How to Track Technician Utilization Rate in a Home Service Business

Technician utilization rate is the percentage of a tech's available work hours spent on revenue-generating activity. It is one of the most direct measures of field operations efficiency, and most home service businesses have never calculated it. It sits alongside the other technician performance metrics that owners typically under-track. The companies that do almost always find it lower than expected, with a recoverable gap worth five figures in annual revenue.

Defining Technician Utilization

Utilization starts with a definition of billable versus non-billable time.

Billable time is time spent on a customer job: travel to the job, time on-site diagnosing and working, and job close-out documentation done at the site. Non-billable time is everything else: shop time in the morning before dispatch, drive between jobs beyond a reasonable threshold, lunch and personal time, admin work done at the office, training, and time waiting on parts or customer access.

The formula is straightforward. Billable hours divided by total paid hours equals utilization rate. A tech paid for 8 hours per day who logs 5 hours of billable time has a 62.5% utilization rate.

The target for well-run field service operations is 70-80%. Below 65% consistently means there are structural issues in routing, scheduling density, or administrative burden. Above 85% is usually a sign that techs are cutting corners on documentation, skipping diagnostics, or driving unsafely to maintain the number.

Text Clint: "what is the average tech utilization this week by technician?"

How to Calculate It from Your CRM Data

The data required for utilization rate exists in every major CRM. What differs is how much manual work is needed to surface it.

ServiceTitan tracks technician utilization natively. The dashboard includes billable hours, drive time, and idle time per tech. You can pull a utilization report by tech by date range without any manual calculation. This is the best out-of-the-box utilization tracking available in field service software.

Housecall Pro does not have a native utilization report. The data to calculate it manually is in the job timestamps: job created, job started (tech on-site check-in), job completed. Export the job list by tech for the period, calculate total on-site hours (completed minus started), add travel time from