Local Businesses And The AI Recommendation Problem

Watch the quality of enquiries as well as the count. A common early signal is that conversations start further along, with the prospect already aware of your price band, your typical timeline and what you do not do, because a machine told them before they arrived. That shows up in sales cycle length and in fewer wasted calls long before it shows up in any dashboard.

Assume the pitch is good. Everyone's pitch is good, and the vocabulary in this field is easy enough that a competent salesperson can hold a convincing conversation without anyone behind them who can do the work.

Absence is not disqualifying on its own, since their category is crowded and they may serve a niche. But they should have an interesting answer, and the answer should not be defensive. A practitioner who has run this test on themselves will have thought about it and will tell you what they found.

Refusals matter. A report that only contains successes is either describing a suspiciously easy month or omitting the parts that did not work, and the omitted parts are usually where the useful information is.

What Padding Looks Like Screenshots of favourable answers with no indication of how many runs produced them. Industry news summaries that could have been written without opening your account. A rising score with no methodology. Traffic charts from unrelated channels included to fill space.

Ask specifically who checks factual accuracy before publication and what happens when the writer does not know the answer. A process that has no step for asking you is a process that will eventually publish something untrue about your business.

Service and Area Pages, Done Honestly The standard local play is a page per service and a page per town, and it fails when those pages are templated with a place name swapped in. Thin, near duplicate pages are treated as low quality and rarely provide anything worth quoting.

That is a month of intermittent effort, it costs almost nothing, and in most local categories it is enough to change what an assistant says. Local is one of the few places where the whole discipline is genuinely accessible without an agency. how to get recommended by AI assistants

Ask Who Writes and Who Reviews Find out whether the writing is done by somebody with subject knowledge or generated and lightly edited. Both happen, and the second is not automatically disqualifying, but you need to know because you are the one who carries the liability for inaccurate claims about your own products.

You are unlikely to read all of it, and its presence changes the incentives entirely. An agency that knows the raw evidence ships with the report writes a different summary than one that knows it will not be checked.

Being named in answers to prompts with buying intent, as opposed to definitional prompts nobody purchases from. Being described accurately, since a confident recommendation containing a wrong price or a service you discontinued costs more than absence. And being cited on the third party sources that appear repeatedly in your category's answers.

The answer you want describes a baseline: a prompt set built from how your customers actually speak, run across the assistants that matter, with raw answers and cited sources recorded. Everything after that should be justified by reference to what the baseline showed.

These names go directly into the prompt set and into any comparison content, and getting them wrong sends the entire measurement effort in the wrong direction. If you lose to a low cost regional operator rather than to the market leader, say so.

Results Split by Intent, With Run Counts Not one number. Mention rate reported as a fraction with the run count visible, broken out by prompt tier, so buying intent is never blended with definitional questions.

A page worth having states what you do in that area specifically: which neighbourhoods, what travel time, what jobs are common there, what the local constraints are. If you cannot write anything genuinely local about a town, the honest answer is not to publish a page for it.

One reframing helps when presenting this internally. Report the channel as influence rather than acquisition. Acquisition framing invites a comparison against paid media on cost per lead, which this channel will lose on the reported numbers even where it is working, because most of its effect never appears as a referral. Influence framing invites the right question, which is whether more of your market arrives already knowing who you are.

Record the conditions alongside the results: which assistant, which model version if visible, whether web access was on, the date and the run number. When a result changes sharply, the conditions log is usually what tells you whether the world changed or your setup did.

Include the constraints too. The job size you turn down, the sector you do not serve, the situation where a competitor is genuinely the better answer. Those are the statements that get quoted, and an agency will not invent them for you.