Business Intelligence for Retail Sales Analytics Training Course

Participants can identify source data, business keys and the correct analytical grain.

• Point-of-sale transaction records and line-item sales grain
• E-commerce orders, returns and fulfilment status data structures
• Product master data including SKU, brand, category and supplier attributes
• Store master records covering location, format, opening date and region
• Customer and loyalty data with consent, identifier and channel fields
• Promotion, pricing and inventory feeds matched through business keys

Participants can assess retail data quality and specify practical remediation controls.

• Column profiling for nulls, duplicates, outliers and invalid codes
• Reconciliation checks between POS totals and finance sales postings
• Referential integrity tests across product, store and transaction records
• Returns, voids and cancellations treated through explicit transaction rules
• Missing product hierarchies identified using exception reports
• Data quality scorecard covering completeness, validity, timeliness and consistency

Participants can document retail metrics that produce consistent reporting results.

• KPI definition template covering purpose, formula, grain and exclusions
• Net sales calculation after returns, discounts, tax and cancellations
• Comparable-store sales criteria based on opening and closure dates
• Markdown rate, sell-through and stock-turn calculation conventions
• Target, threshold and variance rules for performance scorecards
• Metric ownership, approval workflow and controlled definition changes

Participants can design a star schema for retail sales reporting.

• Kimball dimensional modelling principles applied to retail transaction analysis
• Sales fact table measures, foreign keys and declared grain
• Date dimension with fiscal periods, holidays and comparable weeks
• Product dimension hierarchies from SKU through department and division
• Store dimension attributes for format, region, cluster and opening status
• Slowly changing dimensions preserving historical product and store attributes

Participants can model customer, promotion and channel data for retail analysis.

• Customer dimension design using surrogate keys and source identifiers
• Anonymous transactions handled without distorting customer purchase measures
• Promotion bridge tables for multi-product and overlapping campaign assignments
• Channel dimension separating store, web, mobile and marketplace sales
• Basket identifiers linking transaction lines into customer purchase events
• Campaign response facts separating exposure, redemption and purchase outcomes

Participants can query retail tables to produce accurate sales summaries.

• SELECT, WHERE and ORDER BY applied to transaction extracts
• INNER and LEFT JOIN patterns for retail master data
• GROUP BY aggregations for sales, units and margin
• CASE expressions for channel, promotion and return classifications
• Date filters using fiscal weeks, periods and trading days
• Query validation through row counts, totals and sample transactions

Participants can write SQL analyses for store, category and SKU performance.

• Window functions calculating store rankings and category contribution
• Year-over-year comparisons using matched fiscal period query logic
• Common table expressions structuring multi-stage retail sales analysis
• Product mix analysis using sales share, units share and margin share
• Store productivity measures calculated per square metre and labour hour
• Exception queries identifying negative margins, unusual discounts and missing prices

Participants can prepare retail data and configure a Power BI semantic model.

• Power Query transformations for data types, null handling and standardisation
• Appending monthly transaction files into a controlled sales table
• Merging product attributes using validated business key matches
• Star-schema relationships configured with appropriate filter direction
• Model naming conventions for tables, measures and business fields
• Refresh parameters separating development, test and production data sources

Participants can create DAX measures for core retail trading performance.

• Base measures for net sales, units, cost and gross margin
• CALCULATE filter context applied to channel, category and store analysis
• Time intelligence measures for prior year, year-to-date and rolling periods
• Comparable-store measures excluding ineligible stores from period comparisons
• Average transaction value and units per basket calculations
• Margin rate measures designed to avoid incorrect ratio aggregation

Participants can diagnose sales movements against plans and prior periods.

• Trend decomposition separating volume, price, mix and calendar effects
• Budget versus actual variance calculated in value and percentage terms
• Waterfall analysis explaining sales movement between two trading periods
• Daily sales run rates compared with plan and prior year
• Seasonality patterns identified through weekly and monthly index measures
• Exception thresholds prioritising material store and category variances

Participants can assess product and category performance using commercial measures.

• ABC analysis classifying products by cumulative sales or margin contribution
• Pareto analysis identifying categories driving disproportionate revenue and profit
• Assortment productivity measured through sales, margin and SKU counts
• Price-band analysis comparing demand, margin rate and unit movement
• New-product performance tracked from launch date and distribution coverage
• Long-tail products assessed for low sales, margin and inventory productivity

Participants can analyse customer value, repeat purchase and basket behaviour.

• RFM segmentation using recency, frequency and monetary value scores
• Cohort analysis tracking repeat purchasing by first purchase month
• Customer retention rate calculated using clearly defined active periods
• Basket affinity analysis using support, confidence and lift measures
• Cross-sell opportunities identified from category co-purchase patterns
• Customer value distributions compared across stores, channels and segments

Participants can evaluate promotion and price outcomes with suitable comparison methods.

• Promotion baseline established using comparable non-promoted trading periods
• Incremental sales separated from promoted sales through baseline comparison
• Cannibalisation indicators comparing affected categories during campaign windows
• Discount depth analysed against unit response and margin movement
• Control-store comparison for promotional tests with matched store characteristics
• Promotion scorecard covering uptake, incrementality, margin and stock availability

Participants can connect availability data to sales performance and trading action.

• On-shelf availability measures derived from inventory and availability records
• Stockout episodes matched to sales patterns and product demand
• Lost-sales estimation using pre-stockout demand and comparable store data
• Weeks of supply calculated from stock position and sales velocity
• Sell-through analysis for seasonal, clearance and launch inventory
• Availability exceptions prioritised by sales value, margin and customer impact

Participants can design retail dashboards that support timely commercial decisions.

• Dashboard audience mapping for executives, category teams and store managers
• Information hierarchy placing key trading exceptions before diagnostic detail
• Visual selection matched to trend, comparison, contribution and distribution questions
• Drill-through paths from regional results to store and SKU detail
• Annotation techniques explaining events, promotions and operational disruptions
• Management narrative structured as finding, evidence, implication and action

Participants can specify controls for reliable and appropriately restricted retail reporting.

• Row-level security restricting regional and store-level report access
• Refresh schedules aligned to intraday, daily and weekly trading decisions
• Reconciliation checklist comparing dashboard totals with approved source reports
• Measure testing using known transactions, edge cases and expected results
• Report release process covering peer review, approval and version control
• Data lineage documentation from source extraction to published metric

Participants can convert analytical findings into prioritised retail management actions.

• Root cause analysis using five whys and issue trees
• Impact-effort matrix prioritising trading interventions and analytical follow-up
• Decision log documenting recommendation, owner, deadline and expected measure
• Scenario comparison for price, promotion, availability and assortment choices
• Assumption register separating observed facts from analytical estimates
• Executive briefing structure focused on decision, evidence, risk and action

Participants can build, review and present a complete retail sales analytics solution with documented KPIs, model, dashboard and management recommendation.

• Business brief translated into retail KPIs, decisions and stakeholder requirements
• Source data profiled, cleaned and reconciled against stated control totals
• Dimensional model documented with facts, dimensions, grain and relationships
• SQL queries and DAX measures validated through defined test cases
• Power BI dashboard built for sales, margin, promotion and availability decisions
• Peer review tests usability, accuracy, traceability and recommendation quality
• Management presentation communicates findings, actions, owners and expected outcomes