Advanced Analytics for Call Center Operations
Speaker Bio
•
Ali Çabukel
•
Graduated from Hacettepe University Statistics department
•
Experienced in development, DWH, Advanced Analytics
projects in many sectors including service, telecommunication
and call center
•
Kübra Fenerci Canel
•
Msc. in Bogazici University industrial engineering
•
Currently, Big Data Solutions Lead at Oracle
Agenda
•
Turkcell Global Bilgi Company Profile
•
Global Bilgi Data Mining Process
•
Project Timeline
•
Oracle Advanced Analytics Advantages for Global Bilgi
•
Project I: Collection (accessibility) Scoring
•
Project II: IVR Analytics
•
Project III: Agent Analytics (ongoing)
•
Next Steps
•
Q&A
Turkcell Global Bilgi Company Profile
§
Was established in 1999 and provides services with a total of 25 locations,
including 20 in Turkey, 4 in Ukraine and 1 in Russia, with over 12.000
§
Expertise in the telecommunication, public sector, finance, energy and retail
industries
§
§
The company creates value in the fields of customer services, customer
acquisition, telesales, technical support, customers retention and loyalty,
collections, customer information management and analysis
§
§
Won 1st place in the category of “Best Customer Experience and Application”
at the Contact Center World's 2014 Top Ranking Performance Awards, the
Global Bilgi Data Mining Process
Oracle DWH, feeded by different source systemü Data Integration Layer – Data Pool
ü Data Summarization Layer Presentation Layer
OAA Option ODM + ORE June 2015+
OAA Advantages for Global Bilgi
Analysing data in original environment
Ensuring data consistency
Facilitating all cycle management
-
In DB analytics
Contact center data is big enough
!
Using in DB R functionalities without memory limitations in a
-
,
scalable architecture
1
OAA Advantages for Global Bilgi
Building Dynamic Structures
Contribution
of
PL SQL
/
,
ORE
and
DBMS_DATA_MINING
packages for dynamism
ODM
–
GUI based workflows with SQL flexibility
Workflow Logic
Providing data preparation modelling and deployment in the
,
same flow
3
Collection Scoring
:
Business Requirement We reach our customers who are late in payment to,
- .
remind their debt and convince them about on time bill payment In this
, .
project we aim at reaching customers faster and more efficiient :
Solution Suggestion Analysing past behaviour of the customers and building , .
predictive models for call period :
Action Prioritization of calling customers regarding their accessibility scores In . ,
the medium run reaching from different channels depending on their score .
segments
:
Expected Benefit Increasing profitability and operational efficiency by calling
« ».
prioritization as a result of increasing customer access ratio :
Realized Benefit Increased customer access ratio 12 % after our first calls :
Collection Scoring
Call detail and other traces of our customers in our system
are used
Investigated 270 different variable
o
Analytics functions
o
Derived attributes
o
Trend variables
oNormalizations
o
Processed 37 Mio call details
Scores 2 mio customers every month
Collection Scoring
Data Preparation
oDefining target variable oData Quality
oDesigning Integrated data envrionment oData Decomposition
oAnalytics Datamart Design oSampling
Data Analysis
oInvestigating Variables
oClassification and Weighting oVariable Selection
Modelling
oPredictive Modelling / GLM Deployment
Collection Scoring
Understanding Business Needs
:
Understanding business need
Understanding faster reachable customers in collection list so that business will be able to reach more debtors and save time
:
Scope
2014 Feb 2015 May–
Individual customers and
Collection Scoring
Collection Scoring
Data Preparation
.
Feb 14 Mar 14. Apr 14. May 14. June 14. July 14. Aug 14. Sept 14. Oct 14. Nov 14. Dec 14. Jan 15. Feb 15.
TRAINING
.
May 14June 14. July 14. Aug 14. Sept 14. Oct 14. Nov 14. Dec 14. Jan 15. Feb 15. Mar 15. Apr 15. May 15.
VALIDATIO N
Data is splitted for each campaign
For training and validation data is grouped by GSM,
Target variable definition is done
Observation period is divided into quarters and activeness is investigated ,
Recent data Will be modelled ( )
Non recent data Will be modelled- ( )
New comers= Who are in collection list for performance period but ,
OBSERVATION PERIOD (L12M) PERFORMANCE ( ) PERIOD M13
Collection Scoring
Data Preparation
: /
Example Total call time number of collections
:
Example Last 6 months/last 12 months call time 203 variables ( derived gsm ) basis from 23 ( variables from ) call data 90 out of 203 new variables are derived using business aspect
Collection Scoring
Data Preparation
§ORE is used for binning WOE and IV calculations WOE values of , .
.
high IV variables are integrated to modelling dataset
§Higher the variable IV more explanatory on target ,
§Using binning extreme values and missing values are, handled without data loss
§ODM Attribute importance is also used to understand .
explanatory variables
§
§Correlation matrices are calculated using ORE and using highly,
correlated inputs is avoided
Collection Scoring
IVR Analytics
:
Business Requirement
Understanding underlying reasons of not
«
»
.
finding a solution on IVR channel and directing to agent
:
Solution Suggestion
What are the patterns of customers who
,
could not solve their issues on 1st IVR interaction? What are the
differences between customers who find a solution on 1st IVR
./
.
int customers who cannot find a solution on 1st IVR int
:
Action
Optimizing IVR process revising menu design In the
,
.
,
.
medium run real time actions about our customers on IVR
:
Expected Benefit
Increasing operational efficiency customer
,
.
experience and service quality
:
IVR Analytics
Call detail and other traces of our customers in our system are used
o
IVR master IVR details tables
/
o
Training data 15 04 2015 30 09 2015
:
.
.
-
.
.
Investigated 280 different variable
o
Analytics functions
o
Derived attributes
o
Trend variables
oNormalizations
o
Pattern recognition function in DB 12c
IVR Analytics
Data Preparation
oDefining target variable oData Quality
oDesigning Integrated data envrionment oData Decomposition
oAnalytics Datamart Design oSampling
Data Analysis
oInvestigating Variables
oClassification and Weighting oVariable Selection
Modelling
oClassification models decision trees SVM Naive Bayes/ / / oAsscociation Rules
