Pricing for manufacturers and retail: data, experiments, AI

Course overview

Description generated based on course syllabus and open data.

Adopt a structured pricing approach: from setting launch prices for new products and forecasting demand to validating promotions via A/B tests and estimating ROI using elasticity and AI.

Who this pricing for manufacturers and retail suits — and who it does not

Suitable for

• Pricing and category managers in manufacturing and retail.
• Heads of marketing and sales, commercial directors.
• Business owners implementing price policies and promo processes.
• Analysts and data/AI specialists supporting pricing decisions.

Not suitable for

• Those expecting universal quick fixes without data and experiments.
• Those seeking programming training instead of pricing methods.
• Teams without access to basic sales, stock and price data.

Problem → outcome in pricing for manufacturers and retail

• New product without sales history → launch price via analogs, price sensitivity surveys, and A/B validation.
• Demand uncertainty → elasticity estimation, simple linear regression, scenario forecasting.
• Inefficient discounts → promo design, incremental sales measurement and ROI.
• Large assortments → price differentiation rules and sensitivity-based segmentation.
• Market shifts → price update framework accounting for competition and inventory.

Comparison with alternatives in pricing for manufacturers and retail

• Intuition-driven decisions: fast but unstable. Course approach: data, elasticity, tests.
• Black-box repricers: convenient yet opaque. Course approach: transparent, explainable methods.
• Competitor reports: useful but retrospective. Course approach: real-time experiments and promo KPIs.
• One-price-for-all SKUs: simple but suboptimal. Course approach: segmentation, thresholds, price psychology.

Learning outcomes: pricing for manufacturers and retail

• New product price setting with validation through surveys and A/B tests.
• Promo planning and ROI, incrementality and cannibalization assessment.
• Demand forecasting using elasticity and linear regression.
• Pricing rules for different markets and categories.
• Using AI for price recommendations and monitoring.

Lecturer and pricing background

Volodymyr Kuchkanov — Senior Product Manager Pricing Platform at Zalando; 11 years of experience at Anheuser-Busch InBev, Mars, Foxtrot, Intertop, Wiggle; crisis pricing strategy for Mars Ukraine (2014); analytics tools for 50 countries; early AI elasticity algorithms at Competera; analytics systems launched at AB InBev.

Program and pricing tools

• Pricing strategy → tactical rules.
• Optimal prices: tests, surveys, elasticity.
• Linear regression for demand coefficients.
• AI in pricing, price psychology, thresholds and charm pricing.
• Promo ROI, P&L, Cash Flow and Balance Sheet analysis.