Retail & E-Commerce
Seasonal complexity, multi-region noise, and cookieless attribution break traditional regression. Hierarchical Bayesian models don't.
Retail combines high-volume transaction data, multi-channel marketing, seasonal complexity, and geographic scale. Traditional regression gives point estimates that collapse under noise. Hierarchical Bayesian models return full uncertainty distributions that actually support decisions.
Our competition isn't machine learning. It's intuitive-based decision making and Excel spreadsheets.
Work in This Space
HelloFresh
Bayesian MMM + A/B Testing
Hierarchical Bayesian MMM with time-varying CAC via Gaussian Process across multiple European and global markets. Vectorized A/B testing achieved 60x speedup.
Wegmans
Bayesian Store Site Selection
Bayesian spatial model for new store site selection and trade area analysis. Integrated Nielsen/census data with 13–14% MAPE on store sales prediction.
Swarovski
MMM with Time-Varying Intercept
Bayesian MMM using PyMC-Marketing with HSGP time-varying intercept and semi-additive parameterization. MAE reduced by 20%.
Trusted By
- HelloFresh
- Wegmans
- L.L. Bean
- Fabletics
- Swarovski
- Lidl
- MercadoLibre
- Deliveroo
- Westwing
What We Solve
Media Mix Modeling
Production-grade Bayesian MMM across TV, social, search, digital, catalog, and affiliate channels. Multi-market, multi-brand support with hierarchical models across 50+ DMAs.
Store Site Selection
Bayesian spatial models incorporating census data, demographics, and trade area analysis to predict sales lift from new openings and quantify cannibalization on existing stores.
Customer Lifetime Value
Probabilistic cohort-aware CLV estimates with uncertainty quantification. BG/NBD and related models supporting decisions on acquisition, retention, and churn investment.
Demand Forecasting
Hierarchical Bayesian demand forecasting that propagates uncertainty through to inventory and replenishment decisions. Suitable for complex seasonal and promotional patterns.
Why Bayesian
Why Bayesian for Retail & E-Commerce?
- Hierarchical multi-region models — Work across 50+ geographic regions (DMAs, store clusters, countries) with proper uncertainty quantification.
- Cookieless attribution — Bayes cross-links different data sets as third-party cookies disappear. Privacy-first by design.
- Defensible investment decisions — Full posterior distributions for CMOs, not just model outputs for data scientists. Every recommendation carries its uncertainty.
“The Bayesian approach gave us defensible site selection predictions with honest uncertainty bounds — something our previous vendor couldn’t deliver.”