What is a good approach for a lifespan?
I think you need to come up with a way to treat the data such that you're thinking in days, not hours, right? The peaks look they're just at 24, 48, 72, 96, (1 day, 2 days, 3 days, 4 days) etc, and are pretty much normally distributed around those peaks.

I think a good test might be to try a categorical approach to start, to see how well you can predict which 'peak' the ad belongs to (is this an ad in the 24-hour normal distribution? the 48-hour?). If you can figure out which peak that ad belongs to then you might be able to identify features that indicate whether it's more likely to be on the short- or long-side of the hump. If you have bad results putting the ads into categories that might tell you something too.

If you do try this, be careful of how you measure performance as your dataset will be unbalanced by the higher occurrence rate of quickly-pulled ads.