Get predictive analysis of historical sales pattern on your eCommerce website, based on consumer search and market trends, with the help of recommendation system.
Read how GSPANN helped a Cincinnati, 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 rule-based approach in predicting product sequence, which resulted in higher sales.