Pricing and revenue
Pricing, elasticity and revenue optimization
Price, markdown and promotion decisions as one system: elasticities the data can actually identify, constraints the business actually has, and an optimizer that recommends a price list, not a chart.
What this usually looks like
- The elasticity comes from one regression on last year, and it says the same thing for every product.
- Markdown depth is decided by a rule written before the current buyer joined.
- Promotions are measured on sales during the promotion, and nobody subtracts what would have sold anyway.
- Finance wants margin, merchandising wants sell-through, and the price list is where they fight.
What you get
Elasticities you can defend
Estimated per product family, pooled where the data is thin, with the identification problem addressed rather than assumed away.
A price and markdown optimizer
Mixed-integer or stochastic where the constraints demand it: price ladders, monotonic response, inventory positions, contractual floors. The output is a price list with the expected margin next to it.
Willingness to pay for what has no history
Conjoint and discrete-choice studies for launches and new tiers, feeding the same optimizer.
The handover
Backtests by season, every assumption written down, and the training to rerun it without me.
Selected work
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
Apr 18, 2025
Why price elasticity is the most difficult parameter to estimate in retail analytics
A deep dive into why measuring price elasticity remains the biggest challenge in demand modeling, from data scarcity and endogeneity to seasonal confounding—and how modern techniques address these challenges
May 1, 2025
Markdown Pricing Optimization: Stochastic Programming for Retail
How retailers transform predictive demand models into prescriptive pricing decisions through stochastic programming, jointly optimizing markdown prices and inventory allocation under uncertainty
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
May 3, 2024
Conjoint Analysis for Measuring Customer Willingness to Pay
A practical guide to using conjoint analysis for understanding product preferences and pricing optimization
Mar 17, 2026
For Markdown modeling, predict sell-through rate and not sales volume
Sales volume mixes demand and inventory effects. Sell-through rate captures what actually matters: how fast inventory clears under pricing and context constraints.
How it starts
Diagnostic first, build second.
01 · Decision System Diagnostic · From €12,000
Three months, one critical decision. We establish which price decision matters most, measure what elasticity the data can support, and design the operating cadence. You get evidence, a target architecture, and a costed roadmap.
02 · Design and build
Elasticity models, the optimizer, the constraints, and the pipeline that runs them on the pricing calendar. Scoped from the roadmap, priced on the engagement.
03 · Operate and hand over
Monitoring, seasonal backtests, documentation, and the training to run it without me.