Celect leverages predictive analytics and machine learning to optimize retail inventories throughout the merchandise planning and allocation process. With Celect, you benefit from true demand prediction — improving the performance of product assortments, merchandise buys, store allocations, and online order fulfillment decisions.
To improve the effectiveness of these decisions, retailers like you are turning to Celect for prescriptive plan, buy, allocation, and fulfillment recommendations. This insight will help you make better, more profitable merchandising decisions, resulting in higher margins and incremental profits.
The Celect Inventory Optimization Suite provides analytics and recommendations, giving you the ability to filter, analyze and make sense of an overwhelming amount of data from disparate sources. The result is an accurate model of future buying patterns and behavior, helping you make better decisions while leveraging your intuition.
Meet financial targets and customer expectations with intelligent insights into localized customer demand to help optimize assortment plans and merchandise buys. Leverage these insights drive multi-million dollar improvements in revenues and margins.
Optimize the allocation of purchased styles to stores based on localized demand. Allocate purchase orders to stores based on dynamic factors such as existing store inventory, style/color requirements, and presentation requirements.
Intelligently leverage store inventories to fulfill online orders with predictive analytics and real-time optimization capabilities. Maximize store throughput while reducing pick declines, decrease split shipments and delays, and turn store inventories.
The Celect Predictive Analytics Platform, powered by the Celect Engine, consolidates and normalizes inventory, transaction, product catalog, and browse data from disparate organizational silos into one predictive view, providing deep insight into how your retail customers choose.
With data from both stores and digital channels, Celect looks at what each customer bought and [more importantly] what they didn’t buy – forming a choice model to reveal a clear picture of demand for more informed decisions throughout the merchandise, planning, and allocation process.