Anonymized proof

How Booked.Dental measures real patient opportunities.

These examples are anonymized and conservative. They show the operating principle: filter, track, and learn from patient quality instead of optimizing around clicks or raw form volume.

Full-arch implants and All-on-4

Anonymized full-arch implant campaign

Example campaign for a US implant clinic focused on full-arch demand.

Challenge

The clinic needed fewer casual implant inquiries and more prospects who understood the treatment value, timeline, and financial decision.

Approach

Booked.Dental rebuilt the funnel around treatment-specific creative, Google and Meta intent signals, lead filtering, and follow-up feedback from the clinic team.

What this shows

The campaign shifted optimization away from easy form fills and toward qualified full-arch opportunities. Results vary by market, offer, follow-up, and clinic capacity.

Veneers and smile makeovers

Anonymized veneers campaign

Example campaign for a cosmetic clinic selling premium smile design.

Challenge

The clinic wanted veneer prospects who valued planning, aesthetics, and case quality instead of low-price cosmetic shoppers.

Approach

The campaign used UGC-style creative, outcome-focused landing copy, budget-fit filtering, and tracking around smile makeover opportunity quality.

What this shows

The clinic team received clearer cosmetic opportunities and better context before follow-up. Results vary by market, case value, creative, and close process.

Implant and cosmetic lead qualification

Lead filtering and AI learning loop

Example of how rejected and approved leads improve acquisition quality.

Challenge

Raw lead volume made reporting look busy, but the clinic needed to know which campaigns produced reachable, treatment-fit prospects.

Approach

Booked.Dental connected form answers, follow-up outcomes, source data, and qualification status so the ad system could learn from patient quality signals.

What this shows

Budget decisions became easier because the clinic could separate noisy activity from real opportunities. Results vary by data quality and follow-up discipline.

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