Product Recommendation System

Increase sales and maximize the value of customer interactions by generating targeted and personalized product recommendations for customers

This turnkey enterprise AI solution enables the marketing and sales teams to promote the products - at optimal prices - most likely to be purchased by customers by understanding their needs, browsing behavior and purchasing preferences from their history and real-time behaviors. This solution also considers inventory levels to generate suggestions to procurement and marketing to improve the velocity of products and reduce cost of inventory. Get in touch with us if you like to explore how this solution can work for you.

Key Metrics

Increase in revenue by


Increase in purchase value by


Retailers are increasingly utilizing recommendation systems to provide personalized shopping experiences for their customers. By analyzing data such as purchasing history, browsing behavior, and demographic information, these systems are able to make personalized product recommendations to individual customers.


Machine learning algorithms are commonly used to build and train these recommendation models. The models can learn from data such as past purchases, browsing history, and customer demographics to understand the preferences of individual customers. These models can also be updated in real-time as customers interact with the retailer's website or mobile app, providing an ongoing stream of personalized recommendations and optimizing the price point of the products at which the customer is likely to make a buying decision.


Implementing a recommendation system can help retailers to increase sales and customer loyalty by providing customers with products they are more likely to be interested in. Additionally, it can also reduce the need for expensive and general marketing campaigns, by providing more effective and targeted marketing. Retailers can also gain valuable insights into customer behavior and preferences by analyzing the data generated by the recommendation system. With the help of a recommendation system, retailers can also improve their inventory management by providing more accurate demand forecasting and reducing stockouts.


  • Collect and analyze customer data such as purchase history, browsing behavior, and demographic information
  • Build and use a recommendation model using historical customer data
  • Continuously update the model with real-time customer interactions and feedback
  • Personalize product recommendations and optimize price for individual customers
  • Monitor and analyze the performance of the recommendation system and its impact on sales and customer engagement
  • Use A/B testing to evaluate and improve the effectiveness of the recommendations
  • Incorporate data from other marketing tools and platforms to provide a seamless and personalized customer experience
  • Use data-driven insights to identify trending products and improve inventory management

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* Values are approximates arrived at based on earlier experience and/or existing literature. Contact us to find out how you can measure the ROI on this solution for your business