Data mining techniques can be used in virtually all business applications, answering various types of businesses questions. In truth, given the software available today, all you need is the motivation and the know-how. In general, data mining can be applied whenever something could be known, but is not. The following examples describe some scenarios:
Recommendation generation— What products or services should you offer to your customers? Generating recommendations is an important
business challenge for retailers and service providers. Customers who are provided appropriate and timely recommendations are likely to be
more valuable (because they purchase more) and more loyal (because they feel a stronger relationship to the vendor). For example, if you go to online stores such as Amazon.com or Barnesandnoble.com to purchase an item, you are provided with recommendations about other items you may be interested in. These recommendations are derived from using data mining to analyze purchase behavior of all of the retailer’s customers, and applying the derived rules to your personal information.
Anomaly detection — How do you know whether your data is ‘‘good’’ or not? Data mining can analyze your data and pick out those items that don’t fit with the rest. Credit card companies use data mining–driven anomaly detection to determine if a particular transaction is valid. If the data mining system flags the transaction as anomalous, you get a call to see if it was really you who used your card. Insurance companies also use anomaly detection to determine if claims are fraudulent. Because these companies process thousands of claims a day, it is impossible to investigate each case, and data mining can identify which claims are likely to be false. Anomaly detection can even be used to validate data entry—checking to see if the data entered is correct at the point of entry.
Churn analysis — Which customers are most likely to switch to a competitor? The telecom, banking, and insurance industries face severe competition. On average, obtaining a single new mobile phone subscriber costs more than $200. Every business would like to retain as many customers as possible. Churn analysis can help marketing managers identify the customers who are likely to leave and why, and as a result, they can improve customer relations and retain customers.
Risk management—Should a loan be approved for a particular customer? Since the subprime mortgage meltdown, this is the single most common question in banking. Data mining techniques are used to determine the risk of a loan application, helping the loan officer make appropriate decisions on the cost and validity of each application.
Customer segmentation —How do you think of your customers? Are your customers the indescribable masses, or can you learn more about your customers to have a more intimate and appropriate discussion with them. Customer segmentation determines the behavioral and descriptive profiles for your customers. These profiles are then used to provide personalized marketing programs and strategies that are appropriate for each group.
Targeted ads — Web retailers or portal sites like to personalize their content for their Web customers. Using navigation or online purchase
patterns, these sites can use data mining solutions to display targeted advertisements to their Web navigators.
Forecasting — How many cases of wine will you sell next week in this store? What will the inventory level be in one month? Data mining forecasting techniques can be used to answer these types of time-related questions.
Resources:
Data Mining with Microsoft SQL Server 2008: Provides in-depth knowledge about Data Mining with SSAS. Covers the Algorithms, DMX and SSIS Data Mining solutions in detail. This is an absolute must for learning how to develop a Data Mining solution. It has tons of great examples.
Predixion Insight: Jamie MacLennan, Director of SSAS at Microsoft, left Microsoft and started Predixion which offers the best Data Mining SSAS based product on the market. The excel demo comes with great examples as well.
Data Mining Techniques: Provides good examples of how to develop a strategy for using Data Mining to solve business problems.
Automation of Data Mining Using Integration Services: This article is a walkthrough that illustrates how to build multiple related data models by using the tools that are provided with Microsoft SQL Server Integration Services. In this walkthrough, you will learn how to automatically build and process multiple data mining models based on a single mining structure, how to create predictions from all related models, and how to save the results to a relational database for further analysis. Finally, you view and compare the predictions, historical trends, and model statistics in SQL Server Reporting Services reports.
