Funnel Analysis
Analysing conversion by product variant
Define a variant conversion denominator using identified, available options and matched orders, with clear treatment of later purchases.
Archive
October 2026
Funnel Analysis
Analysing products commonly bought together
Count product pairs in qualifying orders, compare directional rates and shortlist useful combinations for a measured trial.
Revenue Analytics
Avoiding double-counted revenue across channels
When several platforms claim credit for one order, adding their reported revenue can exceed the store's actual sales.
Measurement Planning
Basket and merchandising analytics
Use product pairings, recommendation results, category routes and onsite search evidence to guide merchandising decisions.
Data Quality
Checking for sample imbalance in an ecommerce experiment
Compare observed experiment units with the planned split, trace where imbalance begins and decide whether the store-test result can be used.
Data Quality
Checking product IDs across tracking events
Audit item IDs from product view through purchase. Find parent and variant switches, missing IDs and catalogue mapping changes.
Event Tracking
Checking ecommerce tracking after a theme or app update
Replay affected shopping journeys, inspect event details and monitor tracking after a theme or app update.
Measurement Planning
Choosing a guardrail metric for a store test
Choose store-test guardrails from plausible harms, with the right denominator, timing, data source and response rule.
Measurement Planning
Choosing store metrics from business decisions
Choose ecommerce metrics by linking each business decision to a counting unit, comparison, owner and action.
Funnel Analysis
Comparing category navigation paths
Compare category routes from a defined entry task to relevant products, with clear counting rules and path-analysis limits.
Funnel Analysis
Comparing customer value by product entry point
Assign customers from their first-order products and compare observed value at the same age without hiding multi-item orders.
Attribution
Comparing first-touch and last-touch store reports
First-touch and last-touch reports answer different questions.
Measurement Planning
Comparing forecast demand with actual order patterns
Compare issued variant forecasts with qualifying orders, flag stock-constrained periods and read forecast errors within their limits.
Funnel Analysis
Comparing funnels by device and traffic source
Compare funnel progression across devices and traffic sources while keeping segment definitions, source scope and cross-device limits clear.
Measurement Planning
Comparing product views with actual purchases
Compare product-view and purchase counts, identify when a viewer-to-buyer rate is possible, and investigate gaps without mislabelling the measure.
Attribution
Comparing promotion buyers with ordinary customers
Define buyer groups, use equal follow-up windows and interpret repeat purchasing without mistaking selection for a promotion effect.
Funnel Analysis
Comparing repeat purchase across acquisition cohorts
Compare distinct repeat buyers across acquisition cohorts using a fixed follow-up window and clearly defined source labels.
Data Quality
Connecting sales analysis with stock availability
Join variant order lines to dated orderability intervals and keep uncertain stock periods visible before comparing sales rates.
Funnel Analysis
Customer cohort analysis for acquisition-labelled ecommerce value
Compare observed ecommerce value for acquisition-labelled cohorts at equal age, then decide which gap needs investigation.
Measurement Planning
Defining a conversion experiment before changing the store
Write a store-test brief that fixes eligibility, assignment, the conversion formula, decision threshold and end rule before launch.
Event Tracking
Defining purchase, refund and repeat-customer events
Define qualifying purchases, processed refunds and repeat customers with clear order, timing and identity rules.
Data Quality
Detecting missing currencies and order values
Check purchase events for usable currency and value fields, distinguish genuine zeroes from missing data, and trace exceptions to orders.
Funnel Analysis
Distinguishing weak demand from an out-of-stock problem
Check variant orderability, visibility and observed interest before deciding that low ecommerce sales reflect weak demand.
Attribution
Ecommerce acquisition attribution
Attribution assigns credit for an order to marketing interactions under a chosen rule.
Measurement Planning
Ecommerce analytics operations
Set owners, review periods and change records that keep ecommerce reports useful as data and systems change.
Funnel Analysis
Ecommerce conversion funnels
Define shopping funnel stages and counting rules, then use stage gaps to guide investigation without mistaking them for checkout failures.
Attribution
Ecommerce customer value analysis
Structure a customer value report around recorded orders, comparable observation windows, clear amount rules and visible limitations.
Measurement Planning
Ecommerce dashboard design
An ecommerce dashboard should help a team decide what to do today and explain what happened after the numbers settle. Start with decisions, definitions and data …
Data Quality
Ecommerce data quality
Check the definitions, completeness, consistency and freshness of ecommerce data before using store reports to make decisions.
Event Tracking
Ecommerce event tracking
Define ecommerce event triggers, item and order IDs, purchase rules and checks before relying on store reporting.
Measurement Planning
Ecommerce experimentation
Plan an ecommerce experiment around a clear decision, eligible shoppers, one primary outcome, guardrails and a trustworthy result.
Measurement Planning
Ecommerce measurement planning
Plan ecommerce measurement around business decisions, clear metric definitions, source records and a review cycle.
Revenue Analytics
Ecommerce revenue and margin analytics
Read store revenue and margin together by defining eligible orders, discounts, reversals, cost coverage and reporting dates.
Funnel Analysis
Estimating cannibalisation between discounted products
Estimate whether a discounted product displaced nearby items by comparing product and group outcomes against a stated baseline.
