Reported TikTok conversions do not match the orders in the store, and it is unclear whether events are missing or consent is blocking them. In this case, the immediate task is to review TikTok conversion evidence alongside store orders. The workflow should produce a tracking diagnosis and repeatable test plan, each match verified against the records.
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 review TikTok conversion evidence alongside store orders.
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: [pixel and store event logs, the test journey with its browser and consent conditions, the pixel configuration and configured events, and order identifiers and timestamps from the store export]. Match each reported conversion to an order on event identifier, order identifier and timestamp inside a window that allows for processing delay, then classify every unmatched case as a missing event, a consent-suppressed event or an event blocked by the browser, recording the evidence that supports each classification.
Return a tracking diagnosis and repeatable test plan 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 event logs from the pixel and the store, the test journey used for each check, the browser and consent conditions under which events were observed, the pixel configuration with the events it is set to fire, and the order identifiers and timestamps from the store export.
Match each reported conversion to an order on event identifier, order identifier and timestamp inside a window that allows for processing delay, then classify every unmatched case as a missing event, a consent-suppressed event or an event blocked by the browser, recording the evidence that supports each classification.
Deliver a tracking diagnosis and repeatable test plan with every match verified against the records, then confirm that the count of matched plus classified unmatched events equals the reported conversion count and that the test plan states the journey, browser, consent state and expected event for each step a tester repeats.
Cover decision rules, frequent misunderstandings, and required inputs so the team aligns on prerequisites before execution.
The event identifier, the order identifier and matching timestamps on both sides. All three together show that one specific purchase was observed, whereas comparing daily totals can look correct while two entirely different sets of orders sit underneath.
Visitors who decline tracking can still buy, so their orders appear in the store with no matching event. Unmatched events are therefore classified by consent state first, and only those expected to fire are counted as genuine tracking failures.
Over a window that covers processing delay, timezone difference and late event delivery. Matching on a single calendar day rejects valid conversions arriving the next morning and inflates the failure count, which then points the diagnosis at the wrong component.
Start with scenarios under the same topic, then expand into cross-topic matches suggested by similarity.
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