For decades, and with varying degrees of success, retailers have been buying goods based on historical data and gut instinct. Often making “flat bets” across the product line and buying excessive amounts of inventories to ensure product availability.
Using this approach, retailers have no visibility into the financial impact of their buys, sell-through rates, or if the buy was too large or small based on demand — until the buy is executed and sales results arrive, which is too late.
With Celect Buy Optimization, predictive analytics and machine learning impacts your buys in a big way — making them more accurate and productive, with fewer overbuys, markdowns, and stock-outs. Through more accurate buys, you can gain precious margins through a reduction of markdowns (from overbuys) and increase revenues through fewer missed sales (from underbuys).
SOLUTION OVERVIEW (pdf)
With Celect Buy Optimization, your buys will have higher sell-through rates and more accurately reflect demand - for highly productive buys and inventory.
View optimized buy quantities, demand predictions, and rankings based on attributes and styles — for each store. Optimized buy sheets show all rows and columns from the original buy sheet, alongside Celect’s predictions for easy comparison and decision making.
Put an end to making “flat bets” across the product line — buying excessive amounts of inventories to ensure product availability. Reducing extreme over buys lowers inventory cost and the need for markdowns, while meeting target sell-through rates.
Adjust predictions based on your own defined rules and constraints. The recommended buy quantities are impacted by business constraints set in place such as min/max presentation and min-max color counts.