Course Introduction
The Retail Data Analytics and Merchandising Intelligence Training Course provides a practical and comprehensive framework for using data to improve retail merchandising decisions, commercial performance, inventory productivity, and customer value. The course connects retail data from point-of-sale systems, e-commerce platforms, product catalogs, inventory systems, customer programs, promotions, and operational sources to analytical methods that support better merchandise planning and execution.
Modern retail organizations generate large volumes of transactional, product, customer, pricing, inventory, and channel data, making analytical capability increasingly important for merchants, planners, category managers, buyers, and commercial leaders. Retail analytics can help organizations evaluate product performance, understand demand patterns, identify underperforming items, optimize stock availability, and develop more responsive merchandising strategies. Current retail platforms increasingly combine unified data foundations, predictive demand forecasting, assortment optimization, pricing, promotions, and inventory intelligence.
The course places particular emphasis on assortment and merchandising intelligence. Product hierarchy, category structure, channel availability, store clusters, product variants, and assortment breadth and depth are essential analytical dimensions for retailers. For example, Microsoft Dynamics 365 Commerce uses product hierarchies for merchandising, pricing, promotions, reporting, and assortment planning, while its assortment functionality determines which products are available through particular channels and periods.
Participants will learn how to transform retail data into actionable insights across sales, inventory, pricing, promotions, customer behavior, category performance, and store-level operations. The course introduces practical techniques for sell-through analysis, stock productivity, markdown analysis, basket analysis, customer segmentation, demand forecasting, price elasticity, promotion evaluation, and assortment optimization. These capabilities support more informed decisions about what products to sell, where to sell them, when to replenish them, and how to evaluate their commercial contribution.
A strong focus is placed on connecting analytical outputs with real merchandising decisions rather than treating analytics as an isolated reporting function. Participants will examine how historical performance, geographic differences, seasonality, product attributes, trend adoption, placement, and store-level behavior can inform assortment decisions. Current Oracle Retail documentation describes assortment planning in terms of product breadth and depth, historical performance, color or attribute effectiveness, trend adoption, geographic selling, and automated recommendations that can be reviewed or overridden by planners.
By the end of the training, participants will be able to develop retail intelligence frameworks that connect data preparation, descriptive analytics, diagnostic analysis, forecasting, optimization, visualization, and commercial action. Through practical exercises and a capstone project, learners will build an integrated merchandising intelligence solution capable of supporting category reviews, assortment decisions, inventory actions, pricing and promotion analysis, customer insights, and executive retail performance reporting.
Duration
5 days
Who Should Attend
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Retail merchandising managers responsible for product assortment and commercial performance.
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Merchandise planners seeking stronger analytical and forecasting capabilities.
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Category managers evaluating category, SKU, supplier, and customer performance.
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Retail buyers who need data-driven product selection and purchasing insights.
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Inventory planners responsible for stock availability, turnover, and replenishment decisions.
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Pricing and promotion analysts evaluating price, discount, and promotional effectiveness.
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Retail business analysts working with sales, product, customer, and inventory datasets.
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E-commerce analysts responsible for digital product and customer performance intelligence.
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Store operations managers seeking store-level sales and merchandise performance insights.
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Commercial managers responsible for revenue, margin, sell-through, and inventory productivity.
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Data analysts supporting retail planning, merchandising, customer, or supply chain functions.
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Retail executives and decision-makers seeking practical approaches to data-driven merchandising.
Course Objectives
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Develop a comprehensive understanding of retail data analytics and how analytical intelligence supports merchandising, planning, inventory, pricing, and commercial decision-making.
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Identify, integrate, clean, and validate retail datasets from point-of-sale, e-commerce, inventory, product, customer, pricing, promotion, and operational systems.
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Analyze SKU, category, brand, supplier, store, channel, and regional performance using meaningful retail KPIs and multidimensional analytical techniques.
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Evaluate assortment breadth, depth, product productivity, sell-through, demand patterns, and localized performance to support evidence-based assortment decisions.
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Apply inventory analytics to understand stock turnover, availability, overstocks, stockouts, aging inventory, replenishment performance, and working capital implications.
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Analyze pricing, discounts, markdowns, and promotional activity to understand their relationships with sales volume, revenue, margin, demand, and customer response.
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Apply customer and transaction analytics to identify purchasing patterns, customer segments, basket relationships, retention opportunities, and behavioral trends.
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Develop demand forecasts and scenario analyses that incorporate seasonality, trends, promotions, historical sales, product lifecycle effects, and relevant business assumptions.
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Design retail dashboards and merchandising intelligence reports that communicate actionable findings to buyers, planners, category managers, store teams, and executives.
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Complete an integrated retail analytics project that converts transactional and merchandising data into practical recommendations for assortment, inventory, pricing, promotion, and commercial performance.
Comprehensive Course Outline
Module 1: Retail Data Analytics and Merchandising Intelligence Foundations
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Understanding the retail value chain, merchandising lifecycle, commercial planning process, and role of analytics in modern omnichannel retail decision-making.
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Exploring retail data domains including transactions, products, customers, stores, channels, inventory, pricing, promotions, suppliers, and digital engagement.
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Defining retail KPIs such as sales, units, average selling price, gross margin, sell-through, stock turnover, inventory productivity, and conversion.
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Establishing a merchandising intelligence framework that connects business questions, analytical methods, performance indicators, insights, decisions, and measurable commercial outcomes.
Module 2: Retail Data Integration, Preparation and Quality Management
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Designing retail analytical datasets by integrating point-of-sale, e-commerce, inventory, product master, customer, pricing, promotion, and store-level information.
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Developing product, category, brand, store, channel, and customer dimensions that support consistent retail analysis across multiple business functions.
