[ Case study ]
The practice's reviews were years old and mostly about parking. New patients checked profiles and went elsewhere; the front desk knew reviews mattered but had no system, and one staff member's 'only ask the happy ones' idea was a policy violation waiting to happen.
CLIENT a family dental practice — FOCUS Automate the neutral request
Representative examplesEvery case study in this library is an illustrative composite of the kind of engagement we deliver — written to show our method and standards, not to name clients.
Two dentists, a hygienist team, and a front desk of three serve a family dental practice that has traded from the same high-street location for decades. Its patient base is loyal and local, its appointment book full, and its online reputation a fossil: the newest public reviews date back years and mostly discuss the car park. The practice manager reads the review profiles occasionally and winces; the front desk knows reputation matters and has ideas about it, but asking patients for reviews is one unplanned task among two hundred on any given morning.
The practice's reviews were years old and mostly about parking. New patients checked profiles and went elsewhere; the front desk knew reviews mattered but had no system, and one staff member's 'only ask the happy ones' idea was a policy violation waiting to happen.
The reliable part becomes automatic and the human part stays human. Every patient receives the same review request by text after their appointment — uniform, ungated, and unhurried, which is both the policy requirement and the point: honesty at volume. Responses to reviews are drafted from templates but posted only after the practice manager approves, with clinicians consulted on anything sensitive. New reviews reach the manager's phone for one-tap approval, and a dashboard shows the practice its reputation daily instead of quarterly, so the front desk sees the effect of the system they run.
Just as important is what we ruled out, and why:
Post-appointment SMS requests fire for every patient via workflow — honest, uniform, and utterly reliable, which was the whole point.
Review responses are drafted from templates and approved by the practice manager before posting — automation proposes, people speak.
New reviews reach the manager's phone with a one-tap approve; the practice sees its reputation daily instead of quarterly.
Delivered by the growth pod — strategist + automation specialist over 4 weeks, with working increments reviewed with the client every week.
Obstacle
The first response drafts read like a bank's complaint line — polite, hollow, and wrong for a nervous patient who felt rushed — and the dentists refused to let them go out under the practice's name.
Handled: We rewrote the template library with the dentists in the room, kept automation for neutral acknowledgements only, and made clinician review a standing step for anything negative.
Obstacle
The request initially fired minutes after the appointment, when patients were still numb, running late, or standing at the desk — and message opt-outs began to climb.
Handled: The front desk told us when patients actually surface for air; we moved the send to the early evening, and opt-outs fell back to noise within a fortnight.
The headline: new review volume per quarter, sustained across two quarters — +112%, read from Review platform counts. A second check: average rating as recent reviews diluted the parking-era legacy at 4.6 → 4.8.
The front desk stopped experiencing reviews as an accusation and started experiencing them as feedback that arrives on their phone, handled in seconds. New reviews describe actual care — a nervous patient treated kindly, a same-day emergency seen — instead of parking. The practice manager reads the practice's reputation daily in the time it takes to make tea, and the staff member with the gating idea now explains to new colleagues why uniform requests are the honest version.
The result was read from Review platform counts against the pre-engagement baseline over the stated window, with a guardrail check on average rating as recent reviews diluted the parking-era legacy. Where platform-reported numbers and business outcomes differ, this record says which layer it is quoting.
What we would do differently
We would have asked the front desk what 'done' looks like first — their definition (one tap, zero typing) killed two fancy features we'd have built.
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