SGA Pacing Intelligence Dashboard

Six Months of Data Reveal a Clear Seasonal Cycle

The data now spans November 2025 through April 2026 — revealing a clear seasonal cycle. January/February benefits renewal creates a production peak that the R8 rolling average anchors to. By March/April, production returns to November/December baseline levels, making the R8 prediction unreachable.

Weekly Network Production Trajectory

Total weekly production across all practices with R8 prediction overlay

Peak Month (Jan)

$7.06M

$7,058,729/wk

Current (Apr)

$6.34M

$6,338,364/wk

R8 Prediction

$7.54M

$7,539,503/wk

Peak-to-Current Drop

-10.2%

Jan to Apr decline

Dec ≈ Apr

Cyclical

$6.33M ≈ $6.34M

Monthly Summary by Day of Week

Month	Mon	Tue	Wed	Thu	Fri	Weekly Total	vs R8	vs January

Day-of-Week Decomposition

Five mini-charts showing the 6-month trajectory for each day of the week. Each chart has its own R8 line for comparison.

Thursday: Steepest Decline

Dropped from $1.61M (Jan) to $1.34M (Apr) — a loss of $263K/week. Thursday production has declined every month since January, the most consistent downward trend of any DOW.

Tuesday: Largest Absolute Day

Still the highest-production day at $1.58M in April, but down from $1.70M in January. The -7.4% drop represents ~$125K/week in lost production from the network's strongest day.

Wednesday: December Anomaly

Wednesday was the only DOW that actually peaked in December ($1.68M) rather than January. The decline from peak is -$303K/week to April levels.

Friday: Smallest but Most Stable

Fewest practices (59-62) and lowest volume, but the most stable trajectory. The spread between best and worst months (excl. Dec anomaly) is the narrowest of any DOW.

Is the R8 the Wrong Tool?

A seasonal model (comparing to same-month-last-year) or a weighted model (heavier on recent months) would be more accurate than a flat rolling average that treats January the same as April. The R8 anchors to the Jan/Feb peak and will always overpredict during the spring and fall troughs.

Production Trajectory with Projections

Actual weekly production (solid) extended into May/June (dashed) with R8 overlay

R8 Predicts

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Rolling 8-week network weekly average × 4.33 weeks. The R8 is now computed from actual network DOW production (not summed per-practice averages), so it reflects what the network actually produces.

Trend Predicts (May)

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Based on 6-month linear regression of each DOW. More conservative but doesn't model seasonality either.

Calendar-Aware (May)

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Prior-year same-month per-DOW × YoY trend × actual day count, with holiday closures. See the Calendar Forecast tab.

DOW Linear Regression Detail

Day	Slope ($/mo)	May Predicted	Jun Predicted	R8	May vs R8

Calendar-Aware Monthly Projection — May & June 2026

A true projection that accounts for: (1) the actual number of Mondays/Tuesdays/Wednesdays/Thursdays/Fridays in each target month, (2) the seasonal pattern from prior-year same-month per-DOW production, (3) the current YoY growth rate from the rolling 4-week comparison, and (4) explicit holiday closures. This is a fundamentally better model than a flat R8 rolling average for trough months.

May 2026 Forecast

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June 2026 Forecast

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YoY Trend Applied

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latest 4wk vs prior year

May vs R8 Calendar

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Jun vs R8 Calendar

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May 2026

—

Working Days

—

Holiday Closures

—

none

No-Holiday Total

—

if every weekday open

Holiday Drag

—

closed-day impact

DOW	Working Days	Prior-Yr Wk Avg (May 25)	YoY-Adj Wk Avg	Monthly Contribution

June 2026

—

Working Days

—

Holiday Closures

—

none

No-Holiday Total

—

if every weekday open

Holiday Drag

—

closed-day impact

DOW	Working Days	Prior-Yr Wk Avg (Jun 25)	YoY-Adj Wk Avg	Monthly Contribution

Model Comparison — Three Ways to Project May & June

R8 Calendar (DOW-counted R8) vs Linear Trend (regression on Nov–Apr) vs Calendar-Aware (this tab). The calendar-aware model is the only one that uses prior-year same-month seasonality plus actual day counts plus holiday closures.

