E-Commerce Data Analytics Training Course

Participants can map the data sources, identifiers and flows needed for e-commerce analysis.

• Storefront, order management, CRM and advertising data source mapping
• Customer, session, order and product identifiers reconciled between systems
• Event schemas connecting browsing behaviour, baskets, checkout and purchases
• First-party, second-party and third-party data distinguished by collection method
• Data lineage documented from capture through transformation to reporting output
• Common integration failures affecting channel, customer and transaction analysis

Participants can assess e-commerce datasets for reliability, governance and privacy risks.

• Completeness, validity, uniqueness, timeliness and consistency quality dimensions
• Reconciliation checks between orders, payments, refunds and finance records
• Duplicate customer records identified through deterministic matching rules
• Missing campaign parameters diagnosed using URL and tagging inspections
• Data dictionaries specifying fields, definitions, owners and permitted values
• Consent, purpose limitation and retention applied to customer analytics

Participants can specify GA4 events and parameters that support commercial analysis.

• GA4 event model compared with pageview-based web measurement approaches
• Recommended events configured for product views, baskets, checkout and purchases
• Event parameters capturing product, promotion, currency, value and transaction details
• User properties defined for customer status, market and membership tier
• Cross-domain measurement planned for payment providers and booking engines
• DebugView and real-time reports used to verify event collection

Participants can create a tagging plan that preserves campaign and journey attribution data.

• UTM naming conventions standardised for source, medium, campaign and content
• Google Tag Manager variables mapped to commerce events and parameters
• Tagging specifications assigning implementation responsibilities and acceptance criteria
• Promotion identifiers retained from impression through purchase confirmation
• Paid search, social and affiliate links checked for attribution continuity
• Version control records maintained for tag changes and measurement releases

Participants can use SQL to retrieve and structure e-commerce data for analysis.

• SELECT, WHERE and ORDER BY statements retrieving targeted transaction records
• CASE expressions classifying orders, customers and marketing channels
• GROUP BY aggregations calculating revenue, orders, units and average basket value
• INNER and LEFT JOIN operations combining customer, order and campaign tables
• Date functions creating daily, weekly and monthly trading periods
• Query validation through row counts, totals and sampled records

Participants can write analytical SQL queries for customer, product and order insights.

• Common table expressions structuring multi-step customer and order calculations
• Window functions ranking products by revenue, margin and purchase frequency
• Customer purchase histories sequenced using partitioned transaction dates
• Repeat purchase rates calculated from first and subsequent order records
• Product affinity pairs identified from orders containing multiple stock keeping units
• Returns and refunds separated from gross sales in performance queries

Participants can diagnose conversion losses using funnel metrics and behavioural evidence.

• Funnel stages defined from landing page through completed transaction
• Step conversion rates calculated using consistent users, sessions or events
• Drop-off patterns segmented by device, channel, market and customer status
• Checkout abandonment investigated through payment, delivery and error events
• Micro-conversions assessed for search, filters, wishlists and account creation
• Friction hypotheses prioritised by impact, evidence and implementation effort

Participants can segment customers with RFM scoring and propose differentiated actions.

• Recency, frequency and monetary value calculated from customer purchase histories
• RFM quintiles assigned using transparent scoring and threshold rules
• High-value, at-risk, new and dormant customer segments profiled
• Segment revenue, margin and order behaviour compared with population benchmarks
• Lifecycle actions matched to acquisition, activation, retention and win-back needs
• Suppression rules protecting customers from unsuitable campaign contacts

Participants can measure retention patterns and identify cohort-level performance changes.

• Acquisition cohorts formed by first purchase week, month or campaign
• Retention matrices calculated using returning customers and elapsed periods
• Revenue cohorts compared using cumulative spend and repeat order value
• Channel cohorts assessed for downstream value rather than initial conversion
• Seasonality separated from cohort effects using comparable observation windows
• Retention changes translated into hypotheses for service, product and CRM actions

Participants can calculate customer value and assess acquisition economics for commercial decisions.

• Historical customer lifetime value calculated from realised contribution margin
• Predictive lifetime value assumptions documented for retention and purchase frequency
• Customer acquisition cost allocated by channel, campaign and conversion cohort
• Payback period measured against contribution rather than gross revenue
• LTV to CAC ratios interpreted with acquisition maturity and payback timing
• Margin adjustments included for discounts, returns, fulfilment and service costs

Participants can analyse product performance and basket behaviour to inform merchandising actions.

