Product Recommendations in Retail: 4-Week Implementation

MAQ Software

Improve user experience by implementing a product recommendation engine

Retail industry being a highly competitive industry needs constant upgrades based on consumer demands. Preferences of customers are changing very frequently. To attract new customers and retain old customers, you need to provide customers with what they want.

We will assess your current Azure infrastructure and will help you implement a product recommendation engine based on various machine learning tools like Azure Machine Learning, Azure Databricks, and Azure Cognitive Services that can be used to train and deploy models.

This comprehensive four-week program is designed to help you understand how machine learning can be leveraged to deliver outstanding product recommendations.

Target Customers

  • Machine learning engineers
  • Data scientists
  • Business analysts
  • IT professionals


  • Initial assessment of your current data infrastructure and product recommendation needs specific to the retail industry
  • Selection of appropriate recommendation algorithms for your business
  • Data preprocessing and cleansing
  • Model training and evaluation
  • Implementation of the recommendation system
  • Performance monitoring and tuning


  • A fully functional proof of concept recommendation engine
  • A report on the performance of the model
  • Hands-on training for your team on the product recommendation model, and the latest product recommendation techniques and tools specific to the retail industry
  • Detailed documentation of the entire process
  • A performance monitoring and tuning plan


  • Improved product recommendation accuracy and efficiency
  • Reduced time and resources required for product recommendations
  • Increased efficiency and competitiveness using advanced product recommendation models
  • Improved customer satisfaction
  • Increased revenue through targeted product recommendations
  • Receive assistance with identifying potential challenges and opportunities for implementation