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.
Stages (both axes, both sources)
Foundation unstable — base low or margin fragile.
Momentum real, repeatability not yet achieved.
Operationally real — scaling systems, not proving demand.
Strong — gains come from efficiency & throughput.
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.
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