Business Intelligence for Retail Sales Analytics Training Course

Course overview

Retail sales data is often distributed across point-of-sale systems, ecommerce platforms, loyalty programmes, inventory files and promotional calendars. Analysts can produce sales totals, yet still struggle to explain why margin fell, which stores are underperforming, whether a promotion created incremental demand, or where stock availability is suppressing revenue. This course equips retail professionals to turn transactional data into decision-ready intelligence for trading meetings, category reviews, store operations and commercial planning.

Participants build retail-focused data models and dashboards using Microsoft Power BI, SQL Server, Excel and Snowflake data sources. They learn to define retail measures including like-for-like sales, sell-through, gross margin return on inventory investment, basket value, units per transaction, stock cover, markdown rate and promotion uplift. The course covers data extraction, cleaning, star-schema modelling, DAX calculations, drill-through analysis, geographic store views, exception reporting and dashboard design for different retail stakeholders.

Teaching combines instructor-led demonstrations with hands-on work using a realistic multi-channel retail dataset covering stores, products, customers, promotions, inventory and returns. Participants progressively develop a Retail Sales Performance Dashboard and supporting data model, then present findings from a trading scenario involving weak category performance and excess stock. They leave with reusable report pages, measure definitions, a dashboard specification and an action plan for applying the approach to their own retail environment. A certificate is awarded on completion.

The course is designed for intermediate analysts and commercial professionals who already work with retail data and need stronger business intelligence capability. It is equally relevant to managers sponsoring reporting improvement, because it links technical reporting choices directly to sales, margin, inventory and promotional decisions.

Course objectives

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

• Design a retail sales star schema linking transactions, products, stores, customers, promotions and calendar dimensions
• Write SQL queries to extract, join and validate retail sales, stock and returns data
• Build Power Query transformations that standardise product hierarchies, store attributes and trading dates
• Create DAX measures for like-for-like sales, gross margin, average basket value, sell-through and stock cover
• Analyse category, store, channel and customer performance using drill-down, decomposition and variance methods
• Evaluate promotion effectiveness using baseline sales, uplift, cannibalisation and margin-impact calculations
• Produce role-based Power BI dashboard pages for trading managers, category teams and store operations leaders
• Present a retail performance insight pack containing findings, root causes, recommended actions and KPI definitions

Benefits of attending

Target competencies

Who should attend

• Retail Data Analysts — who need to convert sales and inventory extracts into reliable commercial insight
• Business Intelligence Analysts — who build reports for trading, merchandising and store operations teams
• Category Managers — who must diagnose category sales, margin, promotion and availability performance
• Merchandise Planners — who need evidence on sell-through, markdowns, stock cover and range performance
• Retail Operations Managers — who need to compare store execution, conversion proxies and local trading results
• Commercial Reporting Managers — who must standardise retail KPIs and improve the usefulness of management dashboards

Requirements and prerequisites

Participants should be comfortable using Excel for filtering, pivot tables and basic formulas, and should understand common retail terms such as sales value, units, margin, stock, returns, stores, SKUs and product categories. Prior experience reading reports or working with sales extracts is expected. Basic SQL awareness, such as SELECT, WHERE and JOIN, is helpful but not essential because core queries are practised in class. No prior Power BI, DAX, data-modelling or Snowflake experience is required. This is not a programming course; attendees do not need Python, statistics beyond percentages and averages, or database-administration skills.

Training methodology

Each day combines focused instructor-led teaching with guided build sessions in Power BI, SQL and Excel. Participants work from raw retail extracts through to a governed reporting model, using scenarios involving store performance, category margin, promotional activity, stock availability and returns. Short case reviews replicate weekly trading discussions, while paired exercises test KPI choices and interpretation. The final two days are structured as an applied dashboard workshop: participants refine their report, document metric definitions and prepare a practical implementation plan for their own reporting environment.

Course outline

Day 1: Retail BI foundations and commercial questions

• Retail data landscape across POS, ecommerce, loyalty, inventory and promotion systems
• Trading questions for sales, margin, stock, customer and channel performance
• Retail KPI taxonomy and metric governance principles
• Sales value, units, cost, margin and markdown relationships
• Like-for-like sales comparability rules
• Product, store and calendar hierarchy design
• Power BI interface, report views and data-loading workflow

Workshop: Participants map a retail trading question set to required source data, dimensions, measures and report audiences.

Day 2: Extracting and validating retail data with SQL

• Retail transaction table structures and transaction grain
• SELECT, WHERE, GROUP BY and ORDER BY for sales extracts
• INNER JOIN and LEFT JOIN across product, store and customer tables
• Date filtering for trading weeks, fiscal periods and comparable periods
• SQL aggregation for sales, units, discount and return measures
• Data-quality checks for duplicates, missing SKUs and invalid store codes
• Reconciliation of SQL totals against source trading reports

Workshop: Participants write and validate SQL queries that produce a store-by-category sales and margin extract.

