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Step 1
Start with relevant metrics, comparison periods, segments, recent changes and source definitions.
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Spark playbook scenarios · Spark: AI Store Assistant
Investigate plausible root causes of an observed anomaly
A merchant sees an unusual result and needs help connecting evidence to a plausible explanation. In this case, the immediate task is to investigate plausible root causes of an observed anomaly. The workflow should produce a diagnostic report with evidence and next checks, using the merchant's actual records and constraints.
Topic
AI Analytics and Diagnosis
Target keyword
shopify root cause analysis
Copyable AI prompt
A prompt you can drop directly into Spark
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.
ai-prompt.txt
Act as a Shopify operations specialist.
Help me investigate plausible root causes of an observed anomaly.
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: [provide relevant metrics, comparison periods, segments, recent changes and source definitions].
Check data quality, isolate where the change occurs, test alternative explanations and label hypotheses separately from observed facts.
Return a diagnostic report with evidence and next checks 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.Scenario & problem
When does this scenario usually come up?
Start with the business context, the trigger, and the expected output before moving into the execution steps.
A merchant sees an unusual result and needs help connecting evidence to a plausible explanation. In this case, the immediate task is to investigate plausible root causes of an observed anomaly. The workflow should produce a diagnostic report with evidence and next checks, using the merchant's actual records and constraints.
How to solve
A recommended resolution workflow
Split the work into input, judgment, and deliverable phases. Verify each step's output before moving to the next.
02
Step 2
Check data quality, isolate where the change occurs, test alternative explanations and label hypotheses separately from observed facts.
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Step 3
Deliver a diagnostic report with evidence and next checks; verify the result against the supplied records and identify unresolved inputs.
FAQ
Common questions when running this scenario
Cover decision rules, frequent misunderstandings, and required inputs so the team aligns on prerequisites before execution.
Does correlation establish the cause of a change?
No. Related movements can have other explanations. Use further checks or experiments to strengthen causal claims.
Why inspect segments before proposing a fix?
An aggregate change can be caused by a shift in mix rather than a problem affecting every customer or product.
What information is needed to investigate this use case?
Start with relevant metrics, comparison periods, segments, recent changes and source definitions. These inputs establish the relevant scope and help separate an actual issue from missing information or an unsuitable comparison. Without them, the result should remain a proposed approach rather than a confirmed diagnosis.
Related scenarios
Continue with adjacent or same-topic scenarios
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Next step
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