01
Step 1
Collect the sessions attributed to each campaign, the orders credited to those sessions under the attribution setting and window carried in the supplied data, the conversion definition, and the reporting period with its timezone.
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Spark playbook scenarios · Spark: AI Store Assistant
Report conversion rates for advertising traffic
A conversion rate is quoted per campaign, but the sessions, orders and conversion definition behind it were never written down together. In this case, the immediate task is to report conversion rates for advertising traffic. The workflow should return rates with sample sizes and caveats, reconciled against the merchant's order records.
Topic
Ad Performance Analytics
Target keyword
shopify ad conversion rate report
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 report conversion rates for advertising traffic.
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: [sessions attributed to each campaign, orders credited under the stated attribution setting and window, the conversion definition, and the reporting period with its timezone]. Compute each campaign's rate as credited orders divided by attributed sessions using that one denominator throughout, report any campaign whose session or order count sits below the merchant's stated minimum sample size with raw counts instead of a rate, and mark orders placed outside the attribution window as excluded rather than lost.
Return the report with rates, sample sizes and caveats, reconciled against the merchant's order records 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
Compute each campaign's rate as credited orders divided by attributed sessions using that one denominator throughout, report any campaign whose session or order count sits below the merchant's stated minimum sample size with raw counts instead of a rate, and mark orders placed outside the attribution window as excluded rather than lost.
03
Step 3
Deliver the report with rates, sample sizes and the caveat attached to each figure, then reconcile the total credited orders against the merchant's order export for the same window and explain every difference by reference to the attribution rule.
FAQ
Common questions when running this scenario
Cover decision rules, frequent misunderstandings, and required inputs so the team aligns on prerequisites before execution.
When is a campaign's rate too thin to publish?
When its session or order count falls below the merchant's stated minimum sample size, in which case the campaign is reported with raw counts and no rate. A rate over a handful of sessions moves with a single order and would be read as a real performance change.
How are orders placed after the window closed handled?
They are credited to the window in which the session occurred when the attribution setting does so, and excluded with a note when it does not. Mixing the two conventions inside one report makes the totals disagree with the order export and hides the reason.
Is the highest rate the best campaign?
Only once the sample size and the revenue behind the orders are shown, because a narrow campaign can post a high rate on very few sessions. The report ranks campaigns by credited orders and revenue alongside the rate so the comparison reflects volume as well as efficiency.
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