Best tool for analyzing inventory that has binary sales outcome of yes or no
Logistic regression would be an ideal candidate for assessing the probability of a sale, but it would be wise to take Emre's comments on 'how many' into account.  In this case even a basic aggregation of your top 50000 with a 'yes' dependent variable would get you there-- but why stop there?

You may want to isolate whether or not seasonal (or other cyclical) factors are influencing purchasing behavior (e.g. if you were attempting to optimize inventory levels for the summer purchasing habits).  In this case, linear regression with dummy variables accounting for the season or month may be a good way to delve deeper (this touches on the 'classification' point made by Emre).  The more historical data you have here, the better.

Back to your point on 'fast'-- you mention Excel so I am assuming Python or R are being ruled out. 

RapidMiner or Knime would fit your requirement for 'fast' in any of these modeling contexts.  Otherwise, if you're curious, scikit-learn (python) has about 3 dozen other general linear models freely available (R has a vast library as well).

Attached is a link on how to do this kind of modeling within RapidMiner--

Logistic Regression Example (RapidMiner)

https://www.youtube.com/watch?v=0npgGPK0zR8

Happy modeling!