5.1 Applications of Data Mining
A wide range of companies have deployed successful applications of data mining. While early adopters of this technology have tended to be in information-intensive industries such as financial services and direct mail marketing, the technology is applicable to any company looking to leverage a large data warehouse to better manage their customer relationships. Two critical factors for success with data mining are: a large, well-integrated data warehouse and a well-defined understanding of the business process within which data mining is to be applied (such as customer prospecting, retention, campaign management, and so on).
Some successful application ...view middle of the document...
Using data mining to analyze its own customer experience, this company can build a unique segmentation identifying the attributes of high-value prospects. Applying this segmentation to a general business database such as those provided by Dun & Bradstreet can yield a prioritized list of prospects by region.
• A large consumer package goods company can apply data mining to improve its sales process to retailers. Data from consumer panels, shipments, and competitor activity can be applied to understand the reasons for brand and store switching. Through this analysis, the manufacturer can select promotional strategies that best reach their target customer segments.
Each of these examples have a clear common ground. They leverage the knowledge about customers implicit in a data warehouse to reduce costs and improve the value of customer relationships. These organizations can now focus their efforts on the most important (profitable) customers and prospects, and design targeted marketing strategies to best reach them.
There are a number of applications that data mining has. The first is called market segmentation. With market segmentation, you will be able to find behaviors that are common among your customers. You can look for patterns among customers that seem to purchase the same products at the same time. Another application of data mining is called customer churn. Customer churn will allow you to estimate which customers are the most likely to stop purchasing your products or services and go to one of your competitors. In addition to this, a company can use data mining to find out which purchases are the most likely to be fraudulent.
For example, by using data mining a retail store may be able to determine which products are stolen the most. By finding out which products are stolen the most, steps can be taken to protect those products and detect those who are stealing them. While direct mail marketing is an older technique that has been used for many years, companies who combine it with data mining can experience fantastic results. For example, you can use data mining to find out which customers will respond favorably to a direct mail marketing strategy. You can also use data mining to determine the effectiveness of interactive marketing. Some of your customers will be more likely to purchase your products online than offline, and you must identify them.
While many businesses use data mining to help increase their profits, many of them don't realize that it can be used to create new businesses and industries. One industry that can be created by data mining is the automatic prediction of both behaviors and trends. Imagine for a moment that you were the owner of a fashion company, and you were able to precisely predict the next big fashion trend based on the behavior and shopping patterns of your customers? It is easy to see that you could become very wealthy within a short period of time. You would have an advantage over your...