P C M

voltaireblingData Management

Nov 20, 2013 (3 years and 11 months ago)

138 views

P
REPAID

C
HURN

M
ODEL

With Oracle Data Mining

Necdet

Deniz

Halıcıoğlu

deniz
.halicioglu@turkcellteknoloji.com.tr


September 21, 2010

Agenda

Conclusion

SVM

Model

Existing Mining System in Turkcell

Churn Prediction

About Turkcell Technology

Data Mining with
ODM

Agenda

Conclusion

SVM

Model

Existing Mining System in Turkcell

Churn Prediction

About Turkcell Technology

Data Mining with
ODM

Turkcell

Technology

has

more

than

15

years

of

development

experience

with

its

solutions

applied

and

proven

at

leading

operators

in

more

than

10

countries
.


2009

More than 10 years of
experience in Turkcell
ICT


TTECH Center was put
into service

HC: 255 engineers

Focus: Turkcell Group


Focus: Turkcell &
Telia Sonera Group +
Regional Sales

HC: 360 engineers


TTECH was formed with 44
engineers in TÜBİTAK
-
MAM
Technological Free Zone

Focus:
Turkcell

Focus: Turkcell &
Telia Sonera Group

HC: 321 engineers

2008


Today

2007

1994
-

2006

About

Turkcell

Technology

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 h
ighly

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
Minin
g, etc.

Agenda

Conclusion

SVM

Model

Existing Mining System in Turkcell

Churn Prediction

About Turkcell Technology

Data Mining with
ODM

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
.




Data

Preparation

Preprocessing

Mining

Information

Action



Define variable
pool



Perform
mining ETL



Attribute
Importance



Normalization



Outlier Detection



Missing Value
Cleanup



Build



Test



Apply

Success Criteria





Customer Loss


Useless Action


Customer
Annoy
ance




0/0

0/1

1/1

1/0

Agenda

Conclusion

SVM

Model

Existing Mining System in Turkcell

Churn Prediction

About Turkcell Technology

Data Mining with
ODM

Too much manual effort
:
A new project for every new mining

activity


SAS licensing


Not leading, but lagging the business


Administrative overhead of distributed mining environment



Network overhead



Decoupled process monitoring



Data quality problems


Pain Points About Existing Mining

System

E
-
DWH

DM
-
DWH

SAS

Server

End

Users

Approach in Existing Churn Model

Attribute Selection with
Human Expertise

Perform ETL

Build many models in serial
with different


Algorithms


Hyper
p
arameters

Choose best model manually

Replace the existing model
with the best model for churn
prediction

Agenda

Conclusion

SVM

Model

Existing Mining System in Turkcell

Churn Prediction

About Turkcell Technology

Data Mining with
ODM

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.

Give

a
Try

to

Oracle

Data
Mining

Oracle

Our Proposal for Data Mining Framework

Feed all customer
attributes possible

Let AI to filter
important ones

Train Oracle SVM
models with
selected attributes

Externalize those
models for APPLY

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



EXPLANATORY_VALUE


RANK

--------------




-----------------


----


IN_REF_NUMDAYSSINCELASTREFILL


.141200904


1

DT_SUB_ACTIVATIONDATE




.
028200303


2

IN_MNP_PORTINTENURE



.026178093


3

NM_SUB_ACTIVATIONREASON


.025882544


4

IN_MNP_TCELL_TENURE


.025279836


5

.

.

.

Our

Top

5
After

AI

Top 5 by AI

Number of days since last refill

Activation Date

Port in Tenure

Subscriber Activation Reason

Subscription Period in
Turkcell

--
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

PREDICTION

PROBABILITY

----------


----------


-----------


2



1


.418787003

7



0


.922977991

8



0


.99869723

9



0


.999999605

10



0


.9999009


5 rows selected.

Build

&
Apply

the

SVM

Model



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

Other Remarks on
ODM

Agenda

Conclusion

SVM

Model

Existing Mining System in Turkcell

Churn Prediction

About Turkcell Technology

Data Mining with
ODM



Creating the Case Table

Variable Pool

(400 variables)

JOIN MONTH(N)=MONTH(N+1)

Filtered Variable Pool

PREPAID and

INDIVIDUAL and

(ACTIVE or MOC
-
BARRED)


Historic Churn Table

CASE TABLE



Building

the
SVM Model

CASE TABLE


400 Attributes


Unique Identifier


Target Churn Value

ATTRIBUTE IMPORTANCE

FEB DATA


MAR CHURN

CASE TABLE

(180 ATTRIBUTES)

MAR DATA


APR CHURN

APR DATA


MAY CHURN

MAY DATA


JUN CHURN

COMBINE DIFFERENT DATASETS

BUILD SVM MODEL


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.

o
Mainly due to enormous
improvement in data preparation
stage.



Agenda

Conclusion

SVM

Model

Existing Mining System in Turkcell

Churn Prediction

About Turkcell Technology

Data Mining with
ODM



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

To

Sum

Up

Thanks for his

contribution

Data & Information

Technologies

Hüsnü Şensoy
,
VLDB Expert

h
usnu.sensoy@globalmaksimum.com

To learn more on SVM theory

Turkcell Technology Research and Development

TÜBİTAK
MAM

Teknoloji

Serbest

B
ö
lgesi

Gebze


Kocaeli

TURKEY


'

:

+90 (262) 677 40 00

7

:

+90 (262) 677

40 01

8

:

www.turkcelltech.com

THANK YOU!