Dichotomous or Binary: This is a target that takes on two primary states or values:
- Good/Bad – This is the typical binary target for credit risk. Good is usually defined as paid on time as promised. Details of the specific definition are given in the section below.
- Response/Non-response – This is a common target for direct mail solicitations or telemarketing. Usually it is defined as someone who responded positively to an offer.
- Purchase/Not Purchase – This is also a common target for direct mail or telemarketing and is restricted to the responders, classifying them as responding and purchasing or refusing to purchase after a response.
- Attrition/Retention – In studies of existing costumers (credit card, insurance, phone service, cable, magazine subscribers …), it is often important to be able to predict who will attrite or cancel or lapse/non-renew vs. retain the service.
- Fraud/Non-fraud – Is a particular transaction legitimate or fraudulent? A transaction could be an insurance claim, a credit card purchase an application for a service, identity theft, …
- Above or Below a threshold – Sometimes, an exact prediction is not needed, just whether or not a customer will have above or below some threshold on a continuous target, such as revenue, recovery, # transactions, … .
Continuous: This is a target that can take on a wide range of values that have meaningful numeric values (not just numbered classifications):
- Revenue – How much revenue can a customer be expected to generate over time?
- Purchase Amount – How much will an individual purchase in one transaction?
- Losses – How large an insurance loss is a consumer expected to incur over time?
- Recovery – What percent of a credit loss is likely to be recovered?
- Miles driven – What are the actual miles an auto insured drives in a year?
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