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Attain Higher Sales via Machine Learning-based Recommendation System

Read how GSPANN helped a US-based premier retailer, having over 700 departmental stores and 150 specialty stores, in replacing their existing manual, rule-based product recommendation technique with an automated machine learning-based recommendation system to achieve higher sales.

What can you learn from this case study?

  • Accurate product recommendation process: The manual, rule-based process for product recommendation was replaced with Machine Learning-based trained model to make the recommendation system automated, based on the historical sales pattern.
  • Automated product ranking system: Simplified the product ranking by automating the process of extracting historical data from multiple databases.
  • Achieve higher sales: The Machine Learning-based trained model was dynamic and 18% more accurate as compared to a rule-based approach in predicting product sequence, which resulted in higher sales.

Through this paper, you can understand how GSPANN enabled the client to get a predictive analysis of the historical sales pattern of their e-commerce website with the help of a recommendation system.

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