customer insight development in vodafone italy 21_vienna pag. 1 customer insight development in...

Pag. 1Seugi 21_Vienna

Customer Insight development in Vodafone Italy

Emanuele Baruffa – Vodafone

Seugi - Vienna, 17-19 June 2003


ContentsContents::

1. Introduction

2. Customer Base Management

3. Customer Insight

4. Data Environment

5. Conclusions


1. Introduction


3. Customer Insight

4. Data Environment

5. Conclusions


Mobile telephony is one of the fastest Mobile telephony is one of the fastest growing industries in the worldgrowing industries in the world

! 1 billion subscribers around the world

! Over 120 countries have mobile networks

! Further acceleration expected

Source: EITO

Worldwide growth in subscribers (millions)

14 3487

206

479

727

958

1152

1321

1480

1991 1993 1995 1997 1999 2000 2001 2002 2003e 2004e


Italy: Europe�s second biggest mobile marketItaly: Europe�s second biggest mobile market

Country Subscribers Penetration%

Western European TLC market growth by country (%)

5,65,0

4,0

5,7

6,8

5,8

9,1

6,25,6 5,4

6,16,8

0

1

2

3

4

5

6

7

8

9

10

Germany Italy UK France Spain WesternEurope

Source: EITO 2001/2002 2002/2003

Germany 60,300,000 84%

Italy 54,000,000 98%

UK 50,900,000 92%

France 39,000,000 77%

Spain 34,000,000 88%

Sources: internal sources for Italy, Yankee Group for other European countries


Penetration rate in the Italian market Penetration rate in the Italian market

91%

74%

53%

36%

21%11%

0

10,000

20,000

30,000

40,000

50,000

60,000

1996 1997 1998 1999 2000 20010%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%Subscribers (,000)Penetration Rate

96% 98%

2002 2003


Italian market sharesItalian market shares

Total subscribers on 31.03.03

36%

17%

47%


Vodafone Italy Customer BaseVodafone Italy Customer Base

713,0002,460,000

6,190,000

10,418,000

14,920,00017,400,000

Dec. 1996 Dec. 1997 Dec. 1998 Dec. 1999 Dec. 2000 Dec. 2001

18,900,000

Dec. 2002


1. Introduction


3. Customer Insight

4. Data Environment

5. Conclusions


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


1. Introduction


3. Customer Insight

4. Data Environment

5. Conclusions


Approach for developing customer insightApproach for developing customer insight

2. Gather comprehensive

customer information

3. Analyze and segment customer base

4. Data Mining5.

Measure results

6. Fine Tuning

1. Brainstorming


Customer Value ScoreCustomer Value Score

~ 30%

20-30%

High value High value customerscustomers

Low value Low value customerscustomers

% CB % Margin

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

70-80%

~ 70%

Value is calculated on a monthly basis for each customer. Customer value is measured based on a four month average Marpu


Churn Churn ModellingModelling

MACROSEGMENTATION

TARGETDEFINITON

DATA ANALYSIS

MICROSEGMENTATION

MODELBUILDING

MODELIMPLEMEN-TATION

MODELEVALUATION

�Identify relevant market segments

� Example� Consumer# Prepaid# Contracts

� Corporate# Small Accounts# Large Accounts

�Define the event you want to predict

� Example� Inactive SIMfor Prepaid Customers� �Cancellation letter� for Contract Customers

�Data Exploration� Correlation between target variable and explanatory variables� Data transformation#Trends#Grouping

�Identification of groups with similar churn behaviour

� improvement of model accuracy�Focus on:# �Active� customers# High value customers

�Oversampling� Application of data mining techniques

� Logistic regression�Decision tree� Neural Networks

�Measurement of the goodness of fit (model validation on hold-out sample)

�Implement model in a production environment

� Monthly production of churn index

�Ex-post evaluation of model performance

� % of correct predictions

Analyse churn behavior of actual churners to predict churn behavior of the customer base


Churn Propensity ScoreChurn Propensity Score

Model Lift

5.5

3.2

0.5

Red Yellow Green

High Risk to churn

Medium Risk to churn

Low Risk to churn

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.


Segmentation Methodological Road Map

Segmentation Data

Mart

Data Analysis

Clustering

Segmentation

Algorithm

Build a data mart with all segmentation variables•Demographicdata•Voice Traffic(peak-offpeak, network, discounted tariffs)•Service usage(wap, gprs, sms, mplay, music, …)•Propensity to VAS uptake

Cluster analysis•Started from a large number of micro-clusters•Then reduced/ aggregated clusters to a “manageble” and meaningful number

Cluster validationwith market research

Application of segmentation rules to the entire customer base•Customers are assigned to the nearest cluster (rule: minimum Euclidean distance from cluster centroid)

FactoralAnalysis to identify main dimensions of segmentation

Elimination of outliers

Customers are assigned to segments based on their usage of mobile services, attitude towards technologies and their lifestyles


Developed and implemented statistical models to predict on a monthly basis the likelihood of a VO customer to uptake five different Value Added Services.

Propensity to Uptake Propensity to Uptake VASVAS

SMSSMS No Users SMS

Low Users SMS

ADVANCEDADVANCEDSMSSMS High Users SMS

not using Flash SMS

WAPWAPWAP handset owners not using WAP

VOICE VOICE MAILMAIL

No Users Voice Mail

INTERNET INTERNET SELF CARESELF CARE No registered

customers on Vodafone Italy web site

High propensity customers are on average three times more likely to start using a service than an average customer


1. Introduction


3. Customer Insight

4. Data Environment

5. Conclusions


Customer Knowledge Management Customer Knowledge Management System (CKM)System (CKM)

DWH (Oracle) (Oracle)

CKM(Oracle)

daily + monthly

DSS Analysis (Microstrategy)DSS Analysis (Microstrategy)

DWH Campaign ManagementSystem

Campaign ManagementSystem

Raw dataDemographicsTrafficService UsageHandsetLoyalty��

Data Mining (SAS) ORACLE + File System SASData Mining (SAS) ORACLE + File System SAS

End User Data Mining (SAS Miner, SAS Stat, ...)

End User Data Mining (SAS Miner, SAS Stat, ...)End User (Web)End User (Web)


CKM Modelling EnvironmentCKM Modelling Environment

Fase 2Fase 2

Fase 1Fase 1

CKMCKM

DWH

Campa-gne

Campa-gne

��.��.

Segmentation & Sampling

DataTransformation

DataTransformation

Model Development

Model Development

ScoreScore

RulesRules

Metadata

Anagrafica

AnagraficaAnagrafi

ca

Anagrafica

SAS EnterpriseMiner ®

SAS EnterpriseMiner ®

SAS WarehouseAdministrator ®SAS WarehouseAdministrator ®

SAS AccessOracle ®

SAS AccessOracle ®

SAS Connect ®SAS Connect ®

SAS CKM Application


SAS CKM ApplicationSAS 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 sub-universe 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.


1. Introduction


3. Customer Insight

4. Data Environment

5. Conclusions


In Summary�In Summary�

A successful strategy needs to be based on a good understanding of customer needs by customer groups

Develop Customer Insight simple to understand in order to become a real working tool for all parts of the organization (customer care, marketing, sales).

Take actions on customer insight and fine tunemodels 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

customer insight development in vodafone italy 21_vienna pag. 1 customer insight development in...

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