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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]