Read how GSPANN helped a US-based retailer, with 800+ stores and $25B in annual sales, to implement data-driven rules for intelligent product sequencing using machine learning algorithms. The ML model was trained from 1.5+ million transaction data, can deliver better basket conversions, and generate higher revenue. Key highlights: The model is trained every week and is used to predict the next day’s product views and sales. The model automatically recalibrates itself to include recent data but can be adjusted manually for special occasions like seasonal or holiday sales. Bags category in the women's section recorded a 400% increase in sales as compared to old methods. This case study can help you understand how machine learning models can intelligently sequence products based on different behavior metrics, including previous sales, product price, inventory on hand, etc. This data-driven approach is more scientific than relying on human judgment for displaying products.