Data mining applications in customer relationship management can contribute significantly to the bottom line rather than randomly contacting a prospect or customer through a call center or sending e-mail, a company can focus its efforts on prospects that are expected to respond to a likely have to offer. More advanced methods can be used to optimize resources across campaigns, so that we can predict which channel and which offer an individual is most likely to respond to all possibilities.
In addition, advanced applications used to automate the mission. Once the results of data mining (potential prospect / customer and channel / demand) are determined, this “advanced application” will automatically send an e-mail or regular mail. Finally, in cases where many people will take action without an offer, can lift models are used to determine who the greatest increase in responding if given an offer to have.
Grouping data can also be used to automatically detect customer segments or groups within a dataset.
Data mining employment business may see a return on investment, but they acknowledge that the number of predictive models can be large made quickly in order to preserve the volume of the models, versions of the model for management and automated data mining should go.
Data mining the characteristics of their most successful employees to identify may be useful for human resource departments. Like the universities attended by highly successful employees, information, human resources can help by focusing recruiting efforts. Additionally, Strategic Enterprise Management applications help a company corporate level goal, such as profit margins and production plans and applicable level of operational decisions, as translated into the common goal.
Another example of data mining is often called the market basket analysis, is related to its use in retail.
If a clothing store records the purchases of customers, a data mining system of cotton silk shirts to identify those customers that party. However, some clarification of the relationship difficult, it is easy to take advantage of. Example deals with the rules within the group based on transactions data.
Not all data transactions and logical or inexact rules may also be present in a database. In a production application, an imprecise rule states that 73% of products have a specific defect or problem within the next six months, a second problem. Market basket analysis to the alpha consumer buying habits to identify data collected on these companies buy future supply, demand trends and forecasts, analysis may predict.
List of data mining is a very effective tool in the marketing industry. Mail order companies dates back millions of years the customer has a rich history of customer transactions.
Is related to an integrated circuit production, is an example of data mining is described in the paper “Mining IC test data to optimize VLSI testing.” In this article, data mining and decision analysis problem for the die-level functional test in the application which allows you to create. With the use of the system based on historical test data show that profits from the mature IC products have the ability to improve.