Survival analysis of LTV (lifetime value) is a good place to start. It's pretty basic, but it gets the job done. But there is a lot of business intelligence work that you could do with what you have. If you have response rates to advertisements and such it could also provide you with a good way to look at effectiveness.
I agree with rolando2, the good the bad and the ugly - being mathematically defined, is challenging. Especially with no behavioural or secondary element in your data other than purchases, even something as simple as postal code could add fantastic information to your data for understanding things like locus of purchase (it it's a store). I guess you could segment by LTV percentiles... 30%, 50%, 80% (following the 80/20 business rule...).
In terms of software, I have no idea how to do this in Excel or STATA. But, for R there's a mixed intro and example of survival analysis using the
survival package here: http://www.stats.uwo.ca/faculty/jones/survival_talk.pdf from Bruce Jones at the University of Western Ontario. I'm Canadian, sue me.
In his example, Death, would be something like your average time between purchases identified in the data as 0 or 1 if the observation did purchase in the last average time between purchases. Some people like to set this up as Purchased in Last 3 Months... but obviously it's different for every type of business. You wouldn't by a car every month, would you? So that's a judgement call on your end.
Otherwise, there's a lot of interesting things that you can do with your data from a business intelligence perspective. Average purchase price, number of items purchased based on stack outs in a store, or banners on a website if you know the time that the ad or stack out was placed.... those are just a few examples.