial services

  - Customer retention: Identify loyal bank customers who have many high-balance accounts and provide each of them a personalized fee structure. It is much cheaper to retain existing customers than to acquire new ones.
  
  

  - Stock performance: Develop models of stock performance to aid portfolio management. Look for stocks that have performed in ways similar to certain high-performing securities.
  
  

• Distribution

  - Inventory control: Improve inventory control and distribution by developing predictive models of which products or parts will be needed at various distribution points at various points in time.
  
  

One good indication of the value of data mining is the secrecy that surrounds its implementations . Many of the companies that have implemented data mining hesitate to talk about their successes. Some will not even confirm that they use this technology.