01
Step 1
Collect each platform's reported conversions with their timestamps and order identifiers where present, the matching store orders, and the attribution window and model in force.
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Spark playbook scenarios · Spark: AI Store Assistant
Reconcile marketing attribution with store outcomes
Each platform reports its own conversions, and several of them claim the same order as their own win. In this case, the immediate task is to reconcile marketing attribution with store outcomes. The workflow should produce a record-level reconciliation showing matched orders, overlaps and unmatched claims against the merchant's own data.
Topic
Marketing Attribution and ROAS
Target keyword
shopify marketing attribution tool
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 reconcile marketing attribution with store outcomes.
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: [platform-reported conversions with timestamps and order identifiers, the matching store orders, and the attribution window and model in force]. Match platform claims to store orders on order identifier, timestamp and currency, then separate the rows into matched, overlapping where several platforms claim one order, and unmatched where no store order supports the claim, rather than netting the totals.
Return the reconciliation and channel return report 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
Match platform claims to store orders on order identifier, timestamp and currency, then separate the rows into matched, overlapping where several platforms claim one order, and unmatched where no store order supports the claim, rather than netting the totals.
03
Step 3
Deliver a record-level reconciliation with a channel return view built only on matched rows, then reconcile the matched total against the order export and list every unmatched claim with its platform and period.
FAQ
Common questions when running this scenario
Cover decision rules, frequent misunderstandings, and required inputs so the team aligns on prerequisites before execution.
Why does attributed revenue exceed store sales?
Overlap is the usual cause, because platforms credit the same order under different windows. The reconciliation keeps the overlaps visible instead of averaging them away, so the return figure counts each order once while the claim count stays auditable.
Is one attribution model correct?
No model is universally right, so pick one, apply it to every channel in the report, and record the choice with the results. The comparison stays valid across periods only while the model and window stay unchanged.
What happens to orders with no platform claim?
Keep them in a separate unattributed group. They are real revenue the platforms did not credit, and excluding them would make the return figure look better than the business actually performed.
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.
Analyze orders attributed to marketing campaigns
Calculate advertising return using stated cost and value definitions
Connect store sales with available attribution evidence
Compare marketing channel outcomes and tradeoffs
Compare channel ROAS using compatible definitions
Track attributed revenue relative to ad spend
Next step
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