Inside the data: a Q1 2026 performance report on the Zenoti + CorralData network.
Zenoti already stores everything a practice needs to make better decisions. CorralData unlocks it, with AI that reports, predicts, and acts on that data across the whole team. This report is what the data looks like when both are running together.
Across the Zenoti operators in CorralData's network, the data shows a cohort growing roughly five times the industry rate, with materially higher per-visit revenue, materially better retention, and a single behavioral signal — how broadly the team uses the AI layer — that separates the operators pulling ahead from the rest.
The CorralData × Zenoti operator base, in numbers.
The connected Zenoti network spans single-location practices through multi-brand PE-backed portfolios, distributed across aesthetics and aesthetics-adjacent wellness. The figures in this report are computed from each practice's live Zenoti data via the native CorralData connector.
The Zenoti cohort grew 30% year-over-year in Q1 2026.
Across a same-basis cohort of Zenoti operators connected through both Q1 2025 and Q1 2026, blended service and product revenue grew from $68.8M to $89.8M. That's a +30% YoY result, against an AmSpa industry baseline of roughly +6%. The cohort is growing at approximately five times the industry rate, on a revenue base large enough to make the comparison meaningful.
The shape of the network growth
The +30% isn't carried by one outlier. Within the same-basis cohort, growth distributes across bands. Several operators posted growth at 50% or higher, the median is positive, and the only decliner in the sample is concentrated in wellness/GLP-1, consistent with the broader cohort substitution pattern reported in the Q1 2026 Aesthetics Industry Benchmark.
Unique monthly patients tracked the same trajectory, growing from 1,672 to 4,577. Location-level P&L visibility, provider performance tracking, and marketing attribution were built in the first months and used actively throughout.
Zenoti's prior-year fields stay in the EMR until someone pulls them. CorralData puts daily YoY tracking in front of every owner, operator, and location manager — with AI-flagged anomalies and side-by-side peer benchmarks. The growth story becomes a daily operating decision, not a quarterly review.
The cohort runs at ~$400 per visit, against an AmSpa norm of $280–$340.
Revenue per patient visit is the cleanest single measure of pricing power and treatment-planning discipline. The Zenoti cohort sits roughly 20–25% above the AmSpa industry norm, with significant range across operators driven by service mix, geography, and pricing posture.
Operators near the top of the distribution typically share three characteristics: higher injectable share of revenue, treatment planning that books the next visit before the patient leaves, and pricing discipline anchored to brand position rather than to local discounting. CorralData makes those three factors visible at the provider and location level on a daily cadence.
CorralData computes revenue per visit by provider, location, and service line directly from Zenoti, then benchmarks every provider against the team automatically. AI-generated recommendations surface which service mix shifts move the ticket up next, before the manager has to ask.
Returning-patient rate runs at 82% cohort median versus a 38–42% AmSpa benchmark.
The strongest signal in the dataset. Returning-patient rates across the Zenoti cohort cluster well above the AmSpa industry benchmark, with several operators in the network running at 85%+. The metric measures patient stickiness: of patients who showed up in Q1 2026, what share had a prior visit on record. The Zenoti operators in this network keep meaningfully more patients than the industry norm.
Membership health monitoring and patient re-engagement workflows, both requiring Zenoti membership data joined to CRM, were among the first capabilities built. The improvement was sustained, not a single-month spike.
Zenoti's guest and membership tables hold the retention signal. CorralData converts it into action — the lapsed-patient list pushes straight to your CRM with outreach assignments, AI agents flag the highest-LTV at-risk patients first, and retention becomes a daily work queue instead of a quarterly report.
Cohort rebooking rate runs at ~35%, versus an industry baseline near 25%.
Rebooking is the leading indicator behind retention. Among the Zenoti operators in the network, the cohort median sits at ~35% of Q1 2026 patients having a future appointment already booked, with several operators at 45%+. Across the broader CorralData aesthetics cohort, operators who have patient lifecycle workflows running on top of Zenoti's clinical and membership data see roughly 1.5 to 2× the visit frequency of operators using general-purpose CRM tooling. The Zenoti operators in this network sit at the upper end of that range because Zenoti's membership and guest data is the cleanest input the lifecycle layer can run on.
The first question operators ask
"Which patients don't have a future appointment scheduled?"
It reads as a reporting question. The operators in this network who treat it as a work queue, with patient lists pushed to the CRM and outreach assignments tracked, see rebooking move. Operators who treat it as a chart someone glances at once a month do not.
CorralData reads Zenoti's appointment status in real time. The list of patients without a next visit on the books gets pushed to the CRM the same day, with rebooking outreach assignments by provider. AI agents track which providers close the loop best and flag the rest.
Revenue per service hour spreads from ~$300 to $750+ across the network.
Two practices can post the same monthly revenue and run radically different businesses underneath. Revenue per service hour collapses operating productivity into one number, capturing treatment planning, enhancement upsell, room turn time, and pricing power all at once. Across the Zenoti cohort, the spread is more than 2× from bottom to top, and the cohort median sits at the upper end of the broader CorralData aesthetics distribution.
The productivity spread is operating culture, not scale. Single-location operators appear in both the top and bottom quartiles. Multi-location operators do too. The difference is whether the team can see revenue per hour by provider on a daily cadence and act on it.
Zenoti captures appointment duration cleanly — only the analytics layer makes it usable. CorralData computes revenue per service hour daily, benchmarks every provider against the team, and surfaces who's leaving margin on the table. The metric goes from invisible to actionable.
The top cohort converts inbound leads at 42% versus a ~20% industry baseline.