Day 3: Preparing retail data in Power Query

• Power Query connection options for SQL Server, Excel and Snowflake sources
• Data type profiling for currency, quantities, dates and product identifiers
• Cleaning inconsistent store, channel and category labels
• Merging sales, returns, inventory and promotion tables
• Appending historical transaction files
• Creating fiscal calendar and trading-week attributes
• Query folding, refresh considerations and transformation documentation

Workshop: Participants build a documented Power Query pipeline that prepares raw retail extracts for analysis.

Day 4: Retail data modelling for reliable reporting

• Star schema principles for retail analytics
• Fact tables for sales, inventory snapshots, returns and promotions
• Conformed dimensions for date, product, store, channel and customer
• Relationship cardinality and cross-filter direction
• Handling role-playing dates for transaction, delivery and promotion periods
• Surrogate keys and product hierarchy maintenance
• Model validation using filter-path and reconciliation tests

Workshop: Participants create a retail star schema and test its relationships against defined reporting scenarios.

Day 5: DAX measures for sales and margin performance

• Measure design versus calculated columns
• DAX aggregation functions for sales, units, cost and returns
• Gross margin, margin percentage and markdown rate measures
• Average basket value and units per transaction calculations
• Time intelligence for week-to-date, period-to-date and year-to-date sales
• Prior-period and prior-year variance calculations
• Like-for-like sales measures using comparable-store flags

Workshop: Participants create and test a governed KPI measure library for a weekly retail trading dashboard.

Day 6: Store, category and channel performance analysis

• Sales variance bridges for price, volume and mix interpretation
• Category hierarchy drill-down from division to SKU
• Store ranking, peer grouping and regional comparison
• Channel analysis across stores, ecommerce and click-and-collect
• Decomposition trees for diagnosing sales movements
• Scatter analysis for sales, margin and stock relationships
• Drill-through pages for store and category investigation

Workshop: Participants diagnose a declining category and produce a store-level root-cause analysis page.

Day 7: Inventory, availability and markdown intelligence

• Inventory snapshot grain and stock-on-hand measures
• Sell-through calculation by SKU, category and season
• Weeks of cover and stock-cover interpretation
• Stock availability proxies and lost-sales indicators
• Aged inventory and slow-moving SKU analysis
• Markdown depth, markdown rate and margin erosion
• Exception thresholds for replenishment and clearance actions

Workshop: Participants build an inventory exception report identifying overstock, low-cover and markdown-risk products.

Day 8: Promotion and customer performance analytics

• Promotion calendar modelling and promotion-to-sales matching
• Baseline sales selection for promotion evaluation
• Promotion uplift and incremental sales calculations
• Cannibalisation and halo-effect interpretation
• Promotion margin and discount-cost assessment
• Customer segmentation using frequency, value and basket behaviour
• Loyalty and non-loyalty customer comparison

Workshop: Participants assess a promotion campaign and produce a recommendation on whether to repeat, revise or stop it.

Day 9: Retail dashboard design and decision communication

• Dashboard wireframing for weekly trading meetings
• Visual selection for KPIs, trends, variance and exceptions
• Slicers, bookmarks, tooltips and drill-through interactions
• Accessible colour use for positive, negative and alert conditions
• Executive summary pages versus analyst investigation pages
• Narrative titles and annotation for commercial insights
• Power BI sharing, workspace roles and row-level security concepts

Workshop: Participants design and peer-review a two-page trading dashboard for executives and category managers.

Day 10: Applied retail BI capstone and implementation planning

• Capstone dataset briefing and commercial scenario analysis
• End-to-end data refresh and model quality checks
• KPI definition catalogue and calculation traceability
• Insight prioritisation by commercial impact and actionability
• Presenting findings in a simulated trading meeting
• Dashboard adoption measures and feedback loops
• Ninety-day retail BI implementation roadmap

Workshop: Participants present their completed Retail Sales Performance Dashboard, insight pack and 90-day implementation plan.

Tools & standards covered

Microsoft Power BI Desktop, Microsoft Excel, Microsoft SQL Server, Snowflake

A typical training day

08:30 – 10:30	First session
10:30 – 10:45	Refreshment break
10:45 – 12:30	Second session
12:30 – 13:30	Lunch and networking
13:30 – 15:00	Third session
15:00 – 15:15	Refreshment break
15:15 – 16:30	Workshop and daily review

Live online deliveries follow the same structure in the East Africa Time zone, with shorter screen blocks and longer breaks.

What the fee includes

How you can take this course

Certification

Participants who complete the full five days receive the Skillset Development Certificate of Completion, stating the course title, course code, dates and delivery format — suitable for professional-development records and employer reimbursement.

Frequently asked questions