Segment and channel definitions in Google Analytics do not match the store's own measures, so merging the two hides where they disagree. In this case, the immediate task is to combine Google Analytics evidence in a store reporting view. The workflow should produce a unified reporting model and discrepancy log recording every definition difference it finds.
First replace every [bracketed] placeholder with real store scope and constraints, then add any actual records or data you have, and paste it into your preferred AI assistant or Spark agent. Spark will follow this page's workflow to produce structured output and verification steps.
Act as a Shopify operations specialist.
Help me combine Google Analytics evidence in a store reporting view.
My store context is [describe the business, products and target audience].
My scope is [specify the affected products, pages, customers or reporting period]. Use these inputs: [property reporting timezone and currency, segment and channel grouping rules, conversion event definitions with counting rules, and store-side session, order and revenue figures from the order export for the same window]. Place a figure in a shared panel only where the analytics measure and the store figure use the same definition, timezone and currency, and hold every measure whose basis differs, session-scoped against order-scoped or gross against net, as a separately labelled figure naming its source and the rule that produced it.
Return a unified reporting model and discrepancy log recording every definition difference found for the specified task.
Show the relevant evidence or before-and-after examples and explain how I can verify the result.
If essential data is missing, ask for it and mark unsupported conclusions as unknown.
Do not invent metrics, product claims or customer facts.
For any publication, message, account or store change, use only connected tools within my explicitly authorized scope and report success only after verification; otherwise provide the draft or proposed steps.Split the work into input, judgment, and deliverable phases. Verify each step's output before moving to the next.
Collect the property's reporting timezone and currency, the segment and channel grouping rules applied, every conversion event definition with the counting rule it uses, and the store-side session, order and revenue figures from the order export for the same window.
Place a figure in a shared panel only where the analytics measure and the store figure use the same definition, timezone and currency, and hold every measure whose basis differs, session-scoped against order-scoped or gross against net, as a separately labelled figure naming its source and the rule that produced it.
Deliver the unified reporting model and discrepancy log recording each definition difference found, then verify by reconciling the blended order count against the order export for the same window and confirming every retained panel states its source, timezone, currency and conversion definition.
Cover decision rules, frequent misunderstandings, and required inputs so the team aligns on prerequisites before execution.
Only measures whose definition, timezone and currency match on both sides. A session-scoped conversion count placed next to an order-scoped one invites false subtraction, so mismatched measures stay side by side with their source named instead of being averaged into one number.
Each channel or segment that splits differently from the store's own attribution is written into the discrepancy log with both definitions and the window affected. Readers then know a gap comes from naming rules rather than from missing sales, and no one repairs a definition as if it were a data loss.
Reconciling the blended order count against the order export for the same window, and confirming every panel carries its source label. Where the two disagree, the log states which side was reduced by consent or sampling before the view is published.
Start with scenarios under the same topic, then expand into cross-topic matches suggested by similarity.
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