Start with a customer problem and real evidence. Use AI to accelerate a test whose outcome you can measure.
Start with evidence before copy
AI can draft many versions of a campaign, but volume does not establish that customers want the offer. Begin with approved customer interviews, support themes, sales questions, search queries, and actual performance data.
Ask for recurring problems and unanswered questions. Preserve the difference between a customer quote, your interpretation, and an AI-generated hypothesis. Do not turn a fictional persona into a claim about your audience or publish invented testimonials.
Build a brief that constrains the draft
Define audience, problem, offer, proof, channel, next action, and constraints. For example, a fictional bookkeeping studio might target independent designers who struggle to prepare monthly records. Its offer is a paid setup session; its proof must come from real experience or documented work, not an invented success rate.
Ask AI to propose distinct message directions: less administrative confusion, easier preparation, or clearer monthly routines. Evaluate whether each direction reflects evidence and what the offer actually delivers.
Create a coherent small campaign
Choose one message direction and draft a landing-page headline, a short email, and two ad concepts. Give each asset the same offer and next step while adapting the language to the channel.
Use AI to find mismatches: an ad promising a free template when the page sells a consultation, an email mentioning a deadline absent from the page, or a headline making an unsupported outcome claim. Review the drafts for accessibility, clarity, brand voice, and accuracy.
Measure the behavior that matters
Choose one primary outcome before launch. For a lead campaign, that might be qualified inquiries divided by eligible landing-page visits. Define a qualified inquiry in advance, using criteria your team can apply consistently.
In a fictional comparison, version A receives ten inquiries from two hundred visits, while version B receives fourteen from two hundred. Those counts alone do not establish which version is better: examine qualification, tracking, audience differences, and whether the sample supports a reliable conclusion.
Ask AI to help write the measurement plan, but verify calculations independently. Do not let it describe a small, noisy difference as a proven lift.
Use results to choose the next question
After a test, provide the real numbers and contextual changes. Ask what the evidence supports, what remains uncertain, and which next test would reduce that uncertainty. Include lead quality and downstream outcomes where available.
A useful marketing workflow ends with a decision: keep the message, revise the offer, investigate a drop-off, or gather more evidence. It does not end with a folder full of content nobody has evaluated.
Prompts to put it into practice
Replace the brackets with your context. Use information you have permission to share.
Evidence-backed message ideas
Using only these approved customer notes, identify recurring problems and supporting excerpts. Separate direct evidence from hypotheses. Propose three message directions, the proof each requires, and any claims we should avoid. Notes: [notes].
Plan a useful test
Create a small test plan for [offer] aimed at [audience]. State the hypothesis, one changed variable, primary metric, qualification criteria, tracking checks, and reasons we might be unable to draw a conclusion.
Try it on your work
Choose one existing marketing asset. Rewrite its brief using real evidence, generate two alternatives, and define the result that would justify keeping a change.
Check the result
- Every public claim has support.
- The landing page delivers what the message promises.
- The test has a defined outcome and acknowledges uncertainty.
