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Spark playbook scenarios · Spark: AI Store Assistant

Reconcile Facebook attribution with Shopify order evidence

Facebook reports conversions the store cannot always find in its orders, and the difference is never explained anywhere in the reports. 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 giving every gap its likely cause.

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: [campaign and ad set reports with their click and conversion identifiers, the configured attribution window and credit rule, and matching order records from the store export for the same window]. Match each attributed conversion to an order on the shared identifier and currency, treating an order as the authority for whether a sale happened, and write every unmatched row into the discrepancy log with its likely cause, whether a window boundary, a duplicate claim, a sale outside the store or an untracked purchase, instead of dropping it.
Return a unified reporting model and discrepancy log listing every gap with its likely cause 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.
Use in Spark
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.

01

Step 1

Collect the campaign and ad set reports, the click and conversion identifiers each row carries, the attribution window and credit rule configured on the account, and the matching order records from the store export for the same window.

02

Step 2

Match each attributed conversion to an order on the shared identifier and currency, treating an order as the authority for whether a sale happened, and write every unmatched row into the discrepancy log with its likely cause, whether a window boundary, a duplicate claim, a sale outside the store or an untracked purchase, instead of dropping it.

03

Step 3

Deliver the unified reporting model and discrepancy log listing every gap with its likely cause, then verify by reconciling matched conversions against the order export, confirming that the count of matched plus logged unmatched rows equals the reported conversion count and that no order is credited twice.

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 reported conversions outnumber store orders?

The platform credits any conversion it observed inside its own window, including sales that later changed or that never reached the store export. Reconcile on the shared order identifier and currency before treating either number as the count that actually happened.

What should happen to unmatched conversions?

Each one is written to the discrepancy log with its identifier and the reason it stayed unmatched, never dropped. Clusters of unmatched rows usually point at a tracking gap or a sale completed outside the store, both of which change how the channel is judged.

Is widening the attribution window enough?

It explains only the portion of the gap caused by timing, since credit rules and conversion tracking still differ. Re-run the match afterwards and compare the remaining unmatched rows with the previous log to see what the adjustment genuinely resolved.

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Next step

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