So, I've uploaded the data for anyone who wants to take a look.

My R code:

The distribution of the suspect campaign channels is not strange. However, you need a *lot* more visitors to get the same effect. That tells me some of the traffic may be polluted with bots. Note that the most important and expensive one, *adv_banner* is only active for a few weeks per year, hence the many zeroes.

I'm using the square-root link function because it gives a much better fit. I understand that the curve is less steeper (which fits the data), but I'm not sure if I know what it does conceptually.

Summary:

The fit is not great, but it works.

So the *adv* coefficients are really low. For the banner especially, the standard error is also relatively low. The upper limit of its confidence interval is 0.0188 (95%) or 0.0225 (99%). That seems low compared to *referral*, which is probably the best direct comparison. It seems suspiscious, taken together with some other information that's not in this data.

Is it a problem that my predictors are somewhat correlated (mean 0.33, range 0 to 0.80)?

I also realized I have the number of people exiting the ticket page, instead of just entering the page. When people exit, they either exit altogether or they go to the page that takes care the transaction. The number of exits correlates somewhat better with the number of transactions. But I'm not sure how to integrate it in the model.