You have a 4.8-star rating on Google. That feels solid — and it should. A 4.8 is genuinely good.
But if your review count is low and your last review was months ago, patients see a different story than you do. And so does Google’s algorithm.
Rating alone hasn’t been enough for years. What matters now is rating plus count plus recency — all three, simultaneously. A single weak signal in that group can suppress your local ranking and reduce patient confidence, even if everything else looks good on paper.
The Three Signals That Actually Matter
Google’s local ranking algorithm has never relied on stars alone. But the weight given to review count and recency has grown significantly as the local search market became more competitive. Here’s how each signal works:
Rating. You want 4.5 stars or above. Below 4.5, patients start applying additional scrutiny. Below 4.0, a meaningful portion of patients filter you out entirely before they ever look at count or recency. Your 4.8 is doing its job. This isn’t your problem.
Count. Volume matters because it establishes credibility. A high rating with very few reviews is easy to dismiss as a small sample. The same rating backed by a large number of reviews is hard to argue with. Practices with strong review counts and recent activity consistently outrank those without — that’s not a marginal advantage, it’s the difference between page one and page two.
Recency. Google weights fresh reviews more heavily than old ones. A review from four months ago is not helping you the way a review from last week would. The signal Google is looking for is activity — an active practice has patients, and those patients are talking. A review trail that’s gone quiet for months looks like a practice that’s slowing down, even if your waiting list is full.
A high star rating with a small review count and a months-long gap is not a strong review profile. It’s a fragile one. One bad week can drop your average, and you have no recent velocity to buffer it.
How Google’s Local Algorithm Weights Recency
Google hasn’t published the exact formula, but the pattern from observing local search behavior is consistent: reviews from the past 90 days carry the most weight. Reviews from the past 30 days carry even more.
This is sometimes called review velocity — not just how many reviews you have, but how quickly new ones are accumulating. A practice adding reviews consistently every month looks fundamentally different to the algorithm than a practice with a large total but no recent additions, even if the totals are similar.
The practical implication: you can’t treat reviews as a one-time project. Building up your count and then stopping is better than staying low, but it’s not a destination. The practices that hold top local pack positions consistently are the ones generating reviews on an ongoing basis — a steady stream, month after month.
How AI Tools Use Reviews (They Read the Content)
This is the piece most owners miss entirely.
When ChatGPT or Perplexity surfaces a med spa recommendation, they’re not averaging your star ratings. They’re reading the text of your reviews — the actual words patients wrote — to understand what you offer, what the experience is like, and whether patients would recommend you for a specific treatment.
A review that says “Amazing experience, highly recommend!” is almost useless to an AI tool. A review that says “I got Botox here for the first time and Dr. Martinez was incredible — natural results, no bruising, I’ll be back in three months” is a data-rich signal. It tells AI your specific treatment, a provider name, the outcome, and the patient intent to return.
The more your reviews contain specific treatment names, outcomes, and staff names, the more AI can connect your practice to relevant searches. You influence this by what you ask patients to include when you request a review.
The Review Velocity Problem (and Why It Keeps Getting Worse)
Here’s the thing about review gaps: they compound.
A months-long gap in reviews sends a signal. A year from now, if you haven’t addressed it, those reviews are even older — and maybe you’ve added a handful of recent ones, but there’s no consistent pattern. The algorithm treats this as drift. Patients notice the dates.
Meanwhile, your competitor who’s been quietly asking every patient for a review is adding several per month. Their recency signal is strong. Their count has grown well past the point of credibility. They’re showing up higher.
The gap between where you are and where you could be grows faster than most owners expect, because review generation is a compounding activity. Start earlier, stay consistent.
Simple Systems for Steady Reviews (Without Violating Guidelines)
Google’s guidelines prohibit incentivizing reviews — you cannot offer discounts, gifts, or services in exchange for reviews. What you can do is ask, systematically and at the right moment.
The post-appointment text. Send a text message two to four hours after the appointment, while the experience is fresh. Keep it simple: “Hi [Name], thanks for coming in today — we hope you’re happy with your results. If you have a moment, we’d love a Google review. Here’s the link: [direct review link].” A direct link removes friction. Friction kills follow-through.
The checkout ask. Train your front desk to verbally ask at checkout: “We’d really appreciate a Google review if you have a moment — it helps patients find us.” Then hand them a card with the QR code that links directly to your review page. The ask in person, followed by a frictionless link, is more effective than the text alone.
The follow-up email sequence. If you’re not already sending a post-appointment follow-up email, start. Include a review request in that email — three to five days after the appointment, after patients have had time to see their results. This is especially effective for treatments with visible outcomes: filler, laser, Botox at the two-week mark.
None of this requires new software. It requires consistent execution. The practices generating a steady flow of reviews every month aren’t doing anything unusual — they’re just asking every patient, every time, with a direct link and minimal friction.
What a Strong Review Profile Actually Looks Like
The benchmark to aim for: 4.5 stars or above, a review count large enough to establish credibility, and at least one review in the last 30 days. That profile ranks well. It converts patients who are comparing options. It gives AI tools enough data to recommend you confidently.
If your rating is strong but your count is low, the first priority is volume. Generate reviews consistently until you’ve built a base that’s hard to dismiss. Then maintain velocity — a steady cadence keeps your recency signal active without requiring extraordinary effort.
Your rating is already your asset. Build the volume and recency around it, and that 4.8 starts doing the work it should have been doing all along.
To see where your review profile sits in your overall discoverability score — and how it compares to competitors in your market — run your free Cornflower scan.