By Olivia Parr Rud
Increase earnings and decrease expenses by using this selection of versions of the main frequently asked facts mining questions
In order to discover new how one can increase shopper revenues and aid, and in addition to deal with hazard, enterprise managers needs to be capable of mine corporation databases. This e-book offers a step by step consultant to making and enforcing types of the main frequently asked facts mining questions. Readers will easy methods to arrange information to mine, and enhance exact info mining questions. the writer, who has over ten years of information mining event, additionally offers real verified types of particular info mining questions for advertising, revenues, customer support and retention, and hazard administration. A CD-ROM, bought individually, presents those types for reader use.
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Extra resources for Data Mining Cookbook: Modeling Data for Marketing, Risk and Customer Relationship Management
Sample text
To enhance their data mining efforts, many companies are diligently collecting, combining, and scrubbing data. Companies new to data mining are revamping their databases to allow for easier access and extractability. Companies whose primary business is to collect, enhance, and sell data are springing up every day. All of this can be a bit overwhelming. 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.
Lifetime value modeling will identify prospects with a high likelihood of being profitable customers in the long term. •Do you want to avoid high-risk customers? Risk or approval models will identify customers or prospects that have a high likelihood of creating a loss for the company. In financial services, a typical loss comes from nonpayment on a loan. Insurance losses result from claims filed by the insured. •Do you want to understand the characteristics of your current customers? This involves segmenting the customer base through profile analysis.
Then weights or ''inputs" are assigned to each of the nodes in the first layer. During each iteration, the inputs are processed through the system and compared to the actual value. The error is measured and fed back through the system to adjust the weights. 6 Neural network. the weights get better at predicting the actual values. The process ends when a predetermined minimum error level is reached. One specific type of neural network commonly used in marketing uses sigmoidal functions to fit each node.