# 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.

- Topic: Data & Analysis
- Framework: Few-Shot
- Level: Standard
- Tags: Explain & Teach, Fill-in Template
- Source: https://www.thinkwithprompt.com/prompts/make-sense-of-conflicting-attribution-data-across-channels

## Prompt

```text
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.
```

## What to fill in

- windows, view-through on/off, model - if known
- LIST with monthly spend
- e.g. "where to move 20% of budget"

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