User 2568 | 4/10/2016, 9:08:29 AM
I've a large data set (about 350m rows) that contain customer ID and the purchase date, amount, product, category etc. I want to use GraphLab to:
I'm looking for tutorials, examples, code snippets and/or ideas on how this is done efficiently in GraphLab. I'm particularly interested in how to efficiently convert the data from a time series of purchases to a form that can be input to a ML algorithm.
My thought is, like Churn Prediction, you use some period (i.e., quarter or year) and aggregate the purchases by ID by period, i.e., year 1, year 2, year 3. You then calculate the CLV for only the customers that have churned (i.e., they did not make a purchase in period n). Providing you have enough historical data can estimates CLV for even the long lived customers, I thought you would then use this to:
I imagine Customer Lifetime Value this has some commonality with Churn Prediction and a toolkit/tutorial on CLV, or an extension to Churn Prediction, would be great. However, in the short term, I'm happy to build my own based on examples etc, then share with Dato and the community.