Leverage Machine Learning for Product Sequencing to Drive eCommerce Revenue

Read how GSPANN helped a US-based retailer, with 800+ stores and $25B annual sales, to implement data-driven rules for intelligent product sequencing using machine learning algorithms that learned from 1.5+ million transaction data to get better basket conversions and higher revenue.

Key highlights:

• The model is trained every week and is used to predict the next day’s product views and sales dollars. 
• Adhoc model re-training is possible for special cases like seasonal or holiday sales.
• Women Bags category recorded a 400% increase in sales as compared to old methods.

This case study can help you understand how machine learning models can be used to intelligently display products to specific customers based on their historical data and preferred product prices rather than relying on human judgment.