Recommendation Engine

Problem Statement: Customers found it extremely tedious to browse through the entire assortment to place orders leading to customer retention issue.Our goal was to identify individual customer preferences and tailor recommendations. This increased the sales on the platform.”

Our Solution:The retailer had fallen into the trap of promoting the bestsellers on their website. This in turn creates even more sales for those products. However most of its revenue came from rest of the assortment. Which means most of its customers didn’t like the products shown to them. So right products needed to be shown to the right customer

Multifactorial approach was used to identify every customer’s preference and find the best matching product from the entire assortment.The factors considered were:

  • Product attributes (like category, subcategory, collection, type)
  • User Attributes (like location)
  • User Actions
    • Clicks
    • Orders
    • Add-to-cart
    • Wishlist
    • Search
    • Favorites
    • Filters

We delivered nearly 106% increase in the Click-Through-Rate at the landing page itself. This is because customers found products on the landing page to be more relevant to them


We are a team of machine learning programmers, data engineers and strategy analysts.We have no legacy… and that’s our biggest strength we do what we are passionate about, question the status quo and are fresh and unconventional. Our team is skilled at data sciences, algorithms and consulting. We have delivered radical solutions which have been accomplished only because of the unique blend that we bring as a group.

Scientist Technologies ®


Ground Floor, Indiqube Penta, #51 Richmond Road, Ashok Nagar, Bengaluru, Karnataka 560025

+91-991 669 51 56

Copyright © 2018