Download Analytical CRM at Swisscom Fixnet - The data warehouse concept, mode development with Enterprise Miner and implementation

Analytical CRM at Swisscom Fixnet
The data warehouse concept, model development
with Enterprise Miner and implementation
Dr. Miltiadis Sarakinos
Swisscom Fixnet AG, Switzerland
Analytical CRM at Swisscom Fixnet
Analytical CRM: analysing customers and
understanding their behaviour
Data Analysis
• Data Mining
- Predictive Modelling
- Clustering/Segmentation
- CLTV
• Market Research
Campaign Design
Campaign Evaluation
-
- Target group definition
- Offer design
- Customer contact
programs
- Channel, Skill
Management
- Control mechanisms
Response analysis
Channel analysis
Skill analysis
Effectiveness of data
mining models and campaigns
Campaign Execution
Miltiadis Sarakinos, SEUGI 21, Vienna June 2003
-
Dialog programs
Retention management
Prevention management
Winback
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Analytical CRM at Swisscom Fixnet
The Data Warehouse Concept
Operational Systems
Data Mining
Business Analytical
Engine
Campaign Design
Analysis
Model
Billing
Samples
Campaign
Management
Direct Mail
DWH
Data Mart
External
Data
Customer
CCDB
Outbound
Reporting
Campaign Performance
Sales Force
Miltiadis Sarakinos, SEUGI 21, Vienna June 2003
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Analytical CRM at Swisscom Fixnet
Data Mining for CRM
Data Mining
Inbound Campaigns:
Call centers
Outbound Campaigns:
Marketing
Customer
Contact: special
offer, inform, etc.
Miltiadis Sarakinos, SEUGI 21, Vienna June 2003
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Analytical CRM at Swisscom Fixnet
Typical Predictive Modelling Scheme
Model Calibration
Nov
Apr
Model
May
Apply model to predict future
Deliver: customer score for
churn/winback and product
affinity
Miltiadis Sarakinos, SEUGI 21, Vienna June 2003
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Analytical CRM at Swisscom Fixnet
Business Rule to select offering
Example: Customer id 12345 inbound campaign
Age
Value
Prod B
Affinity
Prod A
Affinity
Risk
Churn
Business Ruleset
Propose Prod A
Miltiadis Sarakinos, SEUGI 21, Vienna June 2003
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Analytical CRM at Swisscom Fixnet
Closed Loop
! Record customer response, including refusals
! Do not duplicate offer:
– Do not disturb customer
– Save costs (mailing, call agent time etc)
! Response analysis (mine reaction data, campaing
results):
– Meaningful only if sufficiently large sample is
available.
Miltiadis Sarakinos, SEUGI 21, Vienna June 2003
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Analytical CRM at Swisscom Fixnet
Data Mining
Miltiadis Sarakinos, SEUGI 21, Vienna June 2003
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Analytical CRM at Swisscom Fixnet
The name of the game(1): Data Quality
! Internal Data are delivered with very high quality:
– no missing or wrong variables
! Demographic Data
– At least 70-80% coverage
Miltiadis Sarakinos, SEUGI 21, Vienna June 2003
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Analytical CRM at Swisscom Fixnet
The name of the Game (2): Data
Preparation
! Join data from different sources:.
Internet
Call
center
Revenues
Creditwor
thiness
SAS
Portfolio
Sociodem
Miltiadis Sarakinos, SEUGI 21, Vienna June 2003
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Analytical CRM at Swisscom Fixnet
The name of the Game (3): Data
Preparation
! Aggregate variables
– according to business sense, past experience, instinct:
eg aggregate over calltimes, destinations, tarifs etc
! Create several derivative variables:
– Subscription/Revenue
– Inland Traffic / Total Traffic
! Avoid categorical attributes with a large number of variables.
Group values within one attribute in a business relevant way:
e.g. instead of nationality create a flag foreigner, etc.
! Result: Initial number of variables used for model calibration
explodes. Currently, around 450, growing tendency
Miltiadis Sarakinos, SEUGI 21, Vienna June 2003
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Analytical CRM at Swisscom Fixnet
The Name of the Game (4): What about
model time stability?
