Question
Impression as negative outcome
Hi
All of our adaptive models have been setup (by our system integrator) to translate 'Impression' as a negative outcome. So let's assume a customer responds positively to an offer; Pega records a negative response (from the offer impression) as well as a positive response.
To me it does not seem correct that every time an offer is impressed it is also treated as a negative response. Firstly acceptance would be incorrect at 50% rather than 100% and secondly isn't this likely to confuse the model and yield incorrect propensities?
I can understand this works in scenarios where no positive or any other negative response is ever received, but that is not the scenario in question.
When I quizzed about this i was told 'Pega only uses the last response...' so in the example above only the positive response is considered. However I don't think this behaviour is true but at the time i was none the wiser.
Is my understanding incorrect or the implementation?
Thank you
For use cases where there is no explicit "negative" it is a recommended pattern to do it like you described, posting a negative for any impression then posting a "positive" when things get clicked. You are technically correct that this does, theoretically, skew the propensities a bit.
However, first of all this effect is almost always very small. Typical response rates are very low, so when propensity gets calculated as pos/(2*pos+neg) this is almost always very close to the actual number pos/(pos+neg). Secondly, we don't use the propensities as such. We (almost always) only use them in a relative way, and the ranking of the propositions is hardly affected by this "double counting". If you really, really want, there is a closed for formula to recover the unbiased propensity (divide by (1-p)) but we don't recommend doing that because it adds unnecessary complexity.
Lastly, in recent Pega versions we can deal with implicit negatives differently: you can define a "time out" on a response and only define the "positive" behavior. After the time out we set a negative if there is no positive. Since 7.4 this is a feature in CDH ("Outcome optimization") and recently, in Pega 8, this is a feature of Predictions.