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Step 1
Start with orders, refund events and amounts, reason codes, product IDs and matched time windows.
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Spark playbook scenarios · Spark: AI Store Assistant
Identify products with concentrated refund activity
Refunds are increasing or concentrating in certain products, but the source of the change is unclear. In this case, the immediate task is to identify products with concentrated refund activity. The workflow should produce a refund breakdown and evidence-based action list, using the merchant's actual records and constraints.
Topic
Refund Analysis and Alerts
Target keyword
shopify product refund alerts
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 identify products with concentrated refund activity.
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 orders, refund events and amounts, reason codes, product IDs and matched time windows]. Separate counts from amounts, account for refund timing and identify recurring product or reason patterns before proposing alerts or fixes.
Return a refund breakdown and evidence-based action list 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.
Refunds are increasing or concentrating in certain products, but the source of the change is unclear. In this case, the immediate task is to identify products with concentrated refund activity. The workflow should produce a refund breakdown and evidence-based action list, 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
Separate counts from amounts, account for refund timing and identify recurring product or reason patterns before proposing alerts or fixes.
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Step 3
Deliver a refund breakdown and evidence-based action list; 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.
Is refund rate based on orders or revenue?
Either measure can be useful, but they answer different questions. Label the chosen denominator explicitly.
Why do recent sales and refunds not always align?
A refund recorded today may relate to an older order. Timing can distort a simple same-day comparison.
What information is needed to investigate this use case?
Start with orders, refund events and amounts, reason codes, product IDs and matched time windows. 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
Start with scenarios under the same topic, then expand into cross-topic matches suggested by similarity.
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Summarize recurring refund reasons
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Compare refund rates by product with consistent denominators
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Identify underperforming products and plausible constraints
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Detect unusual product performance changes
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Next step
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