An AI ad-research workflow that ends with a test

Start your AI ad research with a decision: which objection should the next ad address, which message needs testing, or where are enquiries dropping off?

Here is a six-step workflow for turning campaign records and customer feedback into your next ad test.

1. Choose one decision

Start with a question narrow enough to answer. For example: which customer objection should the next ad address?

That is more useful than asking AI to analyse your marketing. It gives the research a boundary and makes the output easier to assess. You need evidence about objections and responses, rather than a report about every aspect of the business.

Write down the offer, the audience and the action you want someone to take. Keep those details attached to the question throughout the review.

2. Gather the records that answer it

For the objection question, useful inputs might include approved sales-call notes, recurring questions from enquiries, ad comments and the messages used in recent creatives. Add the campaign outcomes you can reliably connect to those creatives.

Give every record a source identifier and date. Keep the original wording where it matters. A customer's specific question is more useful than a vague label such as “price concern”.

Remove unnecessary personal information before processing the material, and use tools approved for that data. You do not need someone's name or email address to study a recurring objection.

3. Keep observations separate from explanations

Ask AI to organise the material into a table with five fields:

• Source: where the observation came from.
• Observation: what the record actually says or measures.
• Possible explanation: an interpretation to investigate.
• Missing information: what prevents a stronger conclusion.
• Test idea: a change the team could evaluate.

For example, several enquiries might ask whether installation is included. That supports testing a clearer explanation of the offer. It does not prove that unclear installation terms are the main reason people fail to buy.

Require the output to point back to the source record. If a statement has no supporting record, it should be labelled as a suggestion or removed.

A prompt to start with

Replace the bracketed fields and include the source IDs with your records.

We sell [offer] to [audience]. Our next decision is [one question]. Review the attached records. For each useful finding, return the source ID, exact observation, possible explanation, missing evidence and one test idea. Keep observations separate from suggestions. Do not infer customer intent from clicks alone. If records conflict, show the conflict. End with three questions a person should resolve before choosing a test.

4. Review patterns in their context

A pattern is a starting point for a question. Check whether it comes from the audience, campaign and time period you are actually working on.

Do not combine unrelated services or different definitions of a qualified lead into a single winner ranking. Separate them first. If sales outcomes are incomplete, say so instead of treating missing outcomes as failures.

Then review a sample of the source records yourself. Does the summary preserve what people meant? Are repeated copies of the same comment being mistaken for several independent observations? Has a tentative remark become a confident conclusion?

Those checks are part of the research. They are not something to leave until the ad is already running.

5. Turn one finding into a small creative brief

Choose a finding worth testing and write a brief with these fields: audience, objection, message, supporting proof, proposed creative change and intended response.

Example brief: “For people comparing installation options, explain what is included in the quoted service. Keep the offer unchanged. Test the clearer explanation against the existing message.”

The brief connects a specific customer question to one creative change.

6. Feed the outcome into the next review

Save the brief alongside the version of the creative that ran. When results are available, record what happened, what you still cannot explain and what you want to test next.

Ask AI to help organise that comparison. Keep a person responsible for deciding whether the evidence supports the conclusion. Changes in targeting, offer or follow-up can affect the outcome too.

The result should be a usable record of decisions and evidence. Over time, that gives the next research session more context than a folder full of disconnected reports.

Start with one question, one set of records and one test. If you need help choosing where to begin, use the marketing diagnostic.