01
Step 1
Review recent spend, results and remaining headroom per campaign over the merchant's evaluation window, together with each campaign's cap, the date of its launch or last structural change and the conversion delay those results carry.
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Spark playbook scenarios · Spark: AI Store Assistant
Identify campaigns that may justify a bounded budget test
More budget should follow what works, but per-campaign spend, results and limits have to show which campaigns can absorb an increase. In this case, the immediate task is to identify campaigns that may justify a bounded budget test. The workflow should return a shortlist with the proposed increase, test window and stop condition.
Topic
Ad Optimization and Budget Management
Target keyword
ecommerce ad scaling 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 campaigns that may justify a bounded budget test.
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: [recent spend, results and remaining headroom per campaign over the evaluation window, each campaign's cap, its launch or last structural change date, and the conversion delay behind the results]. Shortlist only campaigns whose result holds across every period inside the window, that still have headroom under the stated cap and that have had no structural change inside the window, and write for each a bounded increase with a test window and a stop condition.
Return a shortlist with the proposed increase, the test window and the stop condition 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.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
Shortlist only campaigns whose result holds across every period inside the window, that still have headroom under the stated cap and that have had no structural change inside the window, and write for each a bounded increase with a test window and a stop condition.
03
Step 3
Deliver the shortlist with the proposed increase, the test window, the stop condition and the figure that would trigger it for each campaign, then verify every entry against the spend export and confirm that the proposed increase keeps the campaign inside its cap.
FAQ
Common questions when running this scenario
Cover decision rules, frequent misunderstandings, and required inputs so the team aligns on prerequisites before execution.
What disqualifies a campaign from a scaling test?
A structural change inside the window, results that hold for only part of it, or no headroom under the cap, because any of those means the increase would be tested on a campaign the records do not yet describe.
How is the stop condition written?
As a figure checked at a fixed point in the test window, such as the objective metric falling below the campaign's own pre-test level, so the increase is reversed on evidence rather than on how the spend felt.
Should the increase be applied in one change?
No, in bounded steps with a check between them, since a single jump changes delivery conditions enough that the pre-test result no longer predicts the enlarged campaign and the test stops being a fair comparison.
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