Retail & E-commerce

We help retailers estimate true price elasticity by customer segment, accounting for promotional interactions and competitive responses. This enables dynamic pricing that maximizes revenue without triggering customer perception problems. Promotion effectiveness analysis separates incremental sales from cannibalization across channels, preventing profitable product loss to discounting. Personalization models identify which customers respond to which product recommendations with genuine incrementality, not just predicted preference. Inventory optimization leverages demand forecasts grounded in causal relationships between promotions, seasonality, and unit sales. Our methodology is detailed in the [Causal Machine Learning textbook](/research#causalml-book).

Retailers using our causal analytics platform experience measurable revenue uplift, gross margin expansion, and reduction in excess inventory. E-commerce companies improve conversion rates while maintaining pricing integrity. Subscription and membership models extend lifetime value prediction by identifying causal drivers of retention and expansion revenue. Multi-channel retailers accurately attribute sales to online and offline touchpoints, informing both inventory allocation and marketing spend.

The platform integrates with POS systems, e-commerce platforms, and ERP systems to operate on real transaction data at scale.