• Product performance assessed through views, conversion, units, revenue and margin
• Search terms analysed for demand, zero results and conversion gaps
• Basket analysis identifies frequently purchased product combinations and attachments
• Assortment performance compared by category, price band and availability
• Stock-outs isolated from low demand using inventory and product-view data
• Discount effectiveness tested against margin, basket value and repeat purchase

Participants can compare channel performance while accounting for attribution limitations.

• Last-click attribution evaluated against customer journey and channel role evidence
• First-click, linear and position-based models compared for campaign interpretation
• Assisted conversions measured for upper-funnel channels and content activity
• Incrementality questions separated from platform-reported conversion claims
• Channel scorecards combine spend, conversion, margin, retention and payback
• Attribution limitations communicated without overstating causal conclusions

Participants can evaluate campaign and CRM performance using commercial and customer metrics.

• Campaign objectives translated into measurable audience, action and value metrics
• Email performance assessed through delivery, engagement, conversion and unsubscribes
• Paid media results reconciled between platform reports and transaction data
• Audience overlap examined to prevent duplicated contact and inflated reach
• Control groups used to estimate CRM campaign incremental impact
• Frequency, recency and fatigue signals monitored for contact policy decisions

Participants can design e-commerce experiments that produce credible decision evidence.

• Test hypotheses framed with intervention, target population and expected mechanism
• Primary metrics selected before launch to prevent result-driven interpretation
• Randomisation methods assigned for users, sessions, regions or campaigns
• Sample size and test duration estimated from baseline conversion rates
• Statistical significance distinguished from commercial significance and implementation risk
• Guardrail metrics protect margin, returns, service demand and customer experience

Participants can specify dashboards that direct attention to commercial decisions and exceptions.

• Dashboard audiences defined for trading, marketing, operations and executive decisions
• KPI hierarchies arranged from business outcome to diagnostic supporting metrics
• Time comparisons selected for seasonality, targets, forecasts and prior periods
• Control charts distinguish normal variation from significant performance exceptions
• Visual encodings matched to ranking, trend, composition and distribution questions
• Dashboard annotations record decisions, assumptions, anomalies and follow-up actions

Participants can produce trading reports and short-term forecasts using relevant commercial drivers.

• Daily trading reports reconcile traffic, orders, revenue, margin and returns
• Variance analysis separates volume, conversion, price and mix effects
• Forecast assumptions document traffic, conversion, basket value and availability
• Moving averages smooth volatile demand patterns for short-term planning
• Promotional calendars incorporated into trading forecasts and performance comparisons
• Exception reports escalate material deviations with proposed commercial actions

Participants can communicate analytical findings as clear recommendations for different stakeholders.

• Executive narratives state decision, evidence, recommendation, owner and timing
• Analytical assumptions disclosed with data limitations and confidence levels
• Recommendations prioritised using expected value, feasibility and operational dependencies
• Finance, marketing and operations perspectives reconciled in performance discussions
• Stakeholder questions anticipated through traceable calculations and source references
• Action plans specify next steps, monitoring metrics and review dates

Participants can define a practical operating model for repeatable e-commerce analytics delivery.

• Analytics demand intake distinguishes reporting requests from decision-focused analysis
• Metric ownership assigned between marketing, trading, finance and data teams
• Weekly trading cadence aligns reports, actions, experiments and accountability
• Backlog prioritisation scores commercial impact, urgency, effort and data readiness
• Quality assurance checkpoints applied before dashboard publication and recommendation release
• Analytics documentation maintained through metric catalogues and decision logs

Participants can build, review and present a complete e-commerce analytics recommendation for a defined commercial problem.

• Business question scoped with commercial target, constraints and decision owner
• Measurement framework assembled from customer, transaction, channel and product metrics
• SQL analysis produces validated findings from integrated e-commerce datasets
• Funnel, cohort and segmentation evidence identifies priority growth opportunities
• Dashboard specification and experiment plan support proposed commercial actions
• Executive presentation defends recommendations, assumptions, risks and measurement plan