Manually analyzing campaign reports, column by column, is one of the most repetitive tasks in performance marketing work. Using AI well for this doesn’t mean asking it to “analyze everything,” it means asking the right questions about specific data.
Ask for comparisons, not generic summaries
A prompt like “summarize this report” gives vague answers. A prompt like “compare the CPA of these campaigns against the previous period and tell me which ones got more than 15% worse” gives an actionable answer, because it defines the comparison criteria upfront.
Ask for hypotheses, not just observations
It’s useful to explicitly ask the AI to propose hypotheses about why a metric changed, not just describe the change. For example: “CTR dropped 20% in this campaign, give me 3 possible hypotheses about the cause, based on the available data.” This saves initial diagnostic time, even though the hypotheses still need validating with more data afterward.
Ask it to flag what’s missing, not just what’s there
A useful prompt is asking the AI to identify what information it would need to give a more precise answer, instead of forcing a conclusion from incomplete data. This avoids rushed conclusions based on a partial dataset.
Ask for the analysis in the format you’ll actually use
Asking directly for the result in its final format (a comparison table, 3 executive bullets, a short message to share with the client) saves the extra step of reformatting manually afterward.
The limit of this approach
AI can greatly speed up the first pass of analyzing a report, but the final decision on what to do with that information (pause a campaign, reallocate budget, change creatives) still needs the full business context, which is rarely all available in the prompt.
If you want help building a more efficient campaign analysis process, message me on WhatsApp.