Using survival analysis results

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Using survival analysis results

  1. 1. For business value add
  2. 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. 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. 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. 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. 6. event sneezing throat pain fatigue diarhoaea deathage0-20 3021-4041-60 661-8081-100 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. 7. event deathage0-20 3021-4041-60 1061-8081-100 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. 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. 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

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