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Step 1
Start with campaign history, spending limits, conversion lag, normal ranges and alert recipients.
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Spark playbook scenarios · Spark: AI Store Assistant
Detect unusual advertising spend patterns
An operator notices advertising problems only after spending has accumulated. In this case, the immediate task is to detect unusual advertising spend patterns. The workflow should produce an ad alert specification and example incidents, using the merchant's actual records and constraints.
Topic
Ad Performance Alerts
Target keyword
shopify ad spend anomaly detection
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 advertising spend patterns.
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 campaign history, spending limits, conversion lag, normal ranges and alert recipients]. Define thresholds using comparable history, include minimum evidence and persistence rules and attach a concrete response to each alert.
Return an ad alert specification and example incidents 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.
An operator notices advertising problems only after spending has accumulated. In this case, the immediate task is to detect unusual advertising spend patterns. The workflow should produce an ad alert specification and example incidents, 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
Define thresholds using comparable history, include minimum evidence and persistence rules and attach a concrete response to each alert.
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Step 3
Deliver an ad alert specification and example incidents; 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.
Why can a short-term ROAS drop be misleading?
Conversions may arrive later than spend, and small samples can fluctuate sharply.
How can alert noise be reduced?
Use minimum volume, persistence windows and clear recovery conditions rather than notifying on every small movement.
What information is needed to investigate this use case?
Start with campaign history, spending limits, conversion lag, normal ranges and alert recipients. 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.
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Continue with adjacent or same-topic scenarios
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Next step
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