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Step 1
Start with product and variant IDs, sales, traffic, conversion data, availability and comparison periods.
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Spark playbook scenarios · Spark: AI Store Assistant
Detect unusual product performance changes
A merchant cannot identify which products are driving revenue or underperforming after accounting for exposure. In this case, the immediate task is to detect unusual product performance changes. The workflow should produce a product performance report with plausible drivers and next tests, using the merchant's actual records and constraints.
Topic
Product Performance Analytics
Target keyword
shopify product performance 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 detect unusual product performance changes.
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 product and variant IDs, sales, traffic, conversion data, availability and comparison periods].
Compare like-for-like products and time windows, separate low demand from low exposure or stock constraints and rank actionable findings.
Return a product performance report with plausible drivers and next tests 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 cannot identify which products are driving revenue or underperforming after accounting for exposure. In this case, the immediate task is to detect unusual product performance changes. The workflow should produce a product performance report with plausible drivers and next tests, 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
Compare like-for-like products and time windows, separate low demand from low exposure or stock constraints and rank actionable findings.
03
Step 3
Deliver a product performance report with plausible drivers and next tests; 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 low sales volume prove a product is poor?
No. Limited traffic, low availability or a recent launch may explain the result.
Why compare products within a relevant group?
Price, seasonality and buyer intent differ across categories, so an overall ranking may hide meaningful context.
What information is needed to investigate this use case?
Start with product and variant IDs, sales, traffic, conversion data, availability and comparison periods. 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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