Pricing At Scale
Protect Margin & Clear Inventory Across 50,000+ SKUs
See how AI helps high-SKU retailers shift from reactive pricing to continuous margin optimization at scale.
Live Webinar | 30th Sep 03:00 PM CEST
OVERVIEW
How leading retailers use AI to optimize pricing decisions across 50,000+ products
Managing pricing across thousands of products has become one of the biggest commercial challenges in retail. Pricing teams must balance margin targets, inventory turnover, competitive pressure, and customer demand, all while making decisions across massive product catalogs.
For high-SKU retailers, traditional pricing rules, spreadsheets, and manual analysis simply do not scale.
Join this webinar to discover how AI-powered margin optimization helps retailers make better pricing decisions at scale by combining demand prediction, price elasticity modeling, inventory intelligence, and automated recommendations into a single platform. Based on business policies and guardrails, the system identifies the optimal pricing action for every SKU to maximize profitability while accelerating inventory movement.
What You'll Learn
Why pricing becomes exponentially harder as assortments grow
Discover why managing up to 50,000+ products creates challenges that traditional pricing approaches cannot solve, leading to margin leakage, inconsistent decisions, and excess inventory.
How AI determines the optimal pricing action for every SKU
Learn how modern retailers use machine learning and scenario simulation to predict demand response, measure price elasticity, and identify the most profitable price move for each product.
How to balance margin protection and inventory clearance
See how AI-driven pricing strategies can support different business objectives, from maximizing margin on fresh inventory to accelerating sell-through of aging stock while maintaining pricing governance.
How retailers maintain control through policies and guardrails
Understand how pricing teams can define margin floors, discount limits, category-specific rules, and approval workflows while allowing AI to automate recommendation generation.
How to measure real business impact
Explore how leading organizations use experimentation and A/B testing to validate pricing decisions and quantify revenue, margin, and inventory improvements before scaling across their entire catalog.
Who Should Attend?
Webinar Agenda
The High-SKU Retail Challenge
- Why pricing complexity increases exponentially with catalog size
- Margin leakage versus inventory clearance
- The limitations of manual pricing processes
How AI-Powered Margin Optimization Works
- Price elasticity and demand prediction
- Scenario simulation and recommendation logic
- Balancing profit, volume, and inventory objectives
Live Demo: AI Pricing in Action
- Margin optimization across core assortments
- Overstock and inventory clearance recommendations
- Pricing campaign setup and governance controls
- A/B testing and performance measurement
- Enterprise-scale pricing operations
Business Impact & Implementation Approach
- Expected operational and financial benefits
- Pilot-to-scale deployment strategy
- Data requirements and integration considerations
Live Q&A
- Ask your questions
KEY TAKEAWAYS
After this session, you will understand how to:
Improve pricing decisions across 50,000+ SKUs
Protect margins without relying on blanket discounting
Reduce excess inventory and trapped working capital
Scale commercial decision-making with AI
Establish governance and measurable business outcomes
Our Speakers
Magda Okrzeja
Engineering background with hands-on experience designing and delivering AI platforms, orchestration layers, and scalable AI delivery models for enterprise clients. Proven track record translating complex business needs into production-ready AI solutions, focused on architecture, integration, and value realization. Experienced in leading cross-functional initiatives that bridge data engineering, machine learning, and business strategy to drive impact.
Bartosz Chojnacki
Leader of the AI Practice at DS Stream, where he helps retail and FMCG organizations build data platforms powered by AI agents. He specializes in translating complex data challenges into practical, production-ready solutions.
Bartosz is also a certified AI trainer and course creator, and is currently advancing his leadership expertise through the Stanford LEAD Program at Stanford Graduate School of Business.
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