Can we predict the dates where each customers is to make transaction(s)?

Question

I came across a project where we have variables in a data set such as customer ids, dates they purchased the products, type of products they purchased, and product price. I wanted to predict at what date the customer is likely to make a transaction and what product they are likely to purchase. Dates could be in days, weeks, or months.

From my understanding, I think I'll have to split the problem into different models. 1st model predicting the product(s) that EACH customer will purchase. 2nd model predicting the date of the transaction that is likely to occur for EACH customer. Obviously for the first model, we should be using classification machine learning models. I am not sure which model should I be using for the 2nd model. It could be time series, but I have not predicted the dates for a model yet. I hope I am the right track.

Main questions are:

1. Can we predict the dates from any machine learning techniques in terms of days, weeks, or months?
2. Can we predict the dates and products that each customer is going to purchase? or do we need to split the problem and perform separate models for it?

Suggestions will be very much appreciated!

Answer 1

Check out the BTYD package:

http://cran.r-project.org/web/packages/BTYD/vignettes/BTYD-walkthrough.pdf

It uses Bayesian models to model customer purchase behaviour - both on the individual customer level and in aggregate. It certainly can solve your problem of "when" customers will buy. Regarding the problem "which products" - I suspect that you could separately model the purchasing process for particular product (or set of products).