Download Customer Insight development in Vodafone Italy

Customer Insight
development in
Vodafone Italy
Emanuele Baruffa – Vodafone
Seugi - Vienna, 17-19 June 2003
Seugi 21_Vienna
Pag. 1
Contents:
1.
Introduction
2.
Customer Base Management
3.
Customer Insight
4.
Data Environment
5.
Conclusions
Seugi 21_Vienna
Pag. 2
1.
Introduction
2.
Customer Base Management
3.
Customer Insight
4.
Data Environment
5.
Conclusions
Seugi 21_Vienna
Pag. 3
Mobile telephony is one of the fastest
growing industries in the world
Worldwide growth in subscribers (millions)
! 1 billion
subscribers
around the world
1480
1321
1152
! Over 120
countries have
mobile networks
958
727
! Further
acceleration
expected
479
206
Source: EITO
Seugi 21_Vienna
14
34
1991
1993
87
1995
1997
1999
2000
Pag. 4
2001
2002
2003e
2004e
Italy: Europe’s second biggest mobile market
Country
Subscribers
Western European TLC market
growth by country (% )
Penetration
%
10
9,1
9
Germany
60,300,000
84%
8
Italy
UK
54,000,000
50,900,000
98%
92%
6
6,8
6,8
7
6,2
5,6
5,6
5,0
5
5,7
6,1
5,4
5,8
4,0
4
France
39,000,000
77%
Spain
34,000,000
88%
3
2
1
0
Germany
Sources: internal sources for Italy, Yankee Group for
other European countries
Seugi 21_Vienna
Source: EITO
Italy
UK
2001/2002
Pag. 5
France
2002/2003
Spain
Western
Europe
Penetration rate in the Italian market
96%
60,000
91%
Subscribers (,000)
50,000
98%
90%
Penetration Rate
80%
74%
70%
40,000
60%
53%
30,000
50%
36%
40%
20,000
30%
21%
10,000
100%
20%
11%
10%
0
0%
1996
1997
1998
Seugi 21_Vienna
1999
2000
2001
Pag. 6
2002
2003
Italian market shares
Total subscribers on 31.03.03
17%
36%
47%
Seugi 21_Vienna
Pag. 7
Vodafone Italy Customer Base
18,900,000
17,400,000
14,920,000
10,418,000
6,190,000
2,460,000
713,000
Dec. 1996 Dec. 1997 Dec. 1998 Dec. 1999
Seugi 21_Vienna
Dec. 2000 Dec. 2001 Dec. 2002
Pag. 8
1.
Introduction
2.
Customer Base Management
3.
Customer Insight
4.
Data Environment
5.
Conclusions
Seugi 21_Vienna
Pag. 9
Customer management strategy
! Strategy: Consolidate leadership through customer base management
! Marketing Goals: meet customers’ needs and identify the best treatment to each
customer at the right time through the most suitable channel at the appropriate
cost. This approach helps to increase
! customer loyalty
! customer value (ARPU)
How
! create customer insight building a Customer Centric DB (CKM) that allow to:
! Identify segments for cross-selling actions " Customer Segmentation
! Identify customers with highest value potential for new high value added
services such as SMS, Voice Mail " Propensity to uptake VAS
! Identify customers with highest churn probability " Churn propensity score
Seugi 21_Vienna
Pag. 10
1.
Introduction
2.
Customer Base Management
3.
Customer Insight
4.
Data Environment
5.
Conclusions
Seugi 21_Vienna
Pag. 11
Approach for developing customer insight
1. Brainstorming
2. Gather
comprehensive
customer information
3. Analyze and segment
customer base
6. Fine Tuning
5.
Measure
results
Seugi 21_Vienna
4. Data Mining
Pag. 12
Customer Value Score
% CB
% Margin
70-80%
~ 30%
High value
customers
~ 70%
Low value
20-30%
customers
20-30%
! High value customers
make up for majority of
margins
! Margins are relevant and
not revenues because some
high spenders also have high
interconnection cost
! Resources should be
allocated proportionally to
margins generated
Value is calculated on a monthly basis for each customer. Customer value is
measured based on a four month average Marpu
Seugi 21_Vienna
Pag. 13
Churn Modelling
MACRO
SEGMENTATION
TARGET
DEFINITON
DATA
ANALYSIS
•Define the event •Data Exploration
•Identify
– Correlation
relevant market you want to
between target
predict
segments
variable and
• Example
• Example
– Inactive SIM explanatory
– Consumer
variables
for Prepaid
# Prepaid
– Data
Customers
# Contracts
– “Cancellation transformation
– Corporate
#Trends
letter” for
# Small
#Grouping
Contract
Accounts
Customers
# Large
Accounts
MODEL
MICRO
SEGMENTATION BUILDING
•Identification of •Oversampling
• Application of
groups with
data mining
similar churn
techniques
behaviour
– improvement – Logistic
regression
of model
–Decision tree
accuracy
– Neural
–Focus on:
Networks
# “Active”
customers •Measurement
# High value of the goodness
of fit (model
customers
validation on
hold-out
sample)
MODEL
IMPLEMENTATION
•Implement
model in a
production
environment
• Monthly
production
of churn
index
MODEL
EVALUATION
•Ex-post
evaluation of
model
performance
– % of correct
predictions
Analyse churn behavior of actual churners to predict churn behavior of the customer base
Seugi 21_Vienna
Pag. 14
Churn Propensity Score
Model Lift
High Risk to churn
Medium Risk to churn
5.5
Low Risk to churn
3.2
0.5
Red
Yellow
Green
The likelihood to churn is estimated on a monthly basis for each VO customer.
