I am completely new to Knime and experimenting with machine learning.
I would like to predict buy/no buy decision. I have a following data set:
Client ID, contact date, result of contact, type of product sold.
Basically for the same client ID, I have more than 1 contact trials. Some contacts result in purchase, some not. For example, within a specific time period, I could have 10 contacts with specific client, but only 2x he has purchased anything.
I do not how to handle “contact history” to prepare an input table for a predictor/e.g. decision tree. In KNIME examples, like “churn prediction”, in input tables 1 client = 1 row. In my case: 1 client = mulitple rows (this is because each contact generates a new record/row in CRM). How to best approach it? Shall I built some supporting statistical indicators such as “average time between contacts” to transform my table to 1 client=1 row table? or somehow aggregate these rows into one. These seem highly impractical.
Thank you for any suggestions