The Facebook pixel is installed but campaign reports show no purchases, and the merchant cannot tell whether it loads. In this case, the immediate task is to diagnose missing Facebook pixel events. The workflow should produce a tracking diagnosis and a repeatable test plan, distinguishing an absent pixel from one that fires without parameters.
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 diagnose missing Facebook pixel events.
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: [the pixel identifiers configured and every install location, the events the merchant expects, the identifiers of the test orders placed, the consent and browser conditions in force, and the window covered by the campaign reports]. Decide for each expected event whether the pixel is absent, installed but not firing, firing without parameters, or blocked by consent or the browser, by running one test journey and comparing the browser capture, the merchant's logs and the order record for the same identifier.
Return a tracking diagnosis and a repeatable test plan with each event confirmed against the supplied records 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 pixel identifiers configured on the store together with every place each one is installed, meaning the theme, an app or the checkout, the events the merchant expects to see, the identifiers of the test orders placed, the consent and browser conditions in force, and the window the campaign reports cover.
Decide for each expected event whether the pixel is absent, installed but not firing, firing without parameters, or blocked by consent or the browser, by running one test journey and comparing the browser capture, the merchant's logs and the order record for the same identifier.
Deliver a tracking diagnosis and a repeatable test plan stating which of those states applies to each event with the evidence and the correction, then confirm the corrected events against the order records for the window and note whether the gap also affects reporting for earlier periods.
Cover decision rules, frequent misunderstandings, and required inputs so the team aligns on prerequisites before execution.
The store configuration and the page source show whether the identifier is present at all, while a test run shows whether it sends. An identifier present in the configuration but sending nothing points at the template or the trigger condition; one absent from both points at installation. The two are recorded separately because they need different corrections.
Checkout steps are often served by scripts the theme does not control and can be replaced or reordered without any theme edit. Testing only the product page can therefore show a firing pixel while the purchase event never runs, which is exactly the pattern behind reports showing visits but no purchases.
Earlier periods can be restated only where the merchant holds an order record for each missing event and the reason for the gap is understood, and any reconstruction is labelled as such. Where neither exists, the period is reported as unmeasured; filling it with an estimate would make later comparisons impossible to interpret.
Start with scenarios under the same topic, then expand into cross-topic matches suggested by similarity.
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