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Campaign analysis often begins with work that has nothing to do with analysis.
Exports arrive with different naming conventions, duplicate rows, missing values and metrics stored in the wrong format. By the time the data is usable, you have less time and energy for the decisions it was meant to support.
This is exactly the kind of chore AI should absorb. The process is rule-based, the changes can be logged and the cleaned result can be checked before anyone relies on it.
Try this prompt with your exported file:
Clean and standardize this campaign dataset so it is ready for analysis. Do not interpret campaign performance yet.
Please:
1. Preserve the original data in a separate tab or file.
2. Standardize dates, currency, percentages and campaign names.
3. Identify duplicate rows without deleting uncertain matches.
4. Flag missing, inconsistent or impossible values.
5. Keep calculated fields separate from source data.
6. Create a data-quality log listing every change, assumption and unresolved issue.
7. Return an editable cleaned file.
Before making changes, inspect the file and tell me which cleaning rules you plan to apply. Ask me about anything that requires business context. Wait for my approval before continuing.
The time you save should go into questioning the result: what changed, why it changed and what you will do differently because of it.
Happy prompting!
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