Predict buying behavior under the condition that a customer is advertised or not I'm trying to predict response of customers on a marketing campaign.
As of now, I have data from one Marketing campaign and rfm data of my customers.

Some proportion of customers say 60%, received adverts. Roughly 10% responded

From the response data, i.e. whether purchased or not, on this marketing campaign I built a random forest using scikit-learn. 

The model performs on a hold out set really well. but the most influential variable is the boolean: CustomerHasBeenAdvertised

I want to use this model, to select customers for a future marketing campaign.
To obtain the "purchasing probabilities" of the customers under the condition of similar advertisement, I set the variable CustomerHasBeenAdvertised to 1.

However, on a data set with this side condition, all the forecasts are above 0.5.

Is this extra ordinary high value due to the variable importance? Or are there other explanations?

Is setting the variable CustomerHasBeenAdvertised to 1 the false approach?

If so, how could one handle the case: Customer bought without beeing  advertised?

Should one simply neglect the information whether advertisement took place or not?

Thanks in advance
It depends what you want to do. If you want a model that will predict who to target/not-target for a marketing campaign, then you want to train a model using only the people who were marketed to, which defines your two classes "responders" and "nonresponders". Given an unseen customer, your classifier will then determine whether they are likely to respond if you send them a directed marketing advertisement. Take a look at uplift marketing.