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Data Warehousing for the Communications Industry: A Data Mining Approach to Customer Churn Analysis in Wireless Industry Shyam Varan Nath Senior Database Engineer Daleen Technologies Session id: 40332
cross-selling is effective for customer retention by increasing switching costs and enhancing customer loyalty
on the other hand, cross-selling can also potentially weaken the firm’s relationship with the customer, because frequent attempts to cross-sell can render the customer non-responsive or even motivated to switch to a competitor
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Role of Data Mining Business Issues in a Wireless Industry
Data Warehousing: Data warehousing is a database or a collection of databases designed to give business decision-makers instant access to information
Data Mining: The Data Mining is the process of using raw data to infer important business relationships that can then be used for business advantage
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“ Simply put, data mining is used to discover [hidden] patterns and relationships in your data in order to help you make better business decisions.” Source: Oracle9i Data Mining 2001
Reporting Tools : Good at drilldowns into the details
OLAP/Statistical Tools : Used to draw conclusions from representative samples
Data Mining: Goes deep into the data. It uses machine-learning algorithms to automatically sift through each record and variable to uncover patterns and information that may have been hidden.
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Predictive Modeling Visual Representation of Predictive Modeling
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Benefits Of Data Warehousing And Predictive Modeling
Immediate Information Delivery
Data Integration from across—and even outside—the Organization
Future Vision from Historical Trends
Tools for Looking at Data in New Ways
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What is ODM? Connected to: Oracle9i Enterprise Edition Release 9.2.0.1.0 - Production With the Partitioning, OLAP and Oracle Data Mining options JServer Release 9.2.0.1.0 - Production SQL> Oracle9 i Data Mining , an option to Oracle9 i Enterprise Edition, that allows users to build advanced business intelligence applications that mine corporate databases to discover new insights, and integrate those insights into business applications.
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Why Oracle? Integrated Environment of Oracle Relational Database
Supervised learning requires identification of a target field or dependent variable. The supervised-learning technique then sifts through data trying to find patterns and relationships between the independent variables and the dependent variable. (ODM provides the Naïve Bayes data mining algorithm for supervised-learning problems.)
Unsupervised learning allows the user not to indicate the objective to the data mining algorithm. Associations and clustering algorithms make no assumptions about the target field. Instead, try to find associations and clusters in the data independent of any a priori defined business objective – Market-basket analysis etc. (ODM provides the Association Rules data mining algorithm for unsupervised-learning problems.)
The Naive Bayes algorithm uses the mathematics of Bayes' Theorem to make its predictions. The algorithm is typically used for:
Identifying which customers are likely to purchase a certain product
Identifying customers who are likely to churn
Predicting the likelihood that a part will be defective
Adaptive Bayes Network
Human readable rules
IF RELATIONSHIP = "Husband" AND EDUCATION_NUM = "13-16" THEN CHURN= "TRUE"
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Bayes Theorem According to the Bayesian rule, the probability of an example E being in class c is: P(C = c|a 1 , a 2 ……, a n ) = p(a 1 , a 2 ……, a n |C = c) p(C = c) p(a 1 , a 2 ……, a n ) The classification is taken as the C’s value with the largest probability: Assume all attributes are independent given the class: p(a 1 , a 2 ……, a n |c) = p(a 1 |c) p (a2|c) ….p(a n |c) The resulting Bayesian classifier is called the Naïve Bayesian classifier.
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Data For Modeling Nature of Dataset Used for Study (real Wireless Customer Data) Sample Size 100,000 51,306 100,462 # of Predictor Variables 171 171 171 Churn Indicator Customer ID Yes 1,000,001 – 1,100,000 No 2,000,001 – 2,051,306 No 3,000,001 – 3,100,462 Calibration Current Score Data Future Score Data
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Database: Oracle 9.2.0.1.0 Installation of Oracle Database Software 9.2.0.1.0 with Oracle Data Mining Option, with the database patch for version 9.2.0.2.1 .
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Java Environment: JDeveloper Installation of JDeveloper 9.0.3
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Our Study The input data was stored in a table called CALIBRATION. Our target variable for prediction is CHURN.
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…study We pick all the input predictor variables (except customer Id) from the list of 171 to predict churn.
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…study compilation and execution of the Java code containing the ODM model. The program runs in an asynchronous mode and we can monitor the progress of the task. The screen shot shows the successful completion of the model.
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…study The Adaptive Bayes Network also generates the rules for the model in human readable form.
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…study Testing the Model using the data from table PRESENT Confusion Matrix Cumulative Lift Chart
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…study The last step is to apply the tested model to the data set where we want to predict the CHURN
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…study When we apply the model, the predictions are obtained and stored in an output table After the Apply task is run
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…study Rating the importance of the various predictor variables.
TOT_ACPT total offers accepted from retention team
OCCU1 occupation of the first household member
AREA geographic area
INCOME estimated household income
DWLLSIZE dwelling size
PROPTYPE property type details
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Cost Savings Based on Churn Data savings per churnable subscriber = [ net(no intervention) – net(incentive) ] / [ L + NL ] net(no intervention) = [ L + NL ] X Cl net(incentive) = [ L + LS ] Ci + [ Pi L + NL ] Cl To estimate cost savings, the parameters Ci (cost of incentive per customer), Pi (reduction in probability to churn due to incentive Ci), and Cl (lost-revenue cost when a subscriber churns) are combined with four statistics obtained from a predictor model: L : number of subscribers who are predicted to leave (churn) and who actually leave barring Intervention. NL : number of subscribers who are predicted to stay (nonchurn) and who actually leave barring Intervention. LS : number of subscribers who are predicted to leave and who actually stay SS : number of subscribers who are predicted to stay and who actually stay
Armstrong, G., and P. Kotler. 2001. Principles of Marketing . Prentice Hall New Jersey.
Duke Teradata 2002. Teradata Center for Customer Relationship Management. [On-line]. Retrieved on: Nov 7, 2002. Available: http://www.teradataduke.org/news_t_2.html
In-Stat. 2002. WLNP Threatens to significantly impact wireless churn rates. [Online]. Retrieved on Sep 2002.
Available: http://www.instat.com/newmk.asp?ID=312
Mozer, Michael, Richard Wolniewicz, Eric Johnson and Howard Kaushansky. 1999. Churn reduction in the wireless industry, Proceedings of the Neural Information Processing Systems Conference , San Diego, CA.
Oracle9i Data Mining 2001. An Oracle white paper December 2001. [Online].
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