Model price elasticity by product segment using historical transaction and promotional data.
Marketing & Sales
Dynamic and Algorithmic Pricing for E-Commerce Retailers
Build and govern algorithmic pricing engines for online retail, from elasticity modelling and competitor tracking to repricing rules that protect margin and brand trust.
🗓️Duration5 training days
📚Content4 modules · 8 sessions
🎓On completionAccredited attendance certificate
About the programme
Course Overview
Online retailers now reprice thousands of stock-keeping units several times a day, yet many pricing teams still rely on static spreadsheets and gut-feel discounting that erodes margin during peak demand and leaves money on the table in quiet periods. This course equips pricing, merchandising and revenue analysts to design algorithmic pricing systems that combine price elasticity estimation, competitor price scraping, inventory position and demand forecasting into rules-based and machine-learning repricing engines. Participants work through real e-commerce pricing scenarios: setting price floors and ceilings, building markdown cadences for seasonal stock, and testing surge and demand-based pricing without damaging customer trust. Teaching combines applied statistics, hands-on rule-building exercises and case discussion of pricing governance failures, so participants leave able to launch a pilot pricing engine, defend it to commercial leadership, and monitor it for unintended discrimination or price-war spirals.
Expected Learning Outcomes
Design repricing rules that incorporate competitor price feeds, stock cover and demand signals.
Set price floors, ceilings and guardrails that protect margin during automated repricing.
Build a markdown cadence and clearance strategy for seasonal and end-of-life inventory.
Evaluate machine-learning demand forecasting models against simpler rules-based alternatives.
Test pricing changes using controlled experiments and interpret their commercial impact.
Present a pricing governance framework that flags collusion, discrimination and error risks to leadership.
Who Should Attend
Pricing analysts and revenue managers at online retailers and marketplaces.
E-commerce merchandising managers responsible for category profitability.
Category and trading managers who set promotional and clearance pricing.
Data analysts moving into commercial pricing and demand forecasting roles.
Growth and revenue operations leads building pricing tools for direct-to-consumer brands.
Commercial directors who must govern automated pricing decisions across product ranges.
Course Modules
Select any module to see its sessions and points.
01Price Elasticity and Demand Analysis for Online Retail
2 sessions · 8 points
Session 1Measuring Price Sensitivity Across Product Categories
- Calculate own-price and cross-price elasticity from historical sales and promotion data.
- Segment products by elasticity, margin contribution and stock turn to prioritise pricing effort.
- Distinguish elasticity driven by seasonality from elasticity driven by genuine price sensitivity.
- Build a demand curve for a sample category and identify its revenue-maximising price range.
Session 2Competitor Price Intelligence and Market Positioning
- Set up competitor price scraping and price-index tracking across key marketplaces and websites.
- Define a price-position strategy relative to named competitor tiers rather than blanket matching.
- Identify when competitor price matching erodes margin without shifting market share.
- Combine competitor signals with elasticity data to set an initial pricing corridor.
02Building Rules-Based and Algorithmic Repricing Engines
2 sessions · 8 points
Session 1Repricing Rule Design and Guardrails
- Translate business pricing policy into explicit repricing rules and conditional logic.
- Set price floors, ceilings and change-frequency limits that prevent runaway automated pricing.
- Incorporate stock cover, lead time and supplier cost changes into repricing triggers.
- Design an exception and override process for manual intervention during rule failures.
Session 2Machine-Learning Demand Forecasting and Dynamic Pricing
- Compare rules-based repricing with machine-learning demand forecasting on accuracy and explainability.
- Evaluate surge and time-based pricing models used for perishable stock and flash promotions.
- Assess data requirements and feature inputs needed to train a reliable demand model.
- Identify signs of model drift and plan a retraining and monitoring schedule.
03Markdown Management and Promotional Pricing Cadence
2 sessions · 8 points
Session 1Seasonal Markdown Planning and Clearance Strategy
- Build a markdown cadence that sequences discount depth against remaining sell-through targets.
- Plan clearance pricing for end-of-life and overstock inventory without disrupting full-price lines.
- Calculate the sell-through rate needed to justify each markdown step within a selling season.
- Coordinate markdown timing across channels to avoid cannibalising full-price online sales.
Session 2Promotional Pricing, Discount Depth and Margin Protection
- Model the margin impact of discount depth, duration and eligible product scope before launch.
- Design promotional pricing rules that avoid training customers to wait for permanent discounts.
- Set rules preventing stacked discounts and coupon abuse from breaching minimum margin thresholds.
- Analyse post-promotion demand to detect pull-forward effects on subsequent full-price sales.
04Experimentation, Governance and Ethical Risk in Automated Pricing
2 sessions · 8 points
Session 1Testing and Measuring Pricing Changes
- Design controlled price tests using holdout groups or geographic splits to isolate causal impact.
- Interpret test results for statistical significance and commercial relevance before rollout.
- Build a rollout plan that scales a successful pricing test across the full product range.
- Document test outcomes in a pricing decision log for future reference and audit.
Session 2Pricing Governance, Compliance and Customer Trust
- Review pricing algorithms for personalised or discriminatory pricing risk before deployment.
- Check repricing practices against consumer protection rules on reference pricing and fake discounts.
- Establish a governance committee that approves algorithm changes and reviews pricing incidents.
- Communicate pricing changes internally so customer service teams can explain them consistently.
What the participant receives
4 course modules
A structured syllabus
8 training sessions
across 5 days
32 detailed points
Applied, detailed content
Accredited attendance certificate
On completing the programme
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