Adaptive Modelling - Propensity per proposition/customer
While testing Adaptive Models we faced some questions regarding propensity calculation.
According to PEGA's course manual (Student Guide - PEGA_Decisioning), the adaptive model computes a propensity for each proposition input. However, since it is a learn on the fly model and the likelihood of a customer accepts an offer slightly changes if a customer with similar profile accepts the same offer, we were expecting to get a propensity per proposition based on customer properties, i.e., two customers with different properties should have different propensities for the same proposition.
On the model report – Score Distribution tab we have propensities for each bin, however when we call the model from a strategy, the output propensity is always the same.
How is the output propensity calculated?
Shouldn’t it be dependent on the customer?
Note: The Output Propensity (for the model in attachment) is always 0,5620.