1. For business value add

2.  Very powerful: tells you not only who but also when
 Who is more likely to die, kill, get cured, go bankrupt,
attrite, drop spend, catch a cold, etc etc and when:
sort of like astrology 
 The who part can also be answered by other methods:
logistic regression, and a host of segmentation
techniques comes to mind
 The when part is the most attractive part about
survival (or astrology). When is a person likely to
attrite, when is he likely to die, or experience an
accident (or get a job, get married, go abroad:
astrology)

3.  One bad thing about survival analysis. It isnt as clear about
the when as an astrologer. For every event you can think
of, a survival analysis model will give you a host of
probabilities. But you’ll have to do the interpretation part
 so:
 A doctor asks when should i give the treatment to this flu
patient?
 An actuary asks what premium should I set for this guy who
wants accident insurance?
 A telecom company asks, who out of my prepaid customers is
likely to walk out and when?
 What do you tell them? They dont wanto look at you host
of probabilities. They are paying you to do that

4.  3 outputs from survival analysis we would consider
 The chances of surviving till particular period
▪ Prob that an accident victim would not die 3 days from today
 The expected lifetime of an individual or a group
▪ The period when the survival probability of the indv becomes 50%
▪ The time when 50% of the group would have had the accident in 6
years
 The chances of the event occurring in a particular interval
given that it has not occurred till before that
▪ Given that a telecom customer has not attrited till the third month,
what are his chances of attriting between periods 3 and 6

5. The most detailed information that you can
provide is a table of expected time to event
for every possible individual profile and all
events of interest

6. event sneezing throat pain fatigue diarhoaea death
age
0-20
30
21-40
41-60
6
61-80
81-100
 This means 50% of the patients in the 61-80 age group
are likely to have throat pain in 6 days from the
inception of the disease compared to 21-40 age group
people.
 So the doc better treat the older people first

7. event death
age
0-20
30
21-40
41-60
10
61-80
81-100
 This means 50% of the patients in the 61-80 age group are
likely to die in 10 years compared to the 21-40 age group
people, half of whom are likely to live for 30 more years
 So the actuary would charge higher premium for selling life
insurance to the seniors since their chances of dying are
much higher and sooner

8. event attrition
spend
0-20
21-40
41-60
10
61-80
30
81-100
 2 of the highest spend groups have dramatically different median survival times
 This means 50% of the customers in the 61-80 spend group are likely to stop
renewing in 10 months compared to the 81-100 spend group people, half of
whom are likely to continue renewing for 30 more months
 So the marketer would provide more freebies like free roaming, lower call
charges, free ringtones, etc to the 61-80 group and before 10 months. For the 81-
100 group, it can wait for 15-20 more months
 Once the treatment is given, it can be measured whether it was effective in
extending the tenure

9.  The treatment to be given to prevent or takle
advantage of the event is out of the scope of survival
analysis
 Survival analysis can be used to measure the effect of
these treatments too, once they are given
 Survival analysis models would typically produce a
much more detailed profiling rather than only one
variable like age, spend etc.
 Be careful where the expected lifetime is way into the
future, the assumption is that whatever was
happening in the modelling period would continue;
maybe true for medical data, but usually untrue for all
others