Ah yes, the old "did it work" question. Heard that one a lot. I would love to hear others opinion, because I have grappled with it many many times.

Now, if there had been an experiment in place, where treatments / interventions were randomized in a controlled setting, where some visitors/buyers got treatment A and others got treatment B, then this is fairly easy to analyze as independent groups and typically is done by comparing two proportions or two means (or multiple proportions or means). So, make sure you understand tests for two proportions (z tests), means (t test, nonparametric alternatives, permutation tests etc.). For multiple categories, chi squares and Anova are your most basic methods for proportions and means respectively.

Without an experiment, when "we used to do X and now we do Y", I think all statistics break down. I think you are left with some really big assumptions - like comparing two groups before and after and assuming the *independent* groups are the same in all other regards.

Maybe you have opportunity to do some sort of a paired test (which for two groups is a paired t test or Mcnemars test. These are two that you should read about and both assume the same or matched individuals have data pre and post the intervention.

Some people try and fit a time series model to the "pre period" and see if the post period deviates. I am always skeptical of this...

Finally the issue of zeros is a hard one. As a lot of your dependent variables sound like counts, I suggest you look into Poisson and Negative Binomial regression. Both are for counts and both can model treatments / interventions as indicator variables. Both allow for some zeros. For extra zeros over and above what the distribution expects, get a book on zero inflated models. There is also a zero inflated gamma model for continuous variables >=0. Often I analyze them as two processes if I need something simple- the zeros, which I would use logistic regression for (probability of being zero or not) and the positive values which would be a linear model (generalized or general).

Hope this helps, at least a little to get you looking around..you really need many methods in the arsenal and certainly need access to statistical software for all but the basic parametric tests.