I used to be quite sure about what a landing page should look like. A certain structure, a clear message and a design that made the next step obvious.
Then I worked with Dario. Their page was long, and I did not expect it to work as well as it did. People connected with the story and trusted the people sharing their experience of the product. The page gave them space to understand it.
That experience changed my thinking. I had been judging the page through my own rules before understanding what the customer needed. It is a lesson worth taking into AI marketing, where producing another version can be much easier than establishing whether it is better.
What does a better marketing result mean?
To measure AI marketing ROI, compare the additional business value attributable to the change with its full additional cost. Clicks, output volume and time saved can help explain the result, but none establishes profitability by itself.
Start by naming the outcome. For a service business, that might be qualified enquiries that become paying customers. For a subscription product, it might be customers who activate and remain subscribed. Make the definition specific enough that two people on the team would count it the same way.
Then choose the earlier signals that help you understand that outcome: click-through rate, enquiry quality, the proportion who book, or the proportion who finish onboarding. A change at one stage may be cancelled out later.
Read what an AI study actually compared
A large Facebook experiment covering nearly 35,000 advertisers reported a 6.7% increase in click-through rates for AdLlama, a model used to generate advertising text variations. Its comparison was another AI model. That is useful evidence about a particular system improvement; it is not a finding that AI beats every human marketer or increases profit by 6.7%. Read the AdLlama research.
The same care belongs in your own reporting. If one campaign reaches a different audience, has a different budget and runs during a promotion, its results do not isolate the effect of the AI-generated creative. Several things changed at once.
Keep the test small enough to understand
Choose one question, such as whether a new explanation of the offer brings more qualified enquiries. Where the platform permits it, use a randomised split with comparable audiences, budgets and timing. Decide what you will measure before viewing the result.
Give customers enough time to complete the outcome you care about. A business with a long sales cycle cannot judge acquired-customer performance from two days of clicks. With small volumes, treat early results as clues rather than declaring a winner.
Record production, review and implementation time as well as software and media costs. Count the cost of correcting mistakes. Capacity released for other work can be valuable, but it is different from cash that actually leaves the cost base.
A simple example of AI marketing ROI
The following numbers are illustrative, not a client result. Suppose a controlled test supports an estimate of ten additional customers, each contributing €100 after the relevant delivery costs. That is €1,000 in additional contribution. If the extra software, creative review and campaign costs total €400, the estimated net gain is €600.
The return on those additional costs is (€1,000 − €400) ÷ €400 × 100 = 150%. This calculation depends on the incremental-customer estimate being credible and on using consistent costs and time periods. Refunds, cancellations or later delivery costs could change it.
Where the evidence cannot support attribution, call the result an estimate. Do not turn every sale after a tool was introduced into a sale caused by that tool.
Follow the customer after the conversion
My work across acquisition and retention is part of why I look beyond the first response. A campaign can bring in people who are interested enough to click but poorly matched to what the business provides. The question then becomes whether the message attracted the right expectation.
At Dario, I learned that my preference for a page mattered less than the customer’s reasons for trusting it. With AI-generated marketing, the same discipline applies: keep looking at what people actually do, including what happens after they buy.
