Data mining in customer acquisition

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Data mining in customer acquisition

Introducing field importance and field-field correlation When the model building process is finished you can work on field importance and field-field correlation. Classification, Clustering, and Regression store detailed field importance and field-field correlation information in the created model.

For example, the primary purpose for constructing Regression models is to predict numerical values for the target field. However, as a second and sometimes even more important purpose, you might be interested in gaining insight into the relations between the explanatory fields and the target field.

Field importance Describes how important each input field is for the generated model. Classification, Clustering, and Regression create a list containing the names of the most important input fields and for each field a number between 0 and 1.

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The larger the number is, the more important is the corresponding input field for the resulting model. Field-field correlations Classification, Clustering, and Regression models contain a matrix of field-field correlations between input fields.

If the training data contains a large number of fields columnsso that the matrix of all field-field correlations would be too large, the data mining function automatically drops part of the correlations and writes only the most important ones into the created model.

Data mining in customer acquisition

Together with the field importance information, the field-field correlations can optimize the data mining process for computation time, the amount of data storage, and the cost of data acquisition.

The field importance information of a first model created by Regression indicates that fields A, C and D are important for the resulting model, B is less important.

The field-field correlations indicate that A and C are strongly correlated, all other field-field correlations are small. The user who performed the mining run knows that acquiring the data for fields B and C is rather costly, whereas A and D can be gathered almost for free.

The initial should have customer tenure dimensions which takes minimum three values. In cases like this, customer acquisition drives the company and advertising, as opposed to direct marketing, is principal approach of attracting new clients. Data mining offers the greatest benefit if the data is ashio-midori.com://ashio-midori.com The cost associated with customer acquisition is usually higher than the cost associated with customer in the prediction of customer loyalty. The evaluated attributes are tabulated in TableI. “The Use of Data Mining for Prediction of Customer Loyalty”, CommIT (Communication & Information Technology) Journal 10(1),41–47, ashio-midori.com Data mining can be useful in all the three phases of a customer relationship-cycle: customer acquisition, increasing value of the customer and customer retention. For example, a typical banking firm let say sends 1 million direct mails for credit card customer ashio-midori.com://ashio-midori.com

Then a good choice for the future would be to stop gathering the data for fields B and C and continue the mining with input fields A and D only. This reduces the costs without decreasing the model quality and thus the economic benefit of the mining considerably.

Even though field C is important for the resulting model, it can be dropped because the strongly correlated field A still is in the input data, so that a large part of the information brought in by field C will still be provided by field A.

Example 2 A retail marketing company plans a new mailing campaign. Based on the common attributes of these customers and on the results of similar mailing campaigns in the past, Regression creates a model which predicts the company's additional profit from the mailing campaign for each customer - also for those customers who have never before been involved in a similar mailing campaign: A Regression mining run analyzes the data of earlier target marketing campaigns and creates a model which relates the customer response behavior to certain other customer attributes.

Then Regression automatically tests the accuracy of this model by applying the model to test data with known customer response behavior. If the user did not specify an extra test data set, Regression can automatically reserve a part of the training data for that test purpose.

The company can apply the validated model with Intelligent Miner to customers for which no response data exist.Data mining is a new way of thinking about your business, using data to ask questions and investigate hypotheses.

In the context of acquisition and retention, the questions can range from sales and marketing issues to operational issues. Data Mining June 1st, Predictive Analytics and Customer Behavior “Predictive analysis is the decision science that removes guesswork out of the decision-making process and applies proven scientific guidelines to find right solution in the shortest time possible.” (Kaith, ) There are seven steps to Predictive Analytics: spot the.

Data Mining in Customer Acquisition

· Data mining is simply the acquisition of information that is already present in your CRM (Customer Relationship Management System) that is intended to be utilized for marketing, customer service, customer informative services and similar ashio-midori.com://ashio-midori.com  · Analytics and Data Mining Applicant Tracking System (ATS) Communications and Marketing Customer Relations Management (CRM) Development and Integration Recruiting / Executive Recruiting HR Information Systems (HRIS) HR Management system (HRMS) Learning and Development Talent Acquisition Workforce Management Business Solutions Career Coachingashio-midori.com  · customer acquisition rate high, often at the expense of customer retention.

But this situation has changed (Mozer et al ). As the well of new wireless subscribers has begun to run dry, Data Mining is the process of using raw data to infer important business ashio-midori.com://ashio-midori.com  · Purpose Customer lifetime value (CLV) scoring is highly effective when applied to marketing databases.

Some researchers have extended the traditional association rule problem by associating a weight with each item in a transaction. However, studies of association rule mining have considered the relative benefits or significance of “items” rather than “transactions” belonging to ashio-midori.com

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