Local Businesses And The AI Recommendation Problem

Why Local Is More Exposed The classic local query is a recommendation request with a geographic constraint, and that maps directly onto what a generated answer does well. Somebody asking who to call for a specific job in a specific town receives two or three names rather than a map and a list to work through.

Include the constraints too. The job size you turn down, the sector ai search visibility 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.

Write these plainly and prominently. A page that says we serve the wider area and offer competitive pricing contains nothing a model can use. A page that says we cover a fifteen mile radius, charge a fixed call out fee, and can usually attend within four hours can be quoted directly into an answer.

What to Spend Where If the budget is small, buy the audit and do the listings work yourself. Correcting your presence on the sources that already get cited is the highest return activity available and it requires attention rather than expertise.

The Mistake That Undoes Everything Markup is a claim, not evidence. Structured data asserting that you own a profile only helps when that profile exists and points back at you. Markup naming an author only helps when the author can be found elsewhere.

Put someone's name against this. Crawler rules sit between marketing, development and whoever administers the content delivery network, which in most organisations means nobody checks them. The failures documented here are not difficult to find, they are simply nobody's job, and a quarterly review taking half an hour prevents the most complete form of invisibility available.

Be wary of proposals where the largest line is content production. It is the easiest work to scale, the easiest to bill and the least likely to be the constraint, particularly before a baseline exists. A proposal weighted toward diagnosis, technical fixes and third party corrections is usually cheaper and almost always sequenced better.

One local specific worth checking is how your opening hours and availability are stated across every listing. These are among the details most frequently quoted in local recommendations and among the most likely to be wrong, because they change seasonally and get updated in one place. An assistant confidently telling somebody you are closed is a lost job that leaves no trace in any report.

The idea is reasonable and adoption is inconsistent. Support varies by provider and no major system currently treats it as required. Treat it as a cheap and speculative addition rather than a deliverable worth paying much for.

Watch the source list as closely as the mention rate, because it usually moves first. New citations from a directory you corrected are a leading indicator, and they typically appear a month or two before any change in whether you are recommended.

A reasonable formulation: after two quarters, we expect movement in mention rate on buying intent prompts, improvement in the accuracy of how we are described, and new citations from the sources our baseline showed matter. If none of those move, we will treat the approach as unsuccessful.

A weak brief produces a generic proposal, and a generic proposal produces a generic engagement that spends the first two months discovering things you already knew. The brief is the cheapest lever you have over the quality of the work.

One section most briefs omit is worth adding: what has already been tried and what happened. Agencies frequently propose work that was done two years ago and abandoned, because nobody told them. Listing previous efforts, including the ones that failed, saves a month and signals that you will be a straightforward client to work with.

Structured data attracts a particular kind of over-investment. Teams implement a dozen schema types, validate them all, and conclude the job is done, having spent most of their effort on markup that changes nothing about how a machine understands the business.

None of them are harmful. They just consume implementation and maintenance time that would achieve more if spent making the Organization markup accurate everywhere, or correcting the directory listing that has your old address on it.

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.

Accuracy Beats Coverage The most common real defect is not missing markup, it is markup that disagrees with the page or with the rest of the web. A founding year in your schema that differs from your about page. A logo URL that returns a 404. A contact point nobody monitors.

Performance and Score Based Models Both sound aligned and both create problems. Payment tied to mentions creates pressure to shape the prompt set toward questions you already win, which is measurable improvement that means nothing.