In many modeling situations there is often a sub group of the target population on which the performance or target is not observed. In most cases, it is important that we understand how these subgroups would have performed had we been able to observe their performance. This process is called performance inference For example:
- There is a subgroup of Credit applicants who are either turned down (rejected) or walk away (accepted but refuse the offer). What would their credit performance have been had they taken the credit?
- Prospects who are mailed sales offer, but don’t respond. What would they have bought had they responded?
- Insurance applicants who are offered an insurance policy but walk away from the offer. What losses would they have incurred had they taken the offer?
These are all examples where performance inference could be important. Essentially, performance inference is a “What if?” exercise. It is typically used to understand the full population under consideration in the business situation of concern:
- In credit or insurance – The full population is anyone who might apply for a loan or insurance. This is known as the Through The Door (TTD) population.
- In marketing – The full population is the people for whom the products are intended. This could be limited by:
- Geography (just people who live in Chicago, IL),
- Interest (just people who attend the Opera)
- Behavior (people who fly internationally)