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Step 1
Start with source exports or authorized connections, field dictionaries, timestamps, currencies and join keys.
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Spark playbook scenarios · Spark: AI Store Assistant
Reconcile Facebook attribution with Shopify order evidence
Data from the store and marketing platforms uses different identifiers, timezones or definitions. In this case, the immediate task is to reconcile Facebook attribution with Shopify order evidence. The workflow should produce a unified reporting model and discrepancy log, using the merchant's actual records and constraints.
Topic
Analytics Integrations and Unified Reporting
Target keyword
shopify facebook ads attribution
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 Facebook attribution with Shopify order evidence.
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 source exports or authorized connections, field dictionaries, timestamps, currencies and join keys]. Map sources, harmonize compatible fields, preserve source-specific metrics and reconcile mismatches rather than forcing all totals to agree.
Return a unified reporting model and discrepancy log 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.
Data from the store and marketing platforms uses different identifiers, timezones or definitions. In this case, the immediate task is to reconcile Facebook attribution with Shopify order evidence. The workflow should produce a unified reporting model and discrepancy log, 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
Map sources, harmonize compatible fields, preserve source-specific metrics and reconcile mismatches rather than forcing all totals to agree.
03
Step 3
Deliver a unified reporting model and discrepancy log; 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 do connected platforms still report different numbers?
Integration moves data but does not make attribution, timing or metric definitions identical.
Should unmatched records be discarded?
No. Keep them visible with a reason so missing joins do not silently bias the report.
What information is needed to investigate this use case?
Start with source exports or authorized connections, field dictionaries, timestamps, currencies and join keys. 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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