Why Calendar-Aware Adds Value Over R8

Even with R8 corrected to the true rolling 8-week network average, it still treats every future month the same. The calendar-aware model adds three things: (1) seasonal anchoring — uses May/June 2025 actuals so it knows summer's profile differs from spring, (2) actual day counts — June 2026 has 5 Mondays vs May 2026's 4, which materially shifts the total, and (3) explicit holiday handling — Memorial Day and Juneteenth are subtracted, not assumed away.

Holiday Treatment

Memorial Day (Mon May 25) is treated as fully closed — nearly all SGA practices observe it. Juneteenth (Fri Jun 19) is treated as fully closed in the conservative case; if half the network operates that day, add roughly +$310K to the June forecast.

Methodology — Calendar-Aware Forecast

For each target month, the projection sums per-DOW contributions:

Baseline: Prior-year same-month per-DOW weekly network average (e.g., May 2025 Tuesday = $1.72M). Calculated from the YoY weekly series, $300/practice-day threshold applied.
YoY adjustment: Multiplied by 1 + (yoy_4wk_pct/100) — the rolling 4-week network total vs the same 4 weeks last year.
Working day count: Actual count of each DOW in the target month minus federal holidays where practices close.
Monthly total: Σ_DOW (working_days × baseline × yoy_mult)

Holiday assumptions (current run): Memorial Day = closed (May 25), Juneteenth = closed (Jun 19). Both are subtracted from working-day counts. The R8 Calendar comparison model uses the same DOW-count math but pulls the per-DOW R8 averages instead of seasonal baselines — isolating the seasonal-vs-anchored question.

Limitations: Prior-year baseline implicitly carries any one-off events from May/June 2025 (e.g., weather, regional issues). The YoY 4-week multiplier is a single-point trend estimate — if the network's trajectory shifts, the projection should be re-run. New practices added since May 2025 are NOT in the prior-year base, so the projection is conservative for network growth.

Declining

54

65% of practices

Stable

14

17% of practices

Improving

15

18% of practices

January Average vs April Average by Practice

Points above the diagonal are improving; below are declining

Practice Detail

Location ▲	OD ▲	Trend ▲	Nov ▲	Dec ▲	Jan ▲	Feb ▲	Mar ▲	Apr ▲	Change ▲	R8/Day ▲

Ops Director Impact

Daily dollar lost from declining practices in each OD's portfolio. Higher bars indicate ODs whose declining practices are losing the most production. Focus coaching and intervention where the dollar impact is greatest.

Daily Production Lost per Ops Director

Sum of daily losses from declining practices in each portfolio

Ops Director Detail

Ops Director ▲	Declining ▲	Total ▲	% Declining ▲	$/Day Lost ▲	Affected Practices

Highest Dollar Impact

Highest Decline Rate

24-Month Year-over-Year View

Pulls two full years of Velox daily booked production so every week in the most recent year has a same-calendar-week prior-year pair. The 8-week trailing rolling average is overlaid as a tracking baseline that adapts as weeks roll — not a fixed line.

Weeks of Data

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Last 4 Weeks

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network production

Same 4 Weeks PY

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prior year baseline

YoY 4-Week

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vs same period last year

Latest Week

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24-Month Weekly Network Production

Solid: weekly Mon–Fri total · Dashed: trailing 8-week rolling average · drag to pan, scroll to zoom, double-click to reset

Year-over-Year Overlay

Current year (solid) vs prior year same calendar week (dashed) · drag to pan, scroll to zoom, double-click to reset

Day-of-Week Year-over-Year

Same six months as the Day-of-Week Trends tab, paired against last year. Solid bars are current year; lighter bars are prior year. Header shows the latest-month YoY %.

