I am currently working on a project using a sales system and trying to come up with a way to use the current pipeline of potential sales to predict the amount of product that will be sold in the future. I’m looking for advice on how to approach this problem and hopefully some resources to teach me what approach to use and why.
The sales system I’m using has historical data for opportunities (potential sales). Around 50,000 of the opportunities are “closed” meaning that they are either won or lost. I have around 1,000 “open” opportunities that have not yet been won or lost. Some variables that I have on each sale include the product (which is generally homogenous except for the amount), the amount, the salesman, the date, the time it was input into the system, the customer, and other data about the customer.
I understand that if I want to predict a dichotomous variable like win / lose then I should look at a logistic regression. However, I’m looking for general advice on how to
- Predict the probability of each individual opportunity closing as won using the data I have (and how to tell if I've done it correctly).
- Estimate the total amount of won opportunities for a period.
I found a similar question here Using a logistic model on the estimates of several other classification models but I’m hoping for a response that gives me a better idea of where to start. I’m comfortable using R or any other statistical software, but ideally I'd like some kind of book or other reference material that is as low-level as possible.