AmpleExpress Market Value Optimization Score (MVOS) methodology
A 0–100 score that estimates how strong a local market is for software-driven efficiency and revenue gains in a specific trade. It blends market signals with trade benchmarks to create a comparable signal across cities.
Data sources
Four signal types power every MVOS score.
Business density
Establishment and payroll indicators that describe how active a local market is.
Wage signals
Pay trends that influence revenue leverage and cost pressure for service teams.
Demand signals
Software-intent indicators normalized by trade and location.
Trade benchmarks
Field-service priors used to ground operational opportunity by trade type.
How MVOS is calculated
MVOS combines four subscores, each normalized 0–100 within a trade. Market Density uses local business activity signals, Wage Advantage uses wage trends to estimate ROI leverage, Demand Signal uses normalized intent, and Operational Opportunity blends trade priors with local signals to estimate uplift potential. We apply a dispersion curve to make differences around the trade median more visible, and include a small spread adjustment so cities with sharper subscore contrasts stand out.
Normalization and comparability
Subscores are percentile-ranked within each trade to make city-to-city comparisons fair. This means a plumbing score in Des Moines is directly comparable to plumbing in Phoenix, but not directly comparable to HVAC in Phoenix.
Limitations
MVOS is an estimate based on market datasets and proxies. We use fallbacks when a local dataset is missing, and update the score on a regular cadence (monthly or quarterly depending on data refresh). MVOS v1 is not an official government rating and should be used as directional guidance.
Versioning
Current version: MVOS v1. Future versions may refine industry mappings, demand signals, or weighting as new datasets become available.