Is binary logistic regression the right choice? Apologies for the rudimentary question. I'm taking on a project at work that's a bit out of my wheelhouse and I want to bounce my ideas off of those more experienced than myself.
We use Salesforce.com at the software company where I work, and I want to identify which lead behaviors (whitepaper downloads, demo views, webinar attendances, etc.) are predictive of those leads turning into qualified sales opportunities.  The idea is that we can use this data to create a model, on which we'll base a scoring model going forward.  I've identified binary logistic regression, using stepwise selection, as the best choice, based on my research.
Essentially, my thinking is that the dependent variable (opportunity status) is binary (Opportunity = 0, Not an Opportunity = 1), which would indicate that logistic regression would be the best approach.  Also, I'm not sure which behaviors and data points will ultimately be predictive of the lead becoming an opportunity, so stepwise selection seems like a good approach.  
Can anyone think of a more appropriate analysis technique, or am I on the right track?
 A: If the outcome variable $Y$ is truly all-or-nothing, like falling off a cliff, then binary logistic model is likely to be appropriate.  But stepwise variable selection is an invalid method.
A: First, I agree with earlier answers and comments about stepwise. 
Second, I am not so sure that binary logistic is the best choice - it may be, it may not. Is "qualified sales opportunity" really a yes/no variable? Might some sales be larger than others? Might some opportunities fail? Perhaps others become long term? All these would argue against binary logistic regression. 
Classification trees are another method you might consider, especially if your N is reasonably large. In R I like the party package but other tools are also good. There are also elaborations on trees such as bagging and boosting that may work well since your goal is prediction. 
A: I would say that logistic regression can be used.
But you could also think other alternatives:
-ordinal logistic regression where there are different opportunities which have rank ordering between them (0=not good, 1=might be a good, 2=good with almost certainly, 3=expectionally good opportunity ect)
-Just give these opportunities customer specific value metric and use some regression method with non-binary dependent variable
