By Olivia Parr Rud
Now on hand, this insightful e-book indicates you the way to create and enforce versions of the main frequently asked information mining questions for advertising, revenues, chance research, and client courting administration and support.In addition to genuine international event and knowing, you will get time-tested confirmed modeling strategies that deal with particular inquiries to assist you locate inventive new how one can bring up revenue and lower costs.Starting from the fundamentals, you will plan the menu and select the appropriate parts - or ask the fitting questions and get info able to mine - sooner than you get right down to the company of constructing the meal.You'll locate quite a few case experiences that aspect to be had info resources for constructing focusing on versions, then discover ways to approach, overview, and enforce them via an intensive case examine of an entire life price version for a existence assurance direct-mail crusade.
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This ebook constitutes the refereed complaints of the eleventh overseas Workshop on Computational Processing of the Portuguese Language, PROPOR 2014, held in Sao Carlos, Brazil, in October 2014. The 14 complete papers and 19 brief papers provided during this quantity have been conscientiously reviewed and chosen from sixty three submissions.
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Extra info for Data Mining Cookbook
If the customer never uses the account, he or she actually ends up costing the bank more than a nonresponder. Most credit card banks offer incentives such as low-rate purchases or balance transfers to motivate new customers to activate. An insurance prospect can be viewed in much the same way. A prospect can respond and be approved, but if he or she does not pay the initial premium, the policy is never activated. There are two ways to build an activation model. One method is to build a model that predicts response and a second model that predicts activation given response.
So, in this chapter, I start at the beginning. The first step in making the best use of any data source is to understand the nature of the data as well as how it is gathered and managed. Chapter 2 begins with a discussion of various types of data along with the strengths and weakness of each type. The next section discusses some typical data sources with descriptions and cases from both internal and external repositories. The final section of the chapter offers some ideas for creating targeting model data sets; this section also includes numerous case studies.
I begin with the most important step in developing a targeting model: establishing the goal or objective. Several sample applications of descriptive and predictive targeting models help to define the business objective of the project and its alignment with the overall goals of the company. Once the objective is established, the next step is to determine the best methodology. This chapter defines several methods for developing targeting models along with their advantages and disadvantages. The chapter wraps up with a discussion of the adaptive company culture needed to ensure a successful target modeling effort.