Step 1
Collect the recorded count at every checkout step from entry through order confirmation for one period, the source and timestamp behind each count, and the device, region and payment method breakdown available per step.
The merchant knows orders are lost inside checkout but not which step loses them. In this case, the immediate task is to identify where shoppers exit the checkout process. The output should be a step-by-step drop-off view naming the largest exits and the device, region and payment conditions recorded at those steps.
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 identify where shoppers exit the checkout process.
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 recorded count at every checkout step for one period, the source and timestamp behind each count, and the device, region and payment method breakdown available per step].
Compare consecutive step counts to find the largest fall, confirm both counts come from the same source and period so a tracking gap is never read as an exit, then describe the device, region and payment conditions recorded at that step.
Return a checkout drop-off report and prioritized remediation tests verifying the stage counts 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 recorded count at every checkout step from entry through order confirmation for one period, the source and timestamp behind each count, and the device, region and payment method breakdown available per step.
Compare consecutive step counts to find the largest fall, confirm both counts come from the same source and period so a tracking gap is never read as an exit, then describe the device, region and payment conditions recorded at that step.
Deliver the step-by-step drop-off view naming the largest exits with their conditions, list the prioritized remediation tests tied to those steps, and reconcile the entry count and stage counts against the supplied records.
Cover decision rules, frequent misunderstandings, and required inputs so the team aligns on prerequisites before execution.
By subtracting each step's count from the step before it inside the same period, taking the largest difference as the exit point, then examining the conditions recorded at that step rather than the one after it.
Yes. A step counted by a different tool or over a different window looks exactly like a drop, so every pair compared must share its source and period before the difference is reported.
The same step can fail for one device or payment method alone, and without those attributes the report names a step but leaves no starting point for the test.
Start with scenarios under the same topic, then expand into cross-topic matches suggested by similarity.
Ciwi Spark builds Shopify-native AI tools and agents around the workflows merchants actually run: research, ads, translation, and customer support. Stop rewriting the same prompts by hand.