The client wanted to create an eCommerce platform to help struggling vendors increase their sales and make it easier for people to place orders, especially those who aren't that tech savvy. This platform utilizes a unique chat-based system for users to describe the product they want, and the system would help them find the best vendor for it. The classification of products helped create product groups that were useful for both users and vendors.

  • Industry

    Retail & Ecommerce

Project Summary

Our client wanted an eCommerce system that would help struggling vendors sell their products and improve the user experience. For this, we needed a chat system where users could describe the product they need, and the system would find the best vendor for them. We used NLP to organize data of different vendors and evaluate certain vendor-based attributes. The context-based search helped us divide products into different categories and identify the best group. This especially helped older people who find it difficult to place an order on eCommerce websites. The vendors also benefited as they were able to get user insights quickly.

  • Predictive eCommerce was developed with the goal to give enormous benefits to the vendors struggling to sell their product.
  • With the development of this platform, the customer does not need to spend hours finding out the products. Users can just describe the product that they are looking for in chat, and the system will find the best vendor matching their needs.
  • The core objective of this development was to allow anyone to use eCommerce for placing their orders.
  • Managing and organizing the data of different vendors was a huge challenge.
  • The system is expected to evaluate the request and propose a vendor based on certain attributes.
  • Those specific attributes cannot be obtained from the vendors by data entry, and this required several data entry tools.
  • We proposed using extensive NLP with the goal to train models for doing a context-based search.
  • The context-based search was the only way to better understand the intent and nature of the product and what could be the best product to match the needs.
  • After finding the closest match, the products would be divided under different categories to find the similarity score under different criteria to identify the best group.
  • The launch of this product changed the complete processing of the business.
  • People loved to place the order by updating the product they needed and its quantity.
  • Old age people who do not know how to browse sites with so many details found it very easy to order the products.
  • Vendors who had more quantity of the product were contacted on the system.
  • Vendors could get insights into the order placement quickly.

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