Measurement Planning
Estimating customer value from observed purchase history
Reconstruct observed customer value from qualifying orders, a fixed time window, later adjustments and the original entrant count.
Data Quality
Filtering internal and test orders from reports
Classify simulated and real test orders, apply report exclusions consistently, and understand the limits of internal-traffic filters.
Revenue Analytics
Finding products with high revenue but weak margin
Screen high-selling products for weak margin using comparable retained sales, dollar profit, cost coverage and adjustment dates.
Revenue Analytics
Gross revenue versus net sales in store reports
Understand how gross product sales become net sales, with checks for discounts, reversals, tax, delivery, order status and dates.
Measurement Planning
Grouping customers by first completed purchase
Define a completed purchase, resolve customer identities and assign first-purchase cohorts without confusing first-order date with payment confirmation.
Data Quality
Handling incomplete histories in a customer-value report
Separate short follow-up, missing earlier orders, uncertain identity and unsettled adjustments in customer-value reports.
Data Quality
Identifying search terms that return no useful products
Find onsite searches with empty or weak results, inspect the products shown and choose a measured fix.
Attribution
Interpreting unattributed and direct traffic
“Direct” and “unknown” are reporting labels, not customer motivations.
Data Quality
Linking returns to product-level performance
Link recorded physical returns, reasons and product-value reversals to original order lines using comparable follow-up windows.
Measurement Planning
Maintaining a dictionary of store metrics
Maintain metric formulas, owners and effective dates so report users can reproduce store figures and recognise definition changes.
Event Tracking
Mapping product views, baskets and checkout steps
Map product, basket and checkout actions to precise ecommerce event triggers, including express routes and skipped steps.
Revenue Analytics
Measuring a sale event after discounts and returns
Reconcile sale-event orders, discounts, returns and costs without double-counting orders or treating an early result as final.
Attribution
Measuring acquisition quality beyond the first order
The cheapest first order is not necessarily the best acquisition.
Funnel Analysis
Measuring drop-off between shopping stages
Calculate shopping-stage drop-off with consistent counts and clear denominators, then check skipped routes and missing events.
Revenue Analytics
Measuring lost shopping opportunities from unavailable variants
Define and count observed unavailable-variant actions while keeping later purchases and unobserved demand separate from lost sales.
Attribution
Measuring the effect of a product recommendation
Separate recommendation exposure and clicks from sales effect, then compare eligible groups on a defined commercial outcome.
Measurement Planning
Planning an analytics migration without breaking historical comparisons
Protect ecommerce trends during an analytics migration with saved definitions, overlap checks and clearly labelled breaks.
Event Tracking
Preventing duplicate purchase events
Control repeated purchase tracking with one order milestone, stable transaction IDs and coordinated sends; trace where duplicates begin.
Measurement Planning
Product performance analytics
Compare product interest, purchases, retained sales, margin and physical returns with clear units, time periods and cost coverage.
Measurement Planning
Promotion analysis
Assess an ecommerce promotion through retained sales, contribution, customer and product effects, and an estimate of demand without the offer.
Revenue Analytics
Reading cohort value without assuming future retention
Calculate observed cohort value per original customer, compare equal follow-up windows and separate recorded sales from projected spend.
Data Quality
Reconciling analytics revenue with store orders
Reconcile GA4 purchase value with store orders by aligning the order population, revenue metric, discounts, refunds, currency and dates.
Data Quality
Reconciling event totals across measurement tools
Align event definitions and reporting periods, match purchase records when possible, and investigate gaps between measurement tools.
Measurement Planning
Removing metrics that do not inform a trading decision
Every daily dashboard metric should answer a question or trigger an action. If it only fills space, move it to a detailed report or retire it.
Attribution
Reporting a test result without overstating significance
Write a store-test result with group rates, effect size, uncertainty, data checks, guardrails and a bounded decision.
Revenue Analytics
Reporting contribution after discounts and returns
Calculate contribution from retained product sales, product cost and other variable costs while showing return treatment and missing-cost coverage.
Revenue Analytics
Reporting uncertainty in lifetime-value estimates
Separate observed customer value from forecasts, label scenario ranges correctly and assess estimates against later outcomes.
Data Quality
Reviewing access to customer-level analytics
Review who can view or export identifiable store data, check effective permissions and record a decision for each grant.
Revenue Analytics
Scheduling monthly reconciliation with finance
Set a monthly ecommerce finance handoff: agree the basis, preserve extracts, explain differences and record sign-off.
Funnel Analysis
Separating checkout failures from abandoned browsing
Separate browsing exits, incomplete checkouts and recorded failures using funnel observations alongside order and payment evidence.
Measurement Planning
Separating leading indicators from confirmed outcomes
Product-page visits, add-to-cart events and checkout starts can warn of a change before sales settle. They are signals to investigate, not sales in another name.
Measurement Planning
Separating seasonal demand from promotional uplift
Build a seasonal baseline for an ecommerce sale, show uncertainty in estimated uplift and plan a stronger comparison for the next offer.
Data Quality
Showing data freshness in a store report
A report without an update time can make yesterday’s incomplete data look final. Show when each source was last processed and whether the current period may still change.
Revenue Analytics
Store demand and stock analytics
Read variant orders alongside orderable time and saleable stock before making replenishment, transfer or range decisions.