! The challenge: model must generalise over time. Make a
model that predicts the future instead of just describing the
past
! What about customers going to vacation? Seasonal effects?
February is 10% shorter than March.
! Combine data over several months. Filter out short term
fluctuations, noise.
! Observation: our model profiles do not change dramatically
over time, hence we believe our models to be stable
Miltiadis Sarakinos, SEUGI 21, Vienna June 2003
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Analytical CRM at Swisscom Fixnet
The Name of the Game (5): Variable
Reduction
! Typical variable selection methods: decision tree, logistic
regression, variable selection node (chisq) but also variables
which from experience play a role
! Remove highly correlating variables
! Prune variable set for final model: try to achieve maximal
precision with the minimal variable set. Model is more likely to
be predictive in this way.
! Several iterations to clean up and believe the result
Miltiadis Sarakinos, SEUGI 21, Vienna June 2003
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Analytical CRM at Swisscom Fixnet
The Models
Miltiadis Sarakinos, SEUGI 21, Vienna June 2003
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Analytical CRM at Swisscom Fixnet
Churn/Winback
100%
Swisscom
High Churn
Rates
Market Share
Market Share
100%
Low Churn Rates,
Winbacks
Swisscom
Other Carriers
Other Carriers
Monopole falls
Today
Miltiadis Sarakinos, SEUGI 21, Vienna June 2003
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Analytical CRM at Swisscom Fixnet
Churn/Winback Modeling
! Consumer has several choices:
– Carrier Preselection
– Call-by-call
! Multiline (ISDN) users can have different preferred carrier for
each number
! Customer motives affected by various criteria/offers: ADSL,
frequent flyer miles etc
! Difficult and irrelevant to define in terms of switch to different
preferred carrier.
! Therefore: define in terms of revenue loss/gain
! Churner profiles not strongly different from loyal customer
profiles. Monthly rates are O(1%). Strongly differentiated
segments would decay quickly hence selfdestruct
Miltiadis Sarakinos, SEUGI 21, Vienna June 2003
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Analytical CRM at Swisscom Fixnet
Typical Flow
Neural Network Training Progress
Miltiadis Sarakinos, SEUGI 21, Vienna June 2003
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Analytical CRM at Swisscom Fixnet
Churn/Winback Model Residential
Customers
Cumulative Lift
6
5
Churn
4
Improvement with
respect to random
selection
3
Winback
2
1
Random Selection
0
0
10
20
30
40
50
Score Percentile
60
70
80
90
100
Example: The 10% of the customers with the highest score contain 30%
of potential churners, the 30% with the highest score capture about 60%
potential ISDN users etc.
Miltiadis Sarakinos, SEUGI 21, Vienna June 2003
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Analytical CRM at Swisscom Fixnet
Product Upselling
! Analog Customers ----> ISDN
– Due to ADSL: ISDN no longer for faster surfing.
– Different value proposition: Incoming call number
recognition, several simultaneous callers (families), SMS
! Ebill: personalized secure login. Phone call list, daily update,
manage account. Customer can suppress paper bill. Free
product but: cost saving, increases retention
Miltiadis Sarakinos, SEUGI 21, Vienna June 2003
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Analytical CRM at Swisscom Fixnet
Product upselling
100
90
80
70
60
50
40
30
20
10
0
Cumulative Gain
eBill
ISDN
No model random
selection
Improvement with
respect to random
selection
0
10
20
30
40
50
Score Percentile
60
70
80
90
100
Example: The 10% of the customers with the highest score contain 34%
of potential ISDN users, the 30% with the highest score capture about
65% of potential ISDN users etc.
Miltiadis Sarakinos, SEUGI 21, Vienna June 2003
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Analytical CRM at Swisscom Fixnet
Model Evaluation
! Models have been implemented in the least few
months
! Response rate analysis requires larger samples
Miltiadis Sarakinos, SEUGI 21, Vienna June 2003
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Analytical CRM at Swisscom Fixnet
Thank you
Miltiadis Sarakinos, SEUGI 21, Vienna June 2003
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