Open World 2003
Data Warehousing for the Communications Industry:
A Data Mining Approach to Customer Churn Analysis in Wireless Industry
Shyam Varan NathSenior Database Engineer
Daleen Technologies
Session id: 40332
Introduction
Oracle Data Mining– JDeveloper– DM4J
Wireless Industry and Customer Churn Data Modeling for Churn Management
“WLNP Threatens to Significantly Impact
Wireless Churn Rates.”
“WLNP Threatens to Significantly Impact
Wireless Churn Rates.”
Source In-Stat 2002
Churn
North American Wireless industry monthly churn rate in Q4-02
Canadian Average
U.S. Average
2.4%
2.8%
Monthly Churn (%) - 4Q-02
Source: Company & analyst reports
Wireless Industry: Some Facts
Wireless Local Number Portability (WLNP) from Nov 2003
Average Cost to Acquire a New Wireless Customer: $400 to $500
Data Mining as a Solution to the Business Problem
…facts
Source: Duke Teradata 2002
…facts
Reasons for Churn
Many companies to choose from Similarity of their Offerings Cheap prices of the handsets
The biggest current barrier to churn:The biggest current barrier to churn: the lack of phone number portability!the lack of phone number portability!
A Dilemma
Cross-Selling Through Database Marketing – 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
Role of Data Mining
Business Issues in a Wireless Industry
Some Definitions
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
“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
Choice of Tools
Justification for Data Mining
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.
Predictive Modeling
Visual Representation of Predictive Modeling
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
What is ODM?
Connected to:
Oracle9i Enterprise Edition Release 9.2.0.1.0 - Production
With the Partitioning, OLAP and Oracle Data Mining optionsOracle Data Mining options
JServer Release 9.2.0.1.0 - Production
SQL>
Oracle9i Data Mining, an option to Oracle9i 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.
Why Oracle?
Integrated Environment of Oracle Relational Database
Supervised v/s Unsupervised Learning
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.)
Naive Bayes algorithm 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"
Bayes Theorem
According to the Bayesian rule, the probability of an example E being in class c is:
P(C = c|a1, a2 ……, an) = p(a1, a2 ……, an|C = c) p(C = c)
p(a1, a2 ……, an) The classification is taken as the C’s value with the largest probability:Assume all attributes are independent given the class:
p(a1, a2 ……, an|c) = p(a1|c) p (a2|c) ….p(an|c)
The resulting Bayesian classifier is called the
Naïve Bayesian classifier.
Major Steps Of Data Mining
Build Model: Models are built in the data-mining server
Test Model: Model testing gives an estimate of model accuracy
Compute Lift: ODM supports computing lift for a binary classification model (confidence of prediction)
Apply Model: Applying a supervised learning model to data results in scores or predictions with an associated probability
computing lift for a binary classification model,
Build Model
Apply Process
Data For Modeling
Sample Size 100,000 51,306 100,462
# of PredictorVariables
171 171 171
Churn IndicatorCustomer ID
Yes1,000,001 – 1,100,000
No2,000,001 – 2,051,306
No3,000,001 – 3,100,462
CalibrationCurrent Score
DataFuture Score
Data
Nature of Dataset Used for Study
(real Wireless Customer Data)
System Setup
Database Java Environment Data Mining Wizard
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 .
Java Environment: JDeveloper
Installation of JDeveloper 9.0.3
Data Mining Wizard: DM4J
Question
Getting Started…
•Unlock odm user•Grants on the tables for wizard to display•Odm_mtr schema
Working with the DM4J Wizard
Creating a new Workspace
Configuring a Database Connection
…DM4J
Selecting a model type in the DM4J wizard.
Algorithm for Data Modeling
Selecting the Algorithm
Fine tuning the algorithm
…DM4J
The DM4J wizard generates the Java code that is compiled and
executed to create the model.
…DM4J
Here is the Java Code!
Our Study
The input data was stored in atable called CALIBRATION.
Our target variable for prediction is CHURN.
…study
We pick all the input predictor variables (except customer Id) from the list of 171 to predict churn.
…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.
…study
The Adaptive Bayes Network also generates the rules for the model in human readable form.
…study
Testing the Model using the data from table PRESENT
Confusion Matrix
Cumulative Lift Chart
…study
The last step is to apply the tested model to the data set where we want to predict the CHURN
…study
When we apply the model, the predictions are obtained and stored in an output table
After the Apply task is run
…study
Rating the importance of the various predictor variables.
Top Ten Variables
1. DUALBAND type of phone set2. CARTYPE dominant vehicle lifestyle3. EDUC1 education level of first house hold member4. ETHNIC ethnicity5. TOT_ACPT total offers accepted from retention team6. OCCU1 occupation of the first household member7. AREA geographic area8. INCOME estimated household income9. DWLLSIZE dwelling size10. PROPTYPE property type details
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 barringIntervention.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 staySS: number of subscribers who are predicted to stay and who actually stay
Churn Management
Expected Saving to Carrier / Churnable Subscriber
Source: Mozer 2000
Future Trends and Conclusion
•Real time Analytics and Text Mining (Oracle 10G) can take Data Mining to next level.
•Oracle Data Mining can resolve a Business problem.
•Churn Prediction and Churn Management can yield significant savings to the wireless provider.
Daleen at a Glance Founded in 1989 with a mission to
build custom software for finance & telecom sectors
Worldwide base of over 80 billing & customer care contracts since 1997
Innovator in deployment of convergent billing, event management & revenue assurance solutions for next-generation services
Long term focus on delivering exceptional customer service through a site license or service bureau relationship
Offices in Boca Raton, St. Louis, Amsterdam & Sydney
RevChain – high performance billing & customer management
Commerce - convergent billing & customer mgmt.
Interact - pure web CSR interface for comprehensive account management
Care - web-based self-care with EBPP
mCommerce - account mgmt. via the mobile device
Asuriti – centralized event management & revenue assurance
Configurable, rules-based architecture
Centralized management of event data
Data transformation & enrichment
Revenue assurance & error management
BillingCentral – comprehensive outsourcing solution
Advanced billing & event management technologies
Proven best practices & process controls
Carrier-class hardware & networks
Performance guarantees & revenue assurance
AQ&Q U E S T I O N SQ U E S T I O N S
A N S W E R SA N S W E R S
References & Useful LinksTechnet http://technet.oracle.com/products/bi/odm/9idm4j
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].Retrieved on: Nov 8, 2002. Available: http://otn.oracle.com/products/bi/pdf/o9idm_bwp.pdf)
Skedd, Kirsten 2002. WLNP threatens to significantly impact wireless churn rates [On-line]. Retrieved on Sep 14, 2002. Available: http://www.instat.com/press.asp?ID=311&sku=IN020258WP
Acknowledgements
Dr Ravi Behara, Faculty (Florida Atlantic University)
David Eastlund and Jennifer from Oracle Cohorts at Daleen Technologies
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Session id: 40332 Data Warehousing for the Communications Industry
Thank you.
Contact Information
Email: [email protected] Cell Phone: (954) 609-2402 Test Message: [email protected]
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