T&R Recovery GroupThe First 30 Days
50%
increase in admissions at one campus versus the same stretch of the prior month
This is the exact update T&R Recovery Group's board received after Brand North's first 30 days on the account: a completed knowledge base, two rebuilt websites, a repaired measurement spine, and a paid-search account that was costing roughly three times what leadership had been told, brought down by more than half within the first month, reported with every caveat intact.
T&R Recovery Group operates two organizations across three Texas campuses, Cypress Lake Recovery, Origins Recovery Center, and Hannah's House. The engagement began August 1, 2026, and everything below comes from that first 30 days.
Percentages and milestones only. No raw client figures.
Chapter 1
Week One: The Knowledge Engine
Before touching a single campaign, Brand North interviewed leadership across both organizations and turned the clinical reality of every campus into structured fact sheets, the same source of truth that feeds both website content and AI and answer-engine training data. Coverage spanned every campus and eight distinct knowledge domains: levels of care, staff credentials, insurance and pricing, amenities, referral mix and outcomes, and brand positioning decisions, including dropping the word "luxury" from how the organization describes itself.
Chapter 2
Weeks Two to Four: Rebuilding Both Websites Without Pausing Intake
Both properties moved to Brand North's infrastructure and were rebuilt from the ground up in the same month, while existing campaigns kept running. Hosting transferred to a modern, secured stack. Both sites were rebuilt on Next.js, no page-builder layer, versioned in GitHub, built for a 100 PageSpeed score. Every finding from the technical audit was fixed at the source-code level, including a heading order Googlebot couldn't read.
Compliance work happened in the same pass: closed outpatient and IOP pages for one location were removed ahead of a LegitScript renewal, and state license numbers were added throughout. Both rebuilt sites went live at the end of the month.
Chapter 3
Repairing the Measurement Spine
One dashboard per location now covers the full funnel, users through leads, opportunities, approved VOBs, and admissions, connecting analytics, search data, the CRM, and call tracking into a single reporting layer. This unlocked cost per VOB and cost per admission by campaign, separated tracking between two properties that had previously been sharing sources, and repaired the conversion upload back into the ad platform so bidding could finally optimize on real admissions.
Reported to the board with the caveat attached: these figures are pre-reconciliation. Definitions of "opportunity" and "approved VOB" are still being aligned with the admissions team, and an attribution discrepancy inside the CRM is under review. That's what honest reporting looks like in month one.
Chapter 4
Paid Search: Audited, Rebuilt, Restructured
The inherited account was believed to be running at a certain cost per admission. The audit showed the real cost was roughly three times higher. Thirty days later, following a full restructure, the account is producing admissions at less than half that audited cost.
That post-restructure figure represents roughly ten days of data, an early indicator, not a settled run rate. A second campus is still ramping, with its first new opportunities landing only after the restructure took effect, too early yet for a meaningful cost-per-admission read of its own.
Indexed: starting point = 100 · percentages only, no raw client figures
- increase in admissions at one campus versus the same stretch of the prior month
- 50%
- increase in admissions at one campus versus the same stretch of the prior month
- increase in verifications of benefits over the same comparison window
- 25%
- increase in verifications of benefits over the same comparison window
- reduction in cost per verification of benefits over the same window
- 23%
- reduction in cost per verification of benefits over the same window
- reduction in cost per admission since the restructure, against the audited baseline
- 50%+
- reduction in cost per admission since the restructure, against the audited baseline
Chapter 5
AEO: Measuring the Baseline Before Optimizing
Baseline visibility was measured in the second week of the engagement, across ChatGPT, Gemini, Perplexity, and Google AI Overviews, before any optimization work landed. Everything going forward gets measured against these starting numbers, not against a competitor average or an industry guess.
Rank tracking runs alongside this across dozens of keywords per property. Origins already ranks on page one for roughly half of its tracked terms, with a meaningful share already in the top three. These are starting positions, not results, baseline first, optimize second, report against the baseline.
Indexed: starting point = 100 · percentages only, no raw client figures
- share of AI answers already recommending Origins Recovery Center
- 87.0%
- share of AI answers already recommending Origins Recovery Center
- share of AI answers already recommending Hannah's House
- 77.5%
- share of AI answers already recommending Hannah's House
- share of AI answers already recommending Cypress Lake Recovery
- 71.6%
- share of AI answers already recommending Cypress Lake Recovery
Questions
Frequently Asked Questions
Why does this case study include caveats instead of just the strongest numbers?
Because that's what an honest first month looks like. Reporting pre-reconciliation figures with their limitations noted is the same discipline behind Brand North's guarantee: no number gets published unless it can be traced and explained.
What's the difference between the inherited cost per admission and the audited one?
T&R Recovery Group's leadership believed the account was performing well within category norms. Once tracking was corrected during the audit, the real cost per admission turned out to be roughly three times higher, exactly the kind of gap discovery is built to catch before more budget gets spent on faith.
What is the AEO baseline used for?
It's the fixed starting point every future AI visibility number gets measured against, so improvement can be shown against a documented figure rather than an assumed or estimated one.
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