Machine Learning-Powered Product Categorization To Increase Conversion Rates

Similar to any other online shop, product categorization is a crucial feature needed to provide text-based and text-free searches, suggestions, and up-selling.

Although the client could categorise items on a retailer’s website, it was having unsolvable problems across several websites.

The accuracy of the listings on the client’s website was improved by Indium Software’s “name matching” model, which overcame these challenges by utilising its experience in advanced machine learning and artificial intelligence.

The client owns an AI-powered e-commerce aggregator that delights customers by giving them intelligent purchasing options.

• The product categorization will always works well on retailer websites, but on the other hand its an issue for e-commerce aggregators as the product categories are defined differently by different retailers for the same product. This creates a problem in assigning the same product from different retailers to the same categories, which leads to a decline in the quality of search results and user experience.
• There was a need for advanced machine learning and artificial intelligence techniques to be deployed to solve some of the most complex problems the industry faces. In particular, this case focuses on how we addressed the persistent issue of product categorization.