I have a dataset with some zeros - based on how I segment my data, it is either 50% of the observations or 80% of the observations. The data is not actually count data, but from what i have read, that doesn't really matter for a poisson regression.
The data can be interpreted as a "signal" or "offer" sent to a person and whether the person made some purchase. The output variable is a USD amount if person makes a purchase and 0 otherwise. therefore the data looks something like:
<columns describing the offer>, <columns describing the person>, <money that the customer payed (0 when no purchase is made>
Now i would like to model the amount of money payed as a function of person characteristics and offer characteristics. For that i am using a poisson QML model (with some lasso shrinkage on top, as i have many variables and also to make it more fancy).
Since i have in the dataset plenty of zeros - what will be the impact of those on my estimates from the poisson regression? Will my estimates be completely useless? Do zeros just make my estimated coefficient lower than what is true value?
In the end of the day, I want to find how characteristics of the sent offer impact the expected payment of the person of given characteristics. I.e if i send a specific offer to 100 people, 20 of them pay 1 dollars and 80 do not make a purchase i want to know that the expected payment of person in this group is 0.2 dollars.