Source-of-Truth Audit — Month to Date

Per-practice booked, completed, scheduled, and budget for the current month. Numbers use the same Gen4 measures as Power BI's Pacing Report — Provider Level Pacing at the full-month grain, so this view should reconcile line-for-line against PBI. Booked = Completed + Scheduled.

Network MTD Booked

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current month

Network MTD Completed

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across all practices

Network Budget

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monthly budget total

Booked vs Budget

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% to budget (booked ÷ budget)

Practices Reporting

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Practice Audit Detail

Practice ▲	OD ▲	Source ▲	Booked Production ▲	Completed Production ▲	Scheduled Production ▲	Budgeted ▲	% to Budget ▲	Days Reported ▲

R8 Rolling Average — Corrected Definition (2026-05-01)

The R8 displayed across this dashboard is the true rolling 8-week network DOW average — the actual sum of network production on Mondays (then Tuesdays, etc.) over the last 8 weeks, divided by 8.

Previously, R8 was computed by summing each practice's 8-week per-DOW average (filtered to days where they actually worked). That overstated the network R8 by 50–170% per DOW because every practice contributed an "if they worked" number even on DOWs where they're typically closed. Friday R8 was 171% too high; Monday–Thursday R8 were 52–57% too high.

True R8 weekly: ~$6.9M (vs the broken $11.4M in data.json)
True R8 monthly equivalent: ~$30M (vs the broken $49M)
Source: yoy.weekly_series last 8 weeks — the actual network DOW totals already in the dataset
Override is applied at the dashboard layer. The next pull-daily.js refresh will rewrite data.json with the broken values; the dashboard re-corrects them on every load. To make this permanent, fix the r8_summary calculation in tools/pacing/pull-daily.js to use rolling network DOW totals instead of per-practice sum.

$300 Threshold

Any practice reporting less than $300 in daily production is excluded from that day's calculation. The $300 threshold represents approximately one doctor-day of minimum production — below that, the practice likely had no provider and the zero would distort the average.

Data Sources

83 practices: Direct VELOX reporting with daily production actuals
7 practices: Estimated from monthly aggregates (no daily VELOX feed)
90 total: Represent the SGA network for pacing analysis
Timeframe: November 2025 through April 2026 (6 months, ~130 business days)

Prediction Model: Linear Regression

The "Trend Predicts" values use ordinary least squares (OLS) linear regression on each DOW's 6-month series. Each month is assigned an index (Nov=1, Dec=2, ... Apr=6) and the regression fits production = slope * month_index + intercept.

May prediction: month_index = 7
June prediction: month_index = 8
Limitation: Linear regression assumes a constant trend. It does not model seasonal cycles. The December anomaly (Monday = $754 due to holidays) skews Monday's slope upward.

The Seasonal Pattern

Six months of data reveal a clear seasonal U-curve:

November: $6.79M/wk — baseline production
December: $6.33M/wk — holiday trough (fewer working days, patient cancellations)
January: $7.06M/wk — benefits renewal peak (new insurance year, pent-up demand)
February: $7.04M/wk — sustained renewal effect
March: $6.94M/wk — beginning to fade
April: $6.34M/wk — returns to December baseline levels

December and April are nearly identical ($6.33M vs $6.34M), confirming this is a seasonal cycle rather than a structural decline. The R8 will always overpredict during trough months because it includes January/February in its 8-week window.

Power BI Direct Integration

The dataset ID b57375c9-d64b-4643-be71-378a520d8f93 was extracted from the Excel connection string. A direct API integration is feasible via:

POST https://api.powerbi.com/v1.0/myorg/datasets/b57375c9-d64b-4643-be71-378a520d8f93/executeQueries
This would eliminate the Excel middleman and enable automated daily refresh
DAX queries can be sent directly — the same measures used in Power BI reports
Requirements: Azure AD service principal with Power BI API permissions (Dataset.Read.All)
Benefit: Dashboard could pull live data instead of requiring manual Excel exports
Status: Proof-of-concept validated via the Power BI bridge at VPS:3050. Full automation requires Azure AD app registration and service principal setup.