SGA Practice S-Curve — Revenue & EBITDA

S-Curve Leverage Analysis · IPO Prep

Finding $— in EBITDA from the practice network

Target: $2M • Window: 12-month realistic capture • As of —

TOP-LEVER EBITDA IDENTIFIED (12 MO)

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Practices with a lever

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All applicable levers

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Network EBITDA (Gen4, TTM)

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How the number breaks down

Methodology summary

Four views, two axes

Every practice scored on Revenue drivers (demand, capture, conversion) and EBITDA drivers (expense ratios, throughput, waste) from two independent sources (PBI financials, lagging; Dental Intel operations, leading).

Lever math

Revenue levers: captured revenue × marginal margin (avg+25pp, cap 65%) × 30% realization, capped at 8% of TTM.
Expense levers: expense-ratio gap × revenue base × 30% realization (flows 1:1 to EBITDA).

Empirical validation

24 months of DI history × 13 metrics tested for within-practice predictive power. Strongest leading indicator: grossHygieneProduction (|r|=0.62 @ lag 3). Methodology tab has full matrix.

Divergence watch — where leading signals are telling us something PBI can't yet see

A practice's DI score (leading) diverging from PBI score (lagging) is often the earliest warning of a margin move in the next 90 days. Here are the 10 largest divergences in the network.

Practice	ROD	PBI Rev	DI Rev	PBI EBITDA	DI EBITDA	Signal

Top 10 $ opportunities

#	Practice · ROD	TTM Rev	Margin	Revenue Driver	Rev $	Expense Driver	Exp $	Combined $

By Regional Ops Director

ROD	Practices	TTM Revenue	TTM EBITDA	Margin	Top-lever $ (12mo)

Coverage disclosure

What's next

Monthly refresh — move from snapshot to automated month-end pipeline, feeding both DI + Gen4 PBI
Complete INTACCT P&L coverage — currently 84 practices; adding the remaining ~180 unlocks practice-level expense-ratio levers network-wide
Correlation re-run — with 36+ months of DI history and richer features (YoY change, trend z-scores) strengthens the empirical signal
ROD drill-down dashboards — ROD-specific rollup with their own top-10 leverage list, exportable

Hero practice for this walkthrough

Revenue Lifecycle · Power BI

Each practice positioned by score. Color = stage. Size = TTM revenue.

Highlight divergences Hide unsourced

4-View Comparison Grid

Every practice scored four ways: Power BI Revenue, Power BI EBITDA, Dental Intel Revenue (leading), Dental Intel EBITDA (leading). Divergences surface practices where leading indicators drift from lagging financials — the insight surface.

Practice · ROD	PBI Rev	PBI EBITDA	DI Rev	DI EBITDA	TTM Rev	TTM EBITDA	Margin	Divergence	Top Lever $

EBITDA Leverboard

Every practice ranked by top-lever 12-month EBITDA upside, with projected margin delta after execution.

Practice · ROD	TTM Rev	Margin	Top Revenue Driver	Rev $ (12mo)	Top Expense Driver	Exp $ (12mo)	Combined $

Four views, two axes

Each practice is scored on two axes (Revenue growth, EBITDA margin) using two independent data sources (Power BI financials — lagging but definitive; Dental Intel operations — leading and predictive). The 4-view grid surfaces divergences where leading signals hint at upside or downside not yet visible in financials.

PBI · Revenue

Net revenue, TTM, budget attainment, YoY (lagging)

PBI · EBITDA

Margin, payroll %, supplies %, occupancy % (lagging)

DI · Revenue

Case acceptance, reappt, recall $, unscheduled $ (leading)

DI · EBITDA

Hygiene completion, no-show/broken, visits/hr (leading)

Stages (both axes, both sources)

Launch
Foundation unstable — base low or margin fragile.

Build
Momentum real, repeatability not yet achieved.

Scale
Operationally real — scaling systems, not proving demand.

Optimize
Strong — gains come from efficiency & throughput.

Mature
Near ceiling — limited by capacity, not discipline.

Scoring weights (v2)

Revenue drivers vs expense drivers

Revenue S-Curve is scored on metrics that drive top-line: demand, capture, conversion, recall. EBITDA S-Curve is scored on expense drivers: cost ratios, labor waste, throughput efficiency. They move independently — a practice can be strong on one and weak on the other, and the levers for each are different.

Lever $ math — how every $ number is computed

Revenue levers flow through marginal margin:

EBITDA_impact = revenue_captured × marginal_margin × realization_factor
marginal_margin = min(avg_margin + 25pp, 65%)
realization_factor = 30% (12-month realistic capture)
cap = 8% of annual revenue base (sanity)
avg_margin uses REAL Gen4 TTM EBITDA margin when available

Expense levers flow 1:1 (reducing an expense ratio by 1pp on $10M revenue = $100K direct EBITDA):

EBITDA_impact = revenue_base × (current_ratio − target_ratio) × realization_factor
target_ratio = portfolio top-quartile (p25 of expense-ratio distribution)

Correlation validation — empirical proof (24-mo within-practice)

24 months of DI history tested against grossProduction at lag 0 / +3 / +6 months. Within-practice correlation removes the practice-size confound and reveals whether changes in a metric predict changes in production.

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How to read: within-practice correlations are much harder to surface than cross-practice because they reflect month-over-month volatility at one location. A |r| ≥ 0.30 at a forward lag is a real leading indicator; 0.10–0.30 is directional but noisy; <0.10 is not a statistically meaningful leading signal at this window. All levers still hold their math validity (deterministic benchmark gaps) regardless — this table tells you which signals deserve the most trust.

Data sources

v3 upgrade path

Correlation validation — 24-mo DI history pulled; correlation against PBI forward production in progress
Full INTACCT coverage — currently 84 practices with P&L decomp; needs full network
Monthly refresh automation — currently snapshot; pipeline should run month-end
Region/ROD rollup dashboards — mapping attached; rollup views next