CUSTOMERS WITH MORE BUSINESS OPPORTUNITIES

 

 

 

 

 

 

 

 

 

 

 

 

 

The virtuous cycle of data mining is about harnessing the power of data and transforming it into actionable business results. Just as water once turned the wheels that drove machines throughout a mill, data needs to be gathered and disseminated throughout an organization to provide value. If data is water in this analogy, then data mining is the wheel, and the virtuous cycle spreads the power of the data to all the business processes. The virtuous cycle of data mining is a learning process based on customer data. It starts by identifying the right business opportunities for data mining. The best business opportunities are those that will be acted upon. Without action, there is little or no value to be gained from learning about customers. Also very important is measuring the results of the action. This com­ pletes the loop of the virtuous cycle, and often suggests further data mining opportunities.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Now it is time to start looking at data mining as a technical process. The high-level outline remains the same, but the emphasis shifts. Instead of identi­ fying a business problem, we now turn our attention to translating business problems into data mining problems. The topic of transforming data into information is expanded into several topics including hypothesis testing, pro­ filing, and predictive modeling. In this chapter, taking action refers to techni cal actions such as model deployment and scoring. Measurement refers to the testing that must be done to assess a model’s stability and effectiveness before it is used to guide marketing actions.The best practices introduced here are elaborated upon elsewhere. The best way to avoid breaking the virtuous cycle of data mining is to understand the ways it is likely to fail and take preventative steps. the authors have encountered many ways for data mining projects to go wrong.