Question
How does Adaptive model use Predictors?
Is it good practice to define both Proposition properties along with Customer properties as Predictors? Or should only the Customer properties be used for Predictors?
While calculating Propensity, does the Adaptive model take into account other properties of the Proposition and Customer than what have been defined as Predictors? OR is it just what have been defined as Predictors - Will it check what combination of Predictors received positive responses?
If its only the defined ones, then is it fair to say just having Customer properties as Predictors will not help?
Is there any document or link available that can give more insight into how the Adaptive models work?
Hi Viswa,
I'm sure there are PDN articles and Academy courses that help understand Adaptive Decisioning better. Look for Decisioning, Customer Decision Hub or Decisioning and Marketing.
But let me try to briefly answer your questions here as well.
The most common practice is to have a single model per proposition and feed customer related data as predictors (usage, demographics, digital activity and so on and so forth). This is, by default, accomplished by defining the propositions in the standard issue/group/name (etc) proposition hierarchy, which is the same set of properties that are (again, by default) defining the context of the adaptive models (i.e. for every unique combination of these values, a separate model will be created). When doing this, adding proposition attributes as predictors will of course not have any effect.
It certainly is possible to use a paradigm where you partition the models by proposition attributes, or use proposition attributes as predictors, but in that case you also step away from the one-proposition-one-model paradigm. I would need to understand your particular use case in order to be able to say something sensible about that.
The propensity is only a function of the predictors. The function can (obviously) be a different for different models.
Hope this helps
Otto