Sourcing (discounted) products customers want Goal: Generate a list of 100 products per vertical (e.g. fashion, electronics) that the teams should source, discount, and list on the website over a specific period. You may assume all customers are online only. 

My thinking so far:


Predict the customers that will come to the website during the
specific period (time series). Only 30-40% customers return YOY. 
Understand what they want (use search data, add to basket but didn't checkout etc). Potentially segment it further by looking at those customers who generate the highest revenue in general vs one off purchasers.
Further filter those products that these customers add (or take from 'viewed or saved' state to the 'checkout' state) once the product is on deal.
Potentially use clustering to recommend products similar to those from step 3 but that have never been on a deal? 


I cannot influence the amount of discount. I can only influence what products we source. Therefore I want to source the ones with the highest potential of purchasers from the customer.

Any thoughts on the approach above?
Clustering is not well suited for prediction.

Use a recommender system instead.