Recommendation Engine

At the location level, segmenting a store allows distributors and marketers to plan their merchandising, their assortment of products, and their marketing efforts for greater efficiency and effectiveness.
Brands and retailers need to understand how each of their stores will perform, based on different factors. For instance, the site’s location (high street, residential, retail park), nearby proximity drivers (transport hubs, attractions, transient work force), local competitors and the consumer profile within catchment. Optimizing the entire supply chain network can produce impressive results when paired with precise insight from data analytics.

Accurately forecasting future demand at a very granular level – per SKU, per day, per location. Our Artificial Intelligence (AI) solution delivers probabilistic forecasts for automating daily replenishment decisions across products and stores based on hundreds of different factors including weather, promotions, festival seasons, sales patterns in nearby stores, etc. Our solution also enables inventory optimization based on a retailer’s stock management policies.

Quadratyx’s self-learning recommendation engine works in real-time, detecting product and customer behavior updates as they happen and updating recommendations accordingly, ensuring a smooth, up-to-date and relevant user experience.