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Uncovering the true Customer Value by using Survival Analysis
Marie WilloCustomer Intelligence and Rewarding Manager – AXA BelgiumChloé Van VreckemCustomer Insights Consultant – 4C Consulting
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accessibleeverywhere
loves (to know)
their customers
connectedwhen needed
highly profitable
engages in every
interaction
we build customer companies
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3
No global
view of the client
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No Time
Dimension
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5
No golden
standard to serve
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CUSTOMER MARGIN
Current customer revenue
CUSTOMER RETENTION
Customer tenure
CUSTOMER EXPANSION
Future customer revenue
CLV is based on 3 building blocks
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CustomerValueManagement
at AXA Belgium
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CUSTOMER MARGIN
Current customer revenue
CUSTOMER RETENTION
Customer tenure
CUSTOMER EXPANSION
Future customer revenue
CLV is based on 3 building blocks
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The origin
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Medicine‘Time is Crucial’
Business‘Time is Money’
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Why considering
Survival Analysis??
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Based on 1 model a global picture can be created of customer behaviour throughout time
1
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Evaluation campaign on arbitrary points in time
Churn
Classic marketing program
30% stays
New marketing program
50% staysAFTER 12 MONTHS
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Evaluation campaign on arbitrary points in time
Churn
Classic marketing program
20% stays
New marketing program
21% staysAFTER 16 MONTHS
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Highlight moments in time where customers are at higher ‘risk’ to leave the company
New marketing program
Classic marketing program
Event = churn
Time (months)
Survival probability
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More technical…
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Probability to survive at any point in time: St =
Total probability of survival till that time:=
ni: # ‘survivors’ just prior time ti
di: # ‘deaths’ at time ti
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We can use the entire population
2
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Customers ‘out of risk’
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By censoring customers(out of risk), all available informationis used
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3Model variables give valuable customer insights for direct marketing campaigns
3
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Which Statistical models??
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Cox proportional hazard model
Most common used model for survival data (*)• Flexible choice of covariates • Fairly easy to model • Standard software exists • Well developed elegant mathematical theory
Few distributional assumptions • Non informative censoring • Proportional hazards • Independence
(*)Goetghebeur E and Van Rompaye B. Survival analysis edition 2011
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0 1 2 3 4 5 6 7 8 9 10 11 120%
20%
40%
60%
80%
100%
S(t)=Survival curve F(t)=Cumulative Incidence
Time (months)
Definitions
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0 1 2 3 4 5 6 7 8 9 10 110%
5%
10%
15%
20%
25%
30%Incidence Hazard
Definitions
Time (months)
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Time Survival Curve Cumulative incidence Incidence Hazard
0 100% 0% 20% 20%
1 80% 20% 20% 25%
2 60% 40% 10% 17%
3 50% 50%
Definitions
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Time Survival Curve Cumulative incidence Incidence Hazard
0 100% 0% 20% 20%
1 80% 20% 20% 25%
2 60% 40% 10% 17%
3 50% 50%
Definitions
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Hazard
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=baseline hazard, ,… , = covariates
Cox proportional hazard model
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=baseline hazard
=-0.7 exp()=0.5
The hazard of men leaving the company is half of the hazard for women.
Cox proportional hazard model
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Classic regression ignores time – time is crucialSolution: survival analysis
Advantages Use of entire sample Instantaneous risk estimation
Conditions Non informative censoring Proportional hazards Independence
In summary…
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CUSTOMER MARGIN
Current customer revenue
CUSTOMER RETENTION
Customer tenure
CUSTOMER EXPANSION
Future customer revenue
Value of the client?
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Target customers with the highest Customer Lifetime Value
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IncreasingBusinessRevenue
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