I'm working on developing a model to predict total sales of a product. I have about a year and a half of bookings data, so I could do a standard time series analysis. However, I also have a lot of data about each 'opportunity' (potential sale) that was either closed or lost. 'Opportunities' are progressed along stages of a pipeline until they are closed or lost; they also have associated data about the prospective buyer, sales person, interaction history, industry, estimated size of bookings, etc.
My goal is ultimately to predict total bookings, but I want to account for all of this information about the current 'opportunities' which are the true 'root cause' of bookings.
One idea I have is to use two different models serially as follows:
Use historical 'opportunities' to build a model that predicts the bookings arising from an individual 'opportunity' (I'd probably use random forests or even plain old linear regression for this step).
Use the model from 1 to predict the estimated bookings of all 'opportunities' currently in the pipeline, then sum those estimates based on the month each 'opportunity' was created.
Use a time series model (possibly ARIMA?), using the 1.5 years of monthly historical time series data AND the predicted (using the model from 1) total bookings for all 'opportunities' created in that month.
Granted there would be a lag in those opportunities converting to actual bookings, but the time series model should be able to deal with the lag.
How does this sound? I've done a lot of reading on time series and predicting sales, and from what I can tell this is a somewhat unique approach. Therefore I'd really appreciate any feedback!