Several tactics still being sold
are now federally prohibited.

If it violates platform policy, yes, through the documented process — Google publishes what counts as prohibited and restricted content, and reviews breaching it can be flagged with the specific policy cited. If it is simply unwelcome but true, no. And the route some vendors still use is now prohibited: the FTC's final rule names review suppression through unfounded legal threats or intimidation and allows civil penalties. What works instead is volume, an honest public reply, and fixing the cause — the same loop covered in profile optimization.

No. That is conditioning the request on the sentiment you expect, and the FTC's final rule prohibits providing compensation or incentives conditioned on a review expressing a particular sentiment. Filtering is the same logic without the payment, and it is squarely the territory the rule addresses. The practical answer is better than the workaround anyway: asking everyone produces more volume, and Google's local ranking guidance names review count alongside rating. A handful of honest three-star reviews among many does less damage than a suspiciously perfect profile. Request timing is built into the CRM workflow rather than run by hand.

Nobody can give you a number, and any specific threshold you have been quoted is unsourced. Google's guidance states that more reviews and positive ratings can help local ranking and that prominence is based partly on review count — it publishes no target. What is observable is relative: the useful comparison is against the businesses currently ranking for your queries, not against an industry figure. Google also states plainly there is no way to request or pay for better local ranking, which is worth remembering when someone quotes a guaranteed position. See local SEO.

Yes, and mostly for people who are not the reviewer. Google provides documented tools for reading and replying to reviews, and every reply is public and permanent. The next prospect reads how you handle a complaint far more often than the complainant re-reads your answer. A reply that adds a specific detail — what the fault was, what you changed — is content in its own right. Forty identical thank-yous tell a reader exactly how much attention their problem would get. Related: content marketing.

Our answer here is observation, not documentation, and we label it that way. Google publishes no mechanic for how assistants select review text, and neither do ChatGPT, Gemini, Perplexity, Claude or Microsoft Copilot. What is structural rather than speculative: a star rating contains no language, so there is nothing in it to quote, while a review naming a specific service in a customer's own words is retrievable text. Google's generative AI guidance does confirm its features retrieve and review information on pages. Measurement runs through AI visibility monitoring.

No, and this is the clearest line in the whole discipline. The FTC's final rule prohibits creating or selling reviews that misrepresent the reviewer's experience and prohibits procuring them from company insiders without disclosure, with civil penalties available. Its endorsement guides set out what a connection to the business requires you to disclose. Platform policy prohibits it separately — Google's content policies cover conflicts of interest. Legitimate volume gets built through the request workflow described under local SEO.

It is arithmetic, and it is slower than anyone wants. A rating is an average, so recovery depends on how many reviews sit beneath the bad ones and how quickly new ones arrive — a business with forty reviews moves far more slowly than one with four hundred. That is why volume is the foundation rather than a vanity metric. What moves faster is the reading experience: a recent, specific, honest reply beneath a complaint changes how the profile reads long before the number moves. No responsible agency promises a rating by a date, and Google states plainly there is no way to request or pay for a better local ranking. Start with a visibility assessment.

Only where it reflects something genuinely on the page. Google documents review snippet structured data and its requirements, and LocalBusiness markup for business details. The failure mode is self-serving markup — a business marking up its own aggregate rating in a way the page does not support — which strays into what the spam policies address. Markup that contradicts reality is another contradiction rather than an advantage, which is the same principle applied in technical SEO.

Stop treating it as a reputation problem. Four reviews naming the same fault across two months is an operational finding delivered free, in customer language, with dates attached. Answering it publicly eleven times while nothing changes produces exactly the outcome you would expect. We track themes over time and escalate recurring faults to the operator with the quotes attached, then confirm the fix later in the review text itself — which is the only proof that carries, and feeds straight back into how the service is described. Google's framing of genuinely useful over performative applies to service as much as content.

In two layers kept separate. Measured: review volume and rating over time, response coverage and speed, and where reviews sit relative to the businesses ranking for your queries — plus Google's generative AI performance report for its own AI surfaces. Observed: sampling the assistants on a fixed question set and recording who gets named. The second layer is genuine signal and is not a ranking metric, because Google states that no third-party tool has access to its internal ranking or AI systems. Start with an A.R.C. Report.