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Applying data cleansing, deduplication, missing-value treatment, validation, reconciliation, and anomaly identification to improve analytical reliability.
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Building reusable retail data preparation workflows that enable consistent reporting, recurring analysis, and scalable merchandising intelligence.
Module 3: Sales, Category and Product Performance Analytics
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Analyzing sales performance across SKU, product hierarchy, category, brand, store, region, channel, customer segment, and time period to identify commercial patterns.
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Measuring product productivity using sales contribution, unit velocity, margin contribution, sell-through, average transaction value, and inventory-related indicators.
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Conducting category performance analysis to identify growth drivers, declining products, concentration risks, substitution patterns, and opportunities for portfolio improvement.
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Developing retail performance scorecards and diagnostic analyses that help merchants move from high-level results toward specific product and category actions.
Module 4: Assortment Planning and Merchandising Intelligence
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Understanding assortment breadth and depth, product variants, store clusters, local preferences, channel requirements, and lifecycle considerations in assortment planning.
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Evaluating historical product performance, attribute effectiveness, geographic selling patterns, trend adoption, and store-level demand to inform assortment decisions.
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Applying assortment rationalization techniques to identify products for expansion, retention, replacement, localization, or removal based on evidence and business constraints.
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Developing assortment scenarios that balance customer relevance, product productivity, inventory availability, space constraints, profitability, and strategic merchandising objectives.
Module 5: Inventory Analytics, Availability and Stock Productivity
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Measuring inventory turnover, days of supply, sell-through, stock aging, stockouts, overstocks, inventory value, and product availability across retail locations.
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Analyzing relationships between demand, inventory levels, replenishment activity, lead times, seasonality, promotions, and service-level performance.
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Identifying inventory exceptions and opportunities for reallocation, replenishment adjustment, markdown intervention, assortment refinement, or store-level balancing.
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Developing inventory intelligence dashboards that connect stock availability and productivity with sales performance, customer demand, and working capital considerations.
Module 6: Pricing, Promotion and Markdown Analytics
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Evaluating base prices, promotional prices, discounts, markdowns, price changes, and customer response across products, stores, channels, and time periods.
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Measuring promotional performance using incremental sales, units, revenue, margin, baseline demand, promotional lift, cannibalization, and post-promotion effects.
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Exploring price sensitivity and elasticity concepts to understand how changes in price may influence demand, volume, revenue, and profitability.
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Building pricing and markdown analytical frameworks that support commercial reviews, promotional planning, competitive analysis, and end-of-life inventory decisions.
Module 7: Customer, Basket and Omnichannel Analytics
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Analyzing customer purchasing behavior using transaction frequency, monetary value, product affinity, recency, channel activity, and category engagement.
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Applying customer segmentation methods to distinguish high-value, growth, occasional, declining, price-sensitive, and category-specific customer groups.
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Conducting market basket and product affinity analysis to identify complementary products, cross-selling opportunities, basket relationships, and merchandising combinations.
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Connecting store, e-commerce, loyalty, and transaction data to develop a broader view of customer journeys and omnichannel purchasing behavior.
Module 8: Demand Forecasting and Advanced Merchandising Analytics
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Developing retail demand forecasts using historical sales, seasonality, trends, product lifecycle, promotions, calendar effects, and relevant external business signals.
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Evaluating forecast accuracy and bias while identifying products, categories, stores, or periods where forecasting performance requires additional intervention.
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Applying scenario analysis to examine alternative assumptions involving demand growth, promotional activity, assortment changes, pricing, inventory constraints, and market conditions.
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Exploring advanced analytics and machine learning concepts for demand prediction, store clustering, product classification, anomaly detection, and merchandising recommendations.
Module 9: Retail Dashboards, Visualization and Decision Intelligence
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Designing executive and operational dashboards that present sales, margin, inventory, assortment, pricing, promotion, customer, and store performance through actionable visualizations.
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Selecting appropriate charts, measures, filters, hierarchies, drill-downs, alerts, and exception views for different retail management and merchandising audiences.
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Developing category and merchandise review dashboards that allow users to investigate performance differences by product, store, channel, region, customer segment, and time.
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Applying storytelling and decision-intelligence principles to translate complex retail datasets into concise recommendations, priorities, risks, and measurable actions.
Module 10: Capstone Retail Data Analytics and Merchandising Intelligence Project
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Building an end-to-end retail analytics solution using transactional, product, inventory, customer, pricing, promotion, and store data from a realistic merchandising scenario.
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Diagnosing commercial performance and identifying the principal drivers of sales, margin, inventory productivity, assortment effectiveness, and customer behavior.
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Developing evidence-based recommendations covering assortment, inventory, pricing, promotions, customer opportunities, and store or channel performance improvement.
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Presenting the final merchandising intelligence dashboard and analytical recommendations as an executive-ready decision framework with measurable performance indicators.
Training Approach
This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.
Tailor-Made Course
This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: training@upskilldevelopment.com Tel: +254 721 331 808
Training Venue
The training will be held at our Upskill Training Centre. We also offer training for a group (at a discount of 10% to 50%) at requested location all over the world. The Onsite course fee covers the course tuition, training materials, two break refreshments, buffet lunch, airport transfers, Upskill gift package, and guided tour.
Visa application, travel expenses, dinners, accommodation, insurance, and other personal expenses are catered by the participant
Certification
Participants will be issued with Upskill certificate upon completion of this course.
Airport Pickup and Accommodation
Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: training@upskilldevelopment.com, +254 721 331 808
Terms of Payment:
Unless otherwise agreed between the two parties’ payment of the course fee should be done 3 working days before commencement of the training so as to enable us to prepare better.
