As of 13 August 2026, AI can analyse your marketing campaign results.

What to actually do

1. Hand it to a person

   The route this page recommends

   A person who owns the outcome does this end to end, worth it when the failure is dear.

2. Use a tool built for this

   Second choice

3. Do it yourself

   The distant third

   A chat interface, chat-fluent skill, and roughly 30 minutes until you can act on the result.

   How to actually do it

   1. Open the advertising platforms, analytics system and sales or CRM report that cover the same reporting period, then export the campaign results with campaign names, dates, spend, impressions, clicks, conversions and revenue where available.
   2. Write down the business objective, conversion definition, attribution method, currency, reporting period, target or benchmark and any tracking changes made during the period.
   3. Remove passwords, customer names, email addresses, telephone numbers and other unnecessary personal data from the exports, then combine the relevant tables into one spreadsheet with clear column headings.
   4. Paste the context and spreadsheet into the prompt, or upload the file to Julius AI, and ask it to complete the data-quality check before analysing performance.
   5. Check every reported total and calculated measure against the original platform exports, including spend, conversions, revenue, click-through rate, conversion rate, cost per conversion and return on ad spend.
   6. Compare the proposed findings with your tracking setup, attribution rules, campaign changes and sales records, then mark which conclusions are observations, assumptions or unverified explanations.
   7. Send the final analysis to the colleague responsible for marketing measurement before changing targeting, pausing campaigns or moving budget.

   Prompt

   Analyse the marketing campaign data below. Use only the figures and definitions I provide, and do not invent missing values. First check the data for duplicate rows, missing values, inconsistent date ranges, currency differences, tracking gaps and mismatched conversion definitions. State every problem before drawing conclusions.
   
   Business objective: [for example, generate qualified leads, increase sales or retain customers]
   Reporting period: [start and end dates]
   Channels and campaign names: [paste the list]
   Definitions: [define impressions, reach, clicks, leads, conversions, revenue and any other measures]
   Attribution method used by the source platform: [paste or state unknown]
   Target or benchmark: [paste or state none]
   Campaign data: [paste a table or attach a spreadsheet export]
   
   Produce:
   1. A short data-quality report.
   2. A table for each campaign and channel showing spend, impressions, clicks, click-through rate, conversions, conversion rate, cost per click, cost per conversion and revenue or return on ad spend where the required inputs exist. Label any measure that cannot be calculated.
   3. Charts or a clear description of the most useful charts.
   4. Comparisons against the stated target or benchmark, without creating a benchmark if none is supplied.
   5. The strongest patterns supported by the data, separating observed correlation from claims about cause.
   6. Three practical actions, each tied to a specific finding and with its main risk or assumption.
   7. A list of figures, tracking details and business context that I must verify before changing spend.
   
   Show the formula used for each calculated measure, preserve the source figures, flag uncertainty, and do not recommend a budget change solely because one campaign has a higher reported return.

   Open it prefilled in ChatGPT or Claude, or copy it into Gemini, which takes no prefill link.