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:

  1. Predict the customers that will come to the website during the specific period (time series). Only 30-40% customers return YOY.
  2. 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.
  3. 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.
  4. 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?

Topic regression time-series predictive-modeling clustering machine-learning

Category Data Science

1
answered at

If you know the leaved basket products or the purchase the user did in the past, you can use Association Rule Mining to find the most probable purchase for the customers. You can select the top-n products/categories and develop the discount strategy for them. Here is good post about Basker Analysis with Assosative Rules. Unless you have very specific sales data ( like B2B with small amount of clients and low purchase volume), you should uncover some insights.

1
answered at

Clustering is not well suited for prediction.

Use a recommender system instead.

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