GSPANN’s Advanced Analytics team developed and deployed algorithms based on Zeolite (our in-house ML-based solution) for dynamic product sequencing. We developed two models based on the supervised ML regression model. These models predict the next day’s ‘product views’ and ‘sales dollars’ for each product by using the historical data. To rank the products, we used the predicted views as qualifiers.
Our implemented Zeolite models uses the attributes based on consumer buying patterns. As the trends change over time, the new patterns present in the recent data get accommodated in the auto-refreshed model. The current model’s refresh frequency is seven days, that auto determines weights based on the historical data. We also enabled an adhoc model re-training for special cases like different seasons, holidays or sales, etc.
The ML model has learned from 1.5+ million transaction data. We’ve A/B tested the performance of both rule-based and new ML-based models. Our ML algorithms were applied to 53 of product categories to search and browse products.