SGA
Practice S-Curve
Revenue + EBITDA lifecycle · dual-source · per-practice leverage
As of —
Practices —
Top-lever EBITDA (12mo) —
Revenue Lifecycle · Power BI
Each practice positioned by score. Color = stage. Size = TTM revenue.

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 Lever Type Source $ EBITDA (12mo) New Margin

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)

S1
Launch
Foundation unstable — base low or margin fragile.
S2
Build
Momentum real, repeatability not yet achieved.
S3
Scale
Operationally real — scaling systems, not proving demand.
S4
Optimize
Strong — gains come from efficiency & throughput.
S5
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