Radical Technologies provides a causal machine‑learning decision layer that predicts demand, conversion, price elasticity, and revenue at the SKU level using a retailer’s own data. The platform simulates marketplace‑wide profit impacts of price changes and integrates inventory, assortment, discovery, promotions, and ad spend into a single optimization engine, enabling e‑commerce retailers to make data‑driven pricing and inventory decisions.
Funding
Funding not disclosed
Founders
Product
Problem
Retailers often rely on aggregate or correlation‑based forecasts that miss SKU‑level demand nuances and fail to account for how price changes affect the broader catalog, leading to suboptimal pricing, inventory mismatches, and lost margin.
Solution
Radical Technologies offers a causal machine‑learning decision layer that predicts demand, conversion, price elasticity, and revenue at the individual SKU level using the retailer’s own data. By estimating true price elasticity and cross‑elasticity across the entire catalog, the platform enables simulation of marketplace‑wide profit impacts before any price is changed. The same layer integrates inventory levels, sales velocity, perishability, and supply‑chain costs, allowing coordinated optimization of pricing, inventory, assortment, discovery, promotions, and advertising. Retailers can apply automated optimization or manually adjust decisions with confidence that the underlying models reflect causal relationships rather than mere correlations.
Target Audience
Primary customers are e‑commerce retailers and brands that manage large, dynamic SKUs and need data‑driven pricing and inventory decisions across their online sales channels.
Features
- Causal ML models that deliver SKU‑level demand, conversion, price elasticity, and revenue forecasts
- Cross‑elasticity analysis to predict how a price change for one item influences the entire catalog
- Integrated optimization of pricing, inventory, assortment, discovery, promotions, and ad spend within a single decision engine
- Inventory‑aware pricing that adjusts prices based on stock levels, perishability, and supply‑chain costs to prevent stock‑outs and unnecessary markdowns
- Simulation tools that let users model marketplace‑wide profit outcomes before implementing price changes
- API‑driven platform that can be embedded into existing e‑commerce stacks for real‑time decision support