Step 1
Ask
Validate whether the pricing model reflected field reality.
Pricing audit question
← Projects
Interactive project report
Finding margin in the field.
We started with a pricing question: does Osprey's estimating model reflect operational reality? When Aspire's pricing logic proved too opaque to audit directly, we pivoted to the better question: which maintenance jobs actually create margin, and why?
1QuestionPricing vs reality
2ConstraintOpaque estimates
3PivotMargin drivers
4OutputOperating playbook
00 / brief
The useful work was not one model. It was a translation layer.
The capstone connected messy field-service records to decisions that branch leaders, account owners, and crew managers could act on. It turned a blocked pricing audit into a profitability-driver study.
Step 2
Hit a wall
Aspire's estimating logic was too opaque to audit directly from the available extracts.
Black-box constraint
Step 3
Pivot
Analyze historical maintenance jobs to learn which property, labor, and template traits created margin.
Profitability-driver study
Step 4
Engineer
Clean branch/property terminology, impute missing estimates, and create bed-to-turf and variance features.
Analysis-ready model
Step 5
Recommend
Translate driver signals into template calibration, training, account targeting, and future dashboard needs.
Strategic blueprint
01 / data spine
Five Aspire exports became the soil profile.
The page makes the raw analytical foundation legible without exposing confidential exports. Tap a dataset to open its contribution to the study.
Tap a dataset to expand it in place
02 / analysis lab
Read the margin system without losing your place.
The analysis treats profitability like a landscape plan: property shape, labor, materials, management, and templates each change where margin can grow. Every card keeps the context, signal, action, and operating implication together.
Pick a lens, swipe for more
5 total
property physics
Bed-to-Turf Morphology
Sixteen bed-to-turf categories helped explain why physical landscape shape changes margin. Turf-heavy jobs were more mechanizable; bed-heavy jobs pulled work back toward manual labor.
morphology buckets
16
Draft peak signal: about 72% gross margin.
Signal
Small bed / large turf configurations reached the strongest margins, with draft materials citing peaks around 72%.
Action
Prospect and price with landscape morphology in mind, especially when large bed areas create hidden labor demand.
Stronger patternSmall bed / large turfMore mechanizable recurring maintenance.
Watch patternBed-heavy propertiesManual labor can hide inside the scope.
Decision use
Use property shape as an estimating and renewal-pricing variable.
03 / findings
The work made margin visible without exposing the raw client data.
These are the public-safe headline findings from the draft project materials. The page deliberately avoids employee-level rankings and property-level confidential detail.
72%
Peak margin signal
Small bed / large turf jobs produced the strongest profitability pattern in the draft analysis.
63% vs 35%
Account-owner spread
The top and bottom account-owner groups showed a wide average gross-margin gap.
16
Bed-to-turf categories
Engineered property morphology segments made landscape complexity visible.
5
Aspire datasets
The project connected profit, work-ticket, opportunity, tenure, and time-entry records.
04 / next dashboard
The next screen should help managers decide, not just admire charts.
This illustrative model shows the recommended dashboard direction: combine route density and true labor burden with the capstone's existing profitability-driver work.
Decision cockpit
Choose an operating posture, then read one decision signal at a time.
Route density
58%
Labor burden
24%
Try another signal below
Current signal
GP per man-hour
58
Use this to compare different properties on a common unit economics basis.
GP per man-hour
58
Template confidence
55
Margin signal
63
05 / strategy
Strategic recommendations, translated for the field.
The capstone's value is in converting analytical signals into habits: who to target, what to reprice, which templates to tune, and where to build the next BI layer.
Target the right landscape shapes
Use bed-to-turf analysis in sales and renewal decisions. Large turf footprints can support mechanized efficiency, while large bed areas need stronger labor assumptions or complexity pricing.
Turn people variance into training
Account-owner and crew-leader differences should become playbook material: what top performers do differently, how they scope work, and how they keep labor close to estimate.
Audit Aspire templates continuously
Treat template performance as a feedback loop. Low-performing templates need production-rate adjustments, better cost assumptions, or retirement.
Build the next dashboard around field decisions
The next BI layer should not simply report margin. It should help managers decide where to sell, what to reprice, which routes to densify, and which teams need support.
06 / handoff
What the capstone could prove, and what the next model should add.
The completed work answered the maintenance-job profitability question. The next phase should expand the model into route density, true labor cost, enhancement mix, and production-rate calibration.
Route density
Add geospatial views for windshield time, stops per mile, and route cluster health.
Labor burden
Move beyond raw wage cost by including taxes, workers' compensation, fuel, equipment, and overhead recovery.
GP per man-hour
Compare properties of different sizes through a unit-economics metric that equalizes scope.
Enhancement take-rate
Pair recurring maintenance durability with higher-margin enhancement work without disrupting base service.
Production-rate calibration
Reverse-engineer Aspire template rates from actual time entries and property takeoff measurements.
Publication caveats
- The raw Aspire exports are not in the repository; the page uses draft report materials and the AI enhancement brief.
- The public-facing page avoids employee-level rankings, client-identifying examples, and confidential raw-data exposure.
- External research is capped at December 31, 2024; later Osprey acquisition news is intentionally excluded from the project narrative.
- The original capstone focused on maintenance jobs, so enhancement revenue and full portfolio mix are framed as next-step dashboard needs.
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