What kind of a distribution is the total spend of a potential customer? I'm trying to figure out how to analyse the data which consists of a number of visits to a website and the total amount the visitor ends up spending there.
There are obviously a lot of zeros - people leaving without buying anything - and few relatively large amounts, so the data is severely left skewed.
I'd like to transform it into some kind of normally distributed data, so I can analyse it properly, but I'm not sure how to go about doing that.
I've looked into various transformation functions, read about Weibull distributions, lognormal, chi-squared and goodness-of-fit, but for my lack of experience, it's too much information :-)
I'd like to get push in the right direction and maybe some comments from people who have worked on something similar.
Thanks!
 A: The problem you face is called "zero inflated data." Most approaches to this are through either the negative binomial or zero-inflated Poisson distributions. Whether or not you can do a transformation depends on why you have zeros in the first place. There are discussions at http://www.theanalysisfactor.com/zero-inflated-poisson-models-for-count-outcomes/ and http://www.ualberta.ca/~bhumphre/class/zeros_v1.pdf. (Although the math is scary, read at least section 3.4 Discussion.)
A: You cannot transform data with a large number of 0's to normality. 
You say you want to "analyze it properly" but you don't say what you want to find out. It seems likely that this variable is the dependent variable in some equation - you want to predict customer spending based on various traits of the customer or the site or something. In that case, the place to start is regression. OLS regression makes no assumptions about the dependent variable except that it is continuous - it makes assumptions about the error as represented by the residuals.
If the residuals are not normal (which seems likely) then there are various things to try. You could try quantile regression (this might be useful, in any case, even if the residuals are normal) or regression trees and their offshoots (forests and so on). 