Among the operators in the network with lead-intelligence tooling connected alongside their Zenoti instance, lead-to-booked-consult conversion runs at roughly twice the published industry baseline. The driver is speed: median time-to-contact on inbound calls and web forms runs at approximately 2 hours, versus the 8 to 24 hour industry range. The lead is hot for an hour, and the operators who answer in that hour book it.
Sub-cohort scope: the 42% figure reflects operators in the network who have connected lead-management data (call tracking, web forms, CRM) to CorralData alongside Zenoti appointment data. The smaller sub-cohort produces a cleaner conversion number; the full Zenoti network range is wider and depends on each operator's lead-source maturity.
CorralData is typically the only place in the stack where ad spend, lead intelligence, and Zenoti appointment outcomes sit in the same model. Per-channel CAC and lead-to-consult conversion become daily metrics, and AI agents adjust paid media spend automatically based on which channel is converting to booked revenue this week — not last quarter.
The single strongest signal in the dataset: how broadly the team uses CorralData's AI capabilities.
The metrics above describe what the data shows. The question worth answering next is what predicts an operator's place in that distribution. Across the broader CorralData aesthetics cohort, one variable separates the operators pulling ahead from the rest of the field more cleanly than any other: how many people on the team are actively engaging the AI layer on the connected data.
The differentiator is breadth, not depth. Operators that put the analytics layer in front of the owner plus the operator plus the front-line manager at each location grew. Operators where one analyst at headquarters pulled reports for everyone else did not. Every Zenoti operator in this network sits on the team-adopted side of that line.
Activation depth correlates with growth outcome
The team-adoption signal sharpens further when operators are grouped by how much of CorralData's capability set they've activated on top of Zenoti. The deeper the activation, the stronger the growth band.
Activation tiers derived from CorralData feature usage data across the broader aesthetics cohort, Q1 2026. Growth ranges represent band-level outcomes observed across the cohort.
What that looks like operationally
Across the Zenoti operators in the network, 1,244 dashboards have been built on top of their connected data, with 29,928 individual metrics tracked. In Q1 2026 alone, operators added 417 new dashboards and 10,771 new metrics, with 16,173 AI-prompted analytics interactions in the same period. This is not occasional reporting behavior. It is operational infrastructure that runs every morning.
CorralData is Zenoti's official analytics partner, formalized at Innergize 2025. The native connector and direct API access are what make this analysis possible: data flows directly from each Zenoti instance into the CorralData warehouse at the schema level, with prior-year fields preserved as Zenoti computes them.
Cohort definition, data windows, and computation.
Network and cohort scope
Network: CorralData customers with at least one Zenoti connector in a connected state established before April 1, 2026, excluding test schemas, paused customers, demo accounts, and standalone Google Sheets ingestion of Zenoti exports. The Zenoti-connected network spans 350+ distinct practice locations across single-location practices through multi-brand PE-backed portfolios.
Same-basis YoY cohort: a sub-cohort of Zenoti operators with comparable Q1 2025 and Q1 2026 service revenue in the Zenoti transactional layer. Sub-cohort revenue: $68.8M in Q1 2025 and $89.8M in Q1 2026, with a +30.5% blended YoY growth rate.
Revenue calculations
Computed from each connected practice's transformed_sales_accrual_flat_file on an accrual basis. Filters applied: status = 'Closed', sale_type = 'Sale', item_type IN ('Service','Product'). Revenue summed at sales_exc_tax excluding sales tax. Multi-brand operators are aggregated across all connected Zenoti orgs in their warehouse.
Revenue per patient visit
Computed as service revenue divided by distinct invoice count over the period, across the Zenoti cohort. Cohort range reflects per-operator medians; outliers in wellness-heavy operators with high-volume, low-ticket visits are excluded from the cohort median. AmSpa industry range is published in the 2024 State of the Industry Report.
Patient retention
Returning-patient rate is computed as the share of Q1 2026 unique guests where first_visit = 'No' on at least one transaction in the period, indicating a prior visit on record. This is a broader retention proxy than the 12-month-only AmSpa definition; the cohort still materially outperforms when both are framed on a same-window basis. The 38–42% AmSpa 12-month retention benchmark is the published industry figure.
Rebooking rate
Computed as the share of Q1 2026 unique guests with at least one future appointment in the Zenoti appointments table (status codes 0, 2, or 4) as of mid-May 2026. Cohort range 17–48%; median ~35%. The ~25% industry baseline is a directional estimate based on published medspa research.
Provider productivity
Revenue per service hour is computed as total service revenue divided by total completed-appointment hours in Q1 2026. Appointment duration is read from Zenoti's native start/end-time fields. Distribution figures reflect a mix of direct cohort calculation and the broader CorralData aesthetics cohort distribution published in the Q1 2026 Aesthetics Industry Benchmark.
Lead-to-booked-consult conversion
The 42% figure reflects operators in the High Growth cohort with lead-intelligence tooling (call tracking, web forms, CRM) connected alongside their Zenoti instance. The ~20% industry baseline is a directional estimate based on published aesthetics industry research. Median time-to-contact (~2 hours) is computed on operators in the same sub-cohort.
Team-AI-adoption gap and activation tiers
The +17.8% / −15.6% revenue-weighted YoY split and the activation tier growth ranges are drawn from the Q1 2026 Aesthetics Industry Benchmark, computed across the broader CorralData aesthetics cohort. Every Zenoti operator in this report's network sits in the team-adopted segment.
Known limitations
The cohort is self-selected. Operators who choose to connect a dedicated analytics layer alongside Zenoti may share other operating characteristics that contribute to observed outcomes independent of the combined system itself. Sub-cohort metrics (lead conversion) are explicitly scoped to operators with the relevant additional integrations connected and should not be read as full-cohort numbers. Industry baseline figures are published comparisons, not internally computed.
