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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
accessibleeverywhere
loves (to know)
their customers
connectedwhen needed
highly profitable
engages in every
interaction
we build customer companies
3
No global
view of the client
No Time
Dimension
5
No golden
standard to serve
CUSTOMER MARGIN
Current customer revenue
CUSTOMER RETENTION
Customer tenure
CUSTOMER EXPANSION
Future customer revenue
CLV is based on 3 building blocks
CustomerValueManagement
at AXA Belgium
CUSTOMER MARGIN
Current customer revenue
CUSTOMER RETENTION
Customer tenure
CUSTOMER EXPANSION
Future customer revenue
CLV is based on 3 building blocks
The origin
Medicine‘Time is Crucial’
Business‘Time is Money’
Why considering
Survival Analysis??
Based on 1 model a global picture can be created of customer behaviour throughout time
1
Evaluation campaign on arbitrary points in time
Churn
Classic marketing program
30% stays
New marketing program
50% staysAFTER 12 MONTHS
Evaluation campaign on arbitrary points in time
Churn
Classic marketing program
20% stays
New marketing program
21% staysAFTER 16 MONTHS
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
More technical…
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
We can use the entire population
2
Customers ‘out of risk’
By censoring customers(out of risk), all available informationis used
3Model variables give valuable customer insights for direct marketing campaigns
3
Which Statistical models??
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
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
0 1 2 3 4 5 6 7 8 9 10 110%
5%
10%
15%
20%
25%
30%Incidence Hazard
Definitions
Time (months)
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
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
Hazard
=baseline hazard, ,… , = covariates
Cox proportional hazard model
=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
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…
CUSTOMER MARGIN
Current customer revenue
CUSTOMER RETENTION
Customer tenure
CUSTOMER EXPANSION
Future customer revenue
Value of the client?
Target customers with the highest Customer Lifetime Value
IncreasingBusinessRevenue
