PREPAID CHURN MODEL
With Oracle Data Mining
Necdet Deniz Halıcıoğlu
deniz.halicioglu@turkcellteknoloji.com.tr
September 21, 2010
Agenda
About Turkcell Technology
Churn Prediction
Existing Mining System in Turkcell
Data Mining with ODM
SVM Model
Conclusion
Agenda
About Turkcell Technology
Churn Prediction
Existing Mining System in Turkcell
Data Mining with ODM
SVM Model
Conclusion
About Turkcell Technology
Turkcell Technology has more than 15 years of development experience with
its solutions applied and proven at leading operators in more than 10 countries.
More than 10 years of
experience in Turkcell
ICT
1994 - 2006
TTECH Center was put
into service
HC: 255 engineers
Focus: Turkcell Group
2007
2008
TTECH was formed with 44
engineers in TÜBİTAK-MAM
Technological Free Zone
Focus: Turkcell
Focus: Turkcell &
Telia Sonera Group +
Regional Sales
HC: 360 engineers
2009
Focus: Turkcell &
Telia Sonera Group
HC: 321 engineers
Today
Areas of Competency
From assisting the operation of network resources to improving business
oriented intelligence, TTECH’s experts provide an expanding portfolio of
packaged and custom solutions for telecom network operators.
Network Services & Enablers
SIM Asset & Services Management
Mobile Marketing
Mobile Internet & Multimedia
Business Intelligence & Support Systems
Turkcell Technology IMS Group
More than 10 years of BI experience in Telecommunications industry
Designed, Built and Running one of the largest data warehouses in telecom
industry
Team of more than 100 highly talented professionals and consultants
Has a proven record of success in BI operations
Flawless operation, providing data for finance and even for NYSE
Early adopter of the new BI trends
Complex Event Processing, Text Mining, etc.
Agenda
About Turkcell Technology
Churn Prediction
Existing Mining System in Turkcell
Data Mining with ODM
SVM Model
Conclusion
What Makes Churn Prediction So Crutial?
Everybody faces the same difficulties…
Competition
Forming Customer Loyalty
High cost of customer acquisition
Optimizing budget for customer retention
People don’t want to hear any more
Basics of Churn Prediction
Churn prediction starts with turning an abundance of data into
valuable information and continues as a cyclic process.
Preparation
Data
 Define variable
pool
 Perform
mining ETL
Preprocessing
 Attribute
Importance
 Normalization
 Outlier Detection
 Missing Value
Cleanup
Action
Mining
 Build
 Test
 Apply
Information
Success Criteria
• Useless Action
• Customer
Annoyance
•
• Customer Loss
0/0
0/1
1/0
1/1
•
Agenda
About Turkcell Technology
Churn Prediction
Existing Mining System in Turkcell
Data Mining with ODM
SVM Model
Conclusion
Pain Points About Existing Mining System
Too much manual effort: A new project for every new mining activity
SAS licensing
E-DWH
DM-DWH SAS Server
Not leading, but lagging the business
Administrative overhead of distributed mining environment
 Network overhead
 Decoupled process monitoring
 Data quality problems
End Users
Approach in Existing Churn Model
Attribute Selection with
Human Expertise
Replace the existing model
with the best model for churn
prediction
Choose best model manually
Perform ETL
Build many models in serial
with different
• Algorithms
• Hyperparameters
Agenda
About Turkcell Technology
Churn Prediction
Existing Mining System in Turkcell
Data Mining with ODM
SVM Model
Conclusion
Give a Try to Oracle Data Mining
Motivations
 Building an automated mining framework based on our Oracle
database experience instead of maintaining manual mining model
cycle.
 No extra licensing cost (under ULA).
 High speed (close to real time) mining with database embedded
mining.
 Centralized mining activity monitoring & administration.
