GSPANN used machine learning techniques and automated the process while considering the historical product sales pattern. We replaced the rule-based system of product ranking and placement with automated Python scripts to deploy a more accurate and dynamic product ranking system.
GSPANN analyzed that the existing product recommendation system was rule-driven, manual, and suffers from various limitations. We provided a proof of concept for the ML-based trained model, which was automated, dynamic, scalable, flexible, and more accurate in ranking the products to achieve higher sales.
The ML-based hybrid system approach was a perfect fit for the new products (with no historical sales pattern) and old products (with available sales pattern) ranking on the e-commerce website.
GSPANN developed an ML trained model to display the product’s listing based on the historical sales pattern. We used Python for programming the ML model, which extracts the historical data from multiple databases and then processes the information to rank the products based on the probability of getting higher sales.