Retail and E-Commerce Data Analytics Training Course

Course overview

Every transaction, browsing session, and abandoned cart leaves a trail of data, and retailers who read that trail well make better decisions about what to stock, what to charge, and whom to reach. This course helps retail and e-commerce professionals move past reporting for its own sake and toward analysis that changes outcomes. Participants examine the metrics that matter across the funnel, from first click to repeat purchase, and learn how those numbers connect to margin and retention.

The course is built around the questions managers and analysts actually face: which customers are worth acquiring, which products sell together, when to discount, and how much inventory to hold. Over five units, participants study the methods behind segmentation, recommendation, forecasting, and pricing, and see how tools such as Google Analytics 4 fit into a working measurement setup. The goal is fluency with the techniques and the judgment to know when each one applies.

Why this matters

Retail runs on thin margins and short attention spans, so small analytical gains compound quickly across thousands of orders.

The discipline has matured well beyond simple sales reports. Market basket and association-rule analysis, often expressed through support, confidence, and lift, reveal which items move together and inform both store layout and online cross-sell. Recommendation systems built on collaborative and content-based filtering shape a growing share of e-commerce revenue, while conversion rate and cart-abandonment analytics expose exactly where a checkout flow loses money. Cohort and lifetime-value analysis reframe marketing spend around long-term customer worth instead of one-off sales, which is where retention initiatives like Customer Loyalty Programs and Retention earn their keep.

On the operations side, dynamic pricing, demand forecasting, and inventory analytics decide whether a business meets demand or ties up cash in dead stock. Forecasting methods range from moving averages and exponential smoothing to regression and seasonal models, and their output feeds reorder points, safety stock, and markdown timing. Underpinning all of it is measurement hygiene: event-based tracking in Google Analytics 4, consistent attribution, and attention to privacy rules such as GDPR and consent handling. Analysts who understand both the statistics and the commercial context turn scattered numbers into decisions a merchandising or marketing team can act on.

What you will be able to do afterwards

By the end of the course, participants will be able to:

• Select and interpret funnel metrics like conversion and AOV
• Run market basket analysis using support, confidence, and lift
• Build RFM and behavioral segments and weigh lifetime value
• Assess collaborative and content-based recommendation systems
• Produce demand forecasts and link them to inventory decisions
• Reason about dynamic pricing using elasticity and margin
• Configure and interpret Google Analytics 4 reports

Course outline

Unit 1: Introduction to retail and e-commerce analytics

• Transactional, behavioral, and channel data
• KPIs: conversion, average order value, margin, CAC
• The stack: POS, orders, and Google Analytics 4
• Pitfalls in data quality and attribution

Unit 2: Customer behavior and segmentation

• Mapping the customer journey
• Behavioral, demographic, and RFM segmentation
• Cohort analysis and customer lifetime value
• k-means clustering of customer data

Unit 3: Personalization and loyalty strategies

• Collaborative and content-based recommendations
• Market basket and association-rule analysis
• Loyalty offers around churn and lifetime value
• Measuring personalization uplift

Unit 4: Sales forecasting and optimization

• Time-series forecasting and seasonal models
• Regression demand modeling and price elasticity
• Dynamic pricing and promotion optimization
• Inventory: reorder points and safety stock

Unit 5: Digital performance and the future of retail analytics

• Event-based tracking in Google Analytics 4
• E-commerce dashboards and KPIs
• Privacy and consent under GDPR
• AI-assisted forecasting and first-party data

Capstone case study

• End-to-end dataset: transactions, sessions, inventory
• Choosing metrics, segments, and forecasts
• Walking through analytical trade-offs
• Recommendations for merchandising or marketing

How the course is delivered

Sessions are led by an experienced instructor and built around discussion, worked examples, and documented case studies drawn from real retail and e-commerce situations. Participants work through calculations and interpretations together, review sample dashboards, and follow guided walkthroughs of analytics tools and reports rather than sitting through abstract theory.

Much of the value comes from group analysis of realistic datasets and scenarios, where the class debates what the numbers mean and which decision they support. The emphasis stays on judgment and method, so participants leave able to apply the same reasoning to their own data.

Who should attend

The course suits professionals who use retail and e-commerce data to inform commercial decisions.

• Retail and e-commerce managers responsible for sales, merchandising, or category performance.
• Marketing and sales professionals planning campaigns, promotions, and retention.
• Data and business intelligence analysts supporting retail teams.
• Category and pricing managers who set assortment and price.
• Digital and e-commerce specialists managing online storefronts and measurement.
• Business leaders steering data-informed retail strategy.

About EuroQuest International Training

EuroQuest International Training, founded in 2015 and headquartered in Bratislava, Slovakia, delivers more than 1,000 courses to over 15,000 participants, with training hubs that include Dubai, London, Barcelona, Istanbul, Vienna, Paris, and Geneva.

Frequently asked questions

Do I need a statistics or coding background to follow this course?

No. The course explains each method in plain terms before showing how it applies to retail data, so a working comfort with spreadsheets and business numbers is enough. Analysts with deeper technical skills will still find the treatment of forecasting and segmentation useful for connecting technique to commercial decisions.

Which tools and methods does the course cover?

It covers the analytical methods behind segmentation, recommendation, forecasting, and pricing, including RFM scoring, market basket analysis, cohort and lifetime-value analysis, and time-series forecasting. On the tooling side it looks at web measurement in Google Analytics 4 and at how dashboards present these results, referenced purely as subject matter rather than through any vendor affiliation.

How is this course different from a general marketing analytics course?

The focus stays specifically on retail and e-commerce problems, such as cart abandonment, cross-sell, demand forecasting, dynamic pricing, and inventory. That means the metrics, examples, and case study all reflect the funnel and margin realities of selling products, online and in store, instead of broad marketing measurement.

Related courses

Participants interested in this topic often explore these related courses.

• Retail and E-Commerce Marketing
• Data-Driven Marketing and Customer Segmentation
• Omnichannel Customer Experience Strategies
• Sentiment Analysis and Customer Feedback Insights

Register for this course

Reserve your place to build the analytical skills that turn retail and e-commerce data into pricing, personalization, and forecasting decisions your team can stand behind. Register today to secure a seat on an upcoming session.