Our Proposal for Data Mining Framework
Feed all customer
attributes possible
Externalize those
models for APPLY
Let AI to filter
important ones
Train Oracle SVM
models with
selected attributes
Oracle
Choosing Attributes with Attribute Importance
--Perform EXPLAIN operation
BEGIN
DBMS_PREDICTIVE_ANALYTICS.EXPLAIN(data_table_name => 'census_dataset',
explain_column_name => 'class',
result_table_name => 'census_explain_result');
END;
/
--View results
SELECT * FROM census_explain_result;
COLUMN_NAME
-------------IN_REF_NUMDAYSSINCELASTREFILL
DT_SUB_ACTIVATIONDATE
IN_MNP_PORTINTENURE
NM_SUB_ACTIVATIONREASON
IN_MNP_TCELL_TENURE
.
.
.
EXPLANATORY_VALUE
----------------.141200904
.028200303
.026178093
.025882544
.025279836
RANK
---1
2
3
4
5
Top 5 by AI
Our Top 5 After AI
Number of days since last refill
Activation Date
Port in Tenure
Subscriber Activation Reason
Subscription Period in Turkcell
Build & Apply the SVM Model
--Perform PREDICT operation
DECLARE
v_accuracy NUMBER(10,9);
BEGIN
DBMS_PREDICTIVE_ANALYTICS.PREDICT(accuracy => v_accuracy,
data_table_name => 'census_dataset',
case_id_column_name => 'person_id',
target_column_name => 'class',
result_table_name => 'census_predict_result');
DBMS_OUTPUT.PUT_LINE('Accuracy = ' || v_accuracy);
END;
/
--View first 10 predictions
SELECT * FROM census_predict_result WHERE rownum < 10;
PERSON_ID
---------2
7
8
9
10
5 rows selected.
PREDICTION
---------1
0
0
0
0
PROBABILITY
----------.418787003
.922977991
.99869723
.999999605
.9999009
Other Remarks on ODM
 No need to perform manual attribute processing in many cases
 EDP : Embedded data preparation
 ADP : Automatic data preparation
 PL/SQL or Java based code generation
 SAS to ORACLE model import
•Eliminates data Movement
•Eliminates data duplication
•Preserves security
Agenda
About Turkcell Technology
Churn Prediction
Existing Mining System in Turkcell
Data Mining with ODM
SVM Model
Conclusion
Creating the Case Table
Variable Pool
(400 variables)
PREPAID and
INDIVIDUAL and
(ACTIVE or MOC-BARRED)
Filtered Variable Pool
JOIN MONTH(N)=MONTH(N+1)
CASE TABLE
Historic Churn Table
Building the SVM Model
CASE TABLE
•400 Attributes
•Unique Identifier
•Target Churn Value
ATTRIBUTE IMPORTANCE
CASE TABLE
(180 ATTRIBUTES)
FEB DATA  MAR CHURN
MAR DATA  APR CHURN
APR DATA  MAY CHURN
COMBINE DIFFERENT DATASETS
BUILD SVM MODEL
MAY DATA  JUN CHURN
ODM on Oracle Exadata v2
o Initially we have used a large
Solaris (100+ UltraSparc 7 cores
and 640 GB memory) box to build
our first SVM models:
• It took 29 hours to complete
model build & apply.
o On Exadata this reduces to a few
hours.
oMainly due to enormous
improvement in data preparation
stage.
Agenda
About Turkcell Technology
Churn Prediction
Existing Mining System in Turkcell
Data Mining with ODM
SVM Model
Conclusion
To Sum Up
 Churn prediction over various customer groups is and will
be the focus of Turkcell
 Embedded data mining with ODM is
 Faster
 More Robust (due to stability of SVM algorithm)
 Easier to automate
 Easier to manage
Thanks for his contribution
Hüsnü Şensoy, VLDB Expert
husnu.sensoy@globalmaksimum.com
Data & Information
Technologies
To learn more on SVM theory
Turkcell Technology Research and Development
TÜBİTAK MAM Teknoloji Serbest Bölgesi
Gebze – Kocaeli
TURKEY
': +90 (262) 677 40 00
7 : +90 (262) 677 40 01
8 : www.turkcelltech.com
THANK YOU!