Measure what
moves the business.
A campaign can look right and still miss its goal. Give your AI marketing team a clear definition of success, then use the work and the results to decide what comes next.
This page describes an evaluation method, not published benchmark results. We do not claim independently verified accuracy, customer ROI, certification or provider-wide availability. Product illustrations and simulated audiences are not observed customer outcomes.
Start with a question worth answering.
Write down the audience, offer, channel, budget and desired outcome before creating the campaign. Are you looking for qualified enquiries, workshop registrations or repeat purchases? Choose the question first, then the metric.
Keep the approved brief and its source material together. Use only data you have permission to process.
Review the work against the brief.
- Accuracy
- Can you support the factual claims, offer and product details?
- Brand
- Does the language and creative feel like your business?
- Relevance
- Does the message give this audience a reason to care?
- Next step
- Is the call to action clear, useful and connected to the right destination?
- Permission
- Are the audience, content rights and channel permissions appropriate?
AI review can help identify issues. Your judgment remains important for factual accuracy, brand fit and legal obligations.
Separate activity from outcomes.
A draft is a piece of work. A published campaign is an action. A registration or sale is an outcome. Track them separately so a completed task doesn't become a claim about business impact.
Confirm publication and delivery through your connected channels. Review the relevant measurement window and account for missing or delayed data.
Compare fairly.
When comparing creative approaches or workflows, use the same brief and criteria. Account for review time, subscription and usage fees, channel spend and revisions. Include the work that did not succeed, not just the strongest example.
If you're measuring incremental impact, use a suitable comparison group and a consistent measurement window. Correlation alone does not establish that a campaign caused a result. Where evidence is incomplete, treat the result as inconclusive.
Keep the evidence reproducible.
Record the source account, time window, sample size, missing observations, costs and model or workflow version. Distinguish provider acceptance from confirmed delivery, attributed revenue from incremental revenue, and synthetic predictions from observed results. Report uncertainty and failed runs. Missing evidence is unknown, not zero; a model confidence score is not a verified probability of business success.
Bring the lesson into the next brief.
Record what you observed, what remains uncertain and what you'd change. Keep customer information private and obtain permission before sharing customer identities or results publicly.
The useful outcome is not a perfect score. It's a better-informed next decision.