Structured Data That AI Search Engines Actually Read

Insist on the raw answers. If a report cannot be disagreed with, it is not a report. This single requirement filters out most of the weak offerings in the market without needing any technical knowledge.

This is why marketplace listings, review sites and roundups dominate product citations while brand product pages appear less often. It is also why a product page that states what it is worse at is unusually valuable, since it can be quoted as an impartial constraint rather than a claim.

Screenshots of favourable answers with no run count, which say nothing about how many attempts produced them. Impressions or traffic from unrelated channels included to fill a report. And activity described in the language of effort, such as ongoing optimisation, with no countable output attached.

Distinguish between a supplier who is failing and one who is reporting badly, because the remedies differ entirely. Ask for the raw answers and read them yourself before deciding. It is not unusual to find that sound work has been buried under a dashboard nobody understands, and fixing the reporting is far cheaper and less disruptive than replacing a team that is actually doing the job.

The guard against this is boring and effective. Change one substantial thing at a time where you can, record what you did and when, and note the alternative explanations alongside your conclusion. Attribution in this channel is genuinely hard, and a team that admits that will make better decisions than one that produces a confident causal story after every movement.

Testing too rarely means you find out about a problem a quarter after it started. Testing too often means drowning in variance that looks like signal and reacting to noise. Both failures are common and the second is more expensive, because it produces work.

Retrieval behaviour changes, competitors keep publishing, listings go stale, product details change and reviews accumulate. A position secured once is not held without maintenance, which is the same lesson search taught over twenty years and which is being relearned rather than transferred.

If you must change the prompt set, add new prompts as a separate cohort and keep the original series running unchanged. Editing the instrument retrospectively destroys the comparison you have been building.

The gap is usually stark. Their page states a turnaround time, a coverage area, a price range and a limitation. Yours describes a commitment to quality and a passion for service. Only one of those contains anything to attach a citation to. llm seo

Before leaving, make sure you take the prompt set, the baseline archive and everything published. If those were not yours under the contract, that is a lesson for the next agreement rather than something to negotiate at the exit.

That means the useful ask is not simply for a rating. Prompting customers to say what they used the product for and what situation it suited produces review text that can actually answer a question, which is what gets quoted.

The result is a content programme aimed at guesses. Sometimes it works by accident. Usually it produces pages nobody retrieves, and the diagnosis that would have directed the effort correctly costs a fraction of what the content did.

One scheduling detail improves comparability more than it should. Run on roughly the same date each month rather than whenever somebody remembers. Retrieval behaviour and the freshness of competing sources both vary over a month, and a series taken at irregular intervals introduces variation that looks like a trend.

When to Test More Often Three situations justify a tighter loop. During an active campaign where you need to attribute a specific change, weekly runs on a subset of prompts are reasonable, provided you accept the variance.

The Types That Rarely Earn Their Keep Elaborate breadcrumb hierarchies, speakable markup, deeply nested item lists and most of the specialised types outside their intended vertical produce little observable difference in how a brand is understood or recommended.

The distinction to draw is between flat results with the inputs completed, and flat results with the inputs missing. The first is a category or timing problem and may be worth persisting with. The second is a delivery problem.

That matters most for the facts that establish identity, because those are the facts that let scattered mentions of you resolve into one record. It matters far less for content, where the model is going to read the prose anyway and is reasonably good at it.

In most categories the pages that generate answers are review platforms, directories, forum threads, comparison articles and trade publications. Being absent or wrong on those explains far more absences than anything on a brand's own site, and correcting a listing costs an afternoon.

The specific damage is that somebody sees a dip, rewrites a page, sees the number recover for unrelated reasons, and concludes the rewrite worked. That false lesson then gets applied elsewhere. A slower cadence with more runs per prompt is more informative than a faster one with fewer.