The team disagrees about why shoppers leave checkout, and none of the explanations offered so far is backed by the funnel data. In this case, the immediate task is to investigate evidence behind checkout abandonment. The output should be a drop-off report pairing each exit with the step records, errors and payment or shipping conditions that support it.
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 investigate evidence behind checkout abandonment.
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: [checkout step records with session identifier, timestamp, device and region, declined or failed payment outcomes, validation error messages, and shipping methods with quoted costs as shown at each step]. Classify an exit as evidenced only when a same-session error, decline code or unavailable option appears in the records immediately before it, mark every exit carrying no such signal as unattributed, and never carry one shopper's explained exit onto another shopper's unexplained one.
Return a checkout drop-off report and prioritized remediation tests confirming each claim 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.
Pull checkout step records, declined or failed payment outcomes, validation errors, and the shipping methods with their quoted costs as presented on the step where each shopper exited, keeping the session identifier, timestamp, device, region and payment or shipping option attached to every event.
Classify an exit as evidenced only when a same-session error, decline code or unavailable option appears in the records immediately before it, mark every exit carrying no such signal as unattributed, and never carry one shopper's explained exit onto another shopper's unexplained one.
Deliver a drop-off report that pairs each evidenced exit with the record supporting it and adds a ranked remediation backlog, reconcile the count of reported exits with the checkout session export, and list separately every explanation the evidence could not support.
Cover decision rules, frequent misunderstandings, and required inputs so the team aligns on prerequisites before execution.
An error, decline code or unavailable shipping or payment option recorded in the same session immediately before the shopper leaves. An explanation resting on nothing but team opinion goes into the unattributed list, so a reason never becomes a claim merely because it sounds plausible.
Use the last step carrying a recorded event in that session, because jumping ahead is itself a signal. Assuming the shopper passed through every intermediate step attributes exits to stages the records never show, which sends remediation at the wrong part of the funnel.
Each line carries the record that supports it, and any exit without a qualifying signal is reported as unattributed rather than distributed across the known reasons. Attributed and unexplained counts therefore always sum to the total exits in the session export.
Start with scenarios under the same topic, then expand into cross-topic matches suggested by similarity.
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