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Shah Hussein (ShahH874)
British Telecommunications PLC

British Telecommunications PLC
ShahH874 Member since 2018 3 posts
British Telecommunications PLC
Posted: October 2, 2020
Last activity: October 7, 2020

Actual propensity before a model is trained


Does anyone know at a high level how pega works out the propensity for a proposition whose model is yet to be trained for the first time? i.e. The minim number of responses before the model is updated has not been reached.

Is is limited to basic principles like acceptance rate from the responses received so far?

I noticed that model evidence builds up although only a fraction of the actual responses are used before the model is updated. How does Pega decide which responses contribute to the propensity calculation and is it actually processing these responses and evaluating predictors etc or again is it based on more basic principles at this stage?

Any light you can shed is much appreciated. We use the smooth propensity concept in our implementation and as props converge towards actual propensity it's important to understand the make up of this propensity given that Pega cannot discriminate between customer profiles before a model has been through at least one round of training.

Thank you

***Edited by Moderator Marissa to update Platform Capability tags****
Pega Customer Decision Hub 7.4 Decision Management