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Spark playbook scenarios · Spark: AI Store Assistant

Compare attribution across multiple customer touchpoints

Every platform claims credit for the same sale under its own rules, and no record shows where those claims conflict. In this case, the immediate task is to compare attribution across multiple customer touchpoints. The workflow should produce a unified reporting model and discrepancy log showing where credit assignments disagree.

Topic
Analytics Integrations and Unified Reporting
Target keyword
shopify multi touch 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 compare attribution across multiple customer touchpoints.
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: [touchpoint records with timestamps, session ordering, store-side conversion timestamps, and each platform's stated attribution rule with its window and credit model].

Compare how each platform credits the same conversion by replaying the touchpoint sequence under every stated rule, documenting each model's output side by side rather than adopting one silently, and marking any order whose credited channels differ between models as a disputed assignment.
Return a unified reporting model and discrepancy log showing where credit assignments disagree 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 touchpoint records with their timestamps, the session ordering that puts them in sequence, the conversion timestamps on the store side, and each platform's stated attribution rule with the window and credit model it applies.

02

Step 2

Compare how each platform credits the same conversion by replaying the touchpoint sequence under every stated rule, documenting each model's output side by side rather than adopting one silently, and marking any order whose credited channels differ between models as a disputed assignment.

03

Step 3

Deliver the unified reporting model and discrepancy log showing where credit assignments disagree, then verify that the sum of credited conversions per model equals that model's own reported total, that every disputed order is listed with the channels involved and that each model is named wherever its figures appear.

FAQ

Common questions when running this scenario

Cover decision rules, frequent misunderstandings, and required inputs so the team aligns on prerequisites before execution.

Which attribution model should be adopted?

None universally. Keep every model in use, state which one each figure comes from, and compare outputs on the same orders. Choosing one without saying so turns a modelling decision into an invisible assumption sitting behind budget decisions.

Can one order be credited twice?

Yes, because each platform credits only what it can observe, so the same purchase appears in several reports. Overlap is reported as overlap, and only records tied to an order identifier are treated as reconciled demand, never as a sum of channel claims.

What records start this comparison?

Touchpoints with timestamps, the session ordering that sequences them, store-side conversion timestamps, and each platform's stated attribution rule, all held for one consistent period. Without the stated rules, a difference cannot be attributed to any model.

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Next step

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