Demand and pricing
Demand planning and pricing, under real uncertainty
Forecasting, markdown and price decisions built as one system. Probabilistic where it matters, constrained by how the business actually operates, and deployed on the cadence planning already runs on.
Probabilistic forecast, one SKU
History, then the forecast as a fan. Inventory is planned against the fan, not the line in the middle: the width is the stockout risk on one side and the overstock on the other.
What this usually looks like
- The forecast is a single number, and nobody plans inventory against a single number.
- Markdown runs on rules written years ago that nobody wants to be the one to change.
- Price elasticity was estimated once, on data that could not identify it.
- Planners override the model every cycle, and no one has measured whether they are right.
What you get
Forecasts with uncertainty attached
Hierarchical where the data is sparse, probabilistic where the decision is asymmetric. Sized to the SKU and horizon the business actually plans on.
Decisions, not predictions
Markdown depth and timing, buy quantities, price moves. The output is what to do, with the forecast underneath it.
Constraints modelled honestly
Lifecycle limits, inventory positions, monotonic price response, contracted minimums. A recommendation nobody can execute is not a recommendation.
The handover
Backtests the team can rerun, drift controls, and documentation of every assumption that matters.
Selected work
Forecasting
Travel retail forecasting system
Multi-SKU demand forecasting pipeline for 30+ products across 50 duty-free stores with automated monthly updates.
Stockouts ↓34%, inventory costs ↓18%; forecast error 38% → 24%
Pricing Strategy
Vitamins product pricing optimization
Data-driven pricing strategy for new vitamin product launch using Bayesian demand modeling and mixed-integer programming to optimize portfolio-wide pricing.
Revenue ↑18%, portfolio margin ↑12% on launch pricing
Pricing Strategy
B2B SaaS pricing optimization
Willingness-to-pay analysis and price sensitivity modeling for a B2B SaaS product, resulting in a new tiered pricing structure.
Revenue per customer ↑31%, enterprise conversion ↑45%
Depth
Mar 25, 2025
The predictive demand model: Bridging historical data and markdown optimization
How predictive demand models serve as the critical bridge between historical sales data and prescriptive markdown optimization, providing both demand estimates and uncertainty quantification
May 15, 2025
From lifecycle limits to markdown rules: The product constraints behind retail profit optimization
Why retailers can't escape markdown pricing—a deep dive into the lifecycle, inventory, and pricing constraints that shape modern retail optimization strategies
Jan 23, 2023
Optimizing Vitamin Product Pricing Through Discrete Choice Modeling
Using Bayesian demand modeling and mixed-integer programming to set optimal prices for a new product line — balancing revenue, cannibalization, and market share
Aug 15, 2026
Building a Customer-Brand Affinity Engine for Physical Retail
A technical architecture for customer-brand affinity in physical retail: customer windows, lift and PMI, Item2Vec embeddings, graph construction, constrained layout optimization, and causal validation.
Aug 15, 2026
Your Store Layout Is a Recommendation System
Department stores already have the data to rethink brand adjacency around real customer behaviour. The opportunity is not another dashboard, but a new merchandising decision system.
Jul 25, 2026
Your data lake is not the prerequisite
Why data science and AI platform projects fail on definitions, not on infrastructure, and how to let a real decision pull the data model into existence instead of waiting for the lake.
How it starts
Diagnostic first, build second.
01 · Decision System Diagnostic · From €12,000
Three months, one critical decision. We establish which decision the forecast serves, measure the baseline, test what the data can support, and design the operating cadence. You get evidence, a target architecture, and a costed roadmap.
02 · Design and build
Models, backtests, the decision layer on top, and the pipeline that runs it. Scoped from the roadmap, priced on the engagement.
03 · Operate and hand over
Monitoring, drift controls, documentation, and the training to run it without me.