Make sense of conflicting attribution data across channels
A clear explanation of why your ad platforms, analytics, and CRM disagree, and a practical way to report credit.
The prompt
Help me reconcile conflicting attribution numbers across my marketing channels.
Sources and what each reports: [e.g. "Meta says 420 purchases, Google Ads says 380, GA4 says 510 total, Shopify says 600"]
Attribution settings in each: [windows, view-through on/off, model - if known]
Channels I run: [LIST with monthly spend]
Sales cycle and buying journey: [e.g. "one-session impulse buy" or "6-month B2B with several touches"]
Decision I need to make with this data: [e.g. "where to move 20% of budget"]
Explain:
1. Why these numbers differ: double counting across platforms, view-through credit, different windows, cross-device gaps, consent and cookie loss, offline or sales-assisted conversions.
2. Which source to treat as the "total" and which as directional.
3. What each channel is likely over- or under-credited for, given my journey.
Then recommend:
- A simple reporting approach I can maintain (e.g. one source of truth for totals plus platform data for in-channel optimization, or blended CAC/MER).
- Whether my situation warrants self-reported attribution ("How did you hear about us?"), incrementality tests, or media mix modeling, and the minimum spend/data each needs.
- A one-paragraph explanation I can give my leadership team.
Do not invent conversion figures. If my inputs are missing a setting, say what to check.
Highlighted [brackets] are blanks to replace with your own details.
Fill in the blanks and use this promptMore Data & Analysis prompts
What to fill in
- windows, view-through on/off, model - if known
- LIST with monthly spend
- e.g. "where to move 20% of budget"
How this prompt is built
This prompt follows the Few-Shot structure (Few-shot prompting). A few-shot prompt includes one or more worked examples of the input and the output you want, so the model can copy the pattern instead of guessing it.
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