Customers flagged in RED are five times more likely to churn than an average
customer.
Seugi 21_Vienna
Pag. 15
Segmentation Methodological Road
Map
Segmentati
on Data
Mart
Data
Analysis
Clusterin
g
Segmentati
on
Algorithm
Cluster analysis
Factoral
Application
Build a data
Analysis to
of
mart with all
•Started from a
identify main large number of
segmentatio
segmentation
dimensions
n rules to
variables
micro-clusters
of
the entire
•Demographic
•Then
reduced/
segmentatio
customer
data
n
base
•Voice Traffic
aggregated
•Customers
(peak-offpeak,
clusters to a
Elimination
are assigned
network,
“manageble” and
of outliers
to the
discounted
meaningful
nearest
tariffs)
number
cluster (rule:
•Service usage
minimum
(wap, gprs,
sms, mplay,
Cluster validation Euclidean
distance
music, …)
with market
clusterof
•Propensity
to
Customers
are assigned to segments based on from
their usage
research
centroid)
VAS uptake
mobile services, attitude towards technologies and
their lifestyles
Seugi 21_Vienna
Pag. 16
Propensity to Uptake VAS
Developed and implemented statistical models to predict on a monthly basis
the likelihood of a VO customer to uptake five different Value Added Services.
SMS
No Users SMS
Low Users SMS
ADVANCED
SMS
High Users SMS
not using Flash
SMS
WAP
INTERNET
SELF CARE
VOICE
MAIL
No registered
customers on
Vodafone Italy web
site
No Users Voice
Mail
WAP handset
owners not using
WAP
High propensity customers are on average three times more likely to
start using a service than an average customer
Seugi 21_Vienna
Pag. 17
1.
Introduction
2.
Customer Base Management
3.
Customer Insight
4.
Data Environment
5.
Conclusions
Seugi 21_Vienna
Pag. 18
Customer Knowledge Management
System (CKM)
Raw data
DWH (Oracle)
Demographics
Traffic
Service Usage
Handset
Loyalty
…………
daily +
monthly
Campaign
Management
System
CKM
(Oracle)
DSS Analysis
(Microstrategy)
End User (Web)
Seugi 21_Vienna
Data Mining (SAS)
ORACLE
ORACLE ++ File
File System
System SAS
SAS
End User Data Mining (SAS
Miner, SAS Stat, ...)
Pag. 19
CKM Modelling Environment
SAS Access
Oracle ®
SAS CKM Application
Campa Campa gne
gne
D
W
H
…….
…….
Anagrafi
Anagrafi
ca
ca
Fase 1
SAS Connect ®
CKM
CKM
Segmentation
& Sampling
Data
Transformation
SAS Enterprise
Miner ®
Model
Development
Rules
Fase 2
Score
Score
SAS Warehouse
Administrator ®
Seugi 21_Vienna
Metadata
Pag. 20
SAS CKM Application
!
A Graphical User interface: has been developed to interact
with the Customer DataMart.
!
Some of the functions of this GUY:
$
Dynamical datamart: extraction and definition of subuniverse for a given temporal interval with the maximum
flexibility
$
Mining :
!
1.
Development of a statistical model with SAS/Enterprise
Miner®
2.
Export of the model
Assessment of predictive models when deployed: allow to
verify performance on model (to measure degradation) and
doing ex-post analysis on redemption.
Seugi 21_Vienna
Pag. 21
1.
Introduction
2.
Customer Base Management
3.
Customer Insight
4.
Data Environment
5.
Conclusions
Seugi 21_Vienna
Pag. 22
In Summary…
A successful strategy needs to be based on a good
understanding of customer needs by customer groups
Customer Insight simple to
understand in order to become a real working tool for all
Develop
parts of the organization (customer care, marketing, sales).
Take actions on customer insight and fine tune
models to improve results over time
Reasons to Fail:
% Lack of strategy
% Lack of data
% Lack of statistical skills
% Lack of commitment from Marketing CRM/Customer operations
Seugi 21_Vienna
Pag. 23