Retail & Supply Chain
In the highly competitive retail landscape, it is vital to stay ahead of the competition by deepening customer relationships and overhauling supply chain processes. Artificial Intelligence provides a mechanism to leapfrog traditional heuristics & simple statistics by learning automatically from data and identifying hidden connections that were previously impossible to detect.
Soothsayer has helped dozens of Fortune 1000 & Midsize companies capture ROI via custom A.I. solutions that:
- Forecast Demand for Thousands of SKUs at Thousands of Locations
- Optimize Inventory/Order Fulfillment at 1,000+ Locations (Ship from Store Optimization)
- Forecast Customer Lifetime Value Multiple Years in the Future
- Identify Opportunities for Cross-Sell & Upsell
- Predict the Optimal Price for Products
- Identify When Major Customer Life Events Take Place & Send Highly Targeted Ads
Soothsayer can help you leverage A.I. to drive more accurate demand forecasting and provide deep insight into the types & characteristics of products that are most popular with your customers. This will help you decrease the impact of holding too much inventory, reduce the number of discounted items, improve your customer loyalty & understanding, and ensure the right inventory is on the right shelves at the right time.
Soothsayer can work with you to model the current topology of your network of stores, manufacturing sites, warehouses, and DCs. We can utilize advanced A.I. techniques to mathematically model your supply chain to optimize for minimum inventory at each location, while ensuring that you still satisfy SLAs.
Artificial Intelligence-driven Segmentation (aka Clustering) allows you to form a much deeper understanding of your customers, products, and processes. Traditional approaches predefine what these groups look like and fit your data to that. With A.I. methods, Soothsayer can ‘regroup’ your data in a much more accurate way – so what you thought was complex, non-linear, and inseparable becomes nice, clean, and highly defined. For example:
- If you are trying to forecast the demand for a new SKU with no shelf-history, we can analyze the attributes of existing & past products to identify the most similar examples and predict the success of introducing a new SKU. This can be used for purposes such as improved Demand Forecast accuracy and New Product Design.
- If you are looking to better understand your customers, how interactions can be more proactive & personalized, their Lifetime Value, and when they will churn, then Scientific Segmentation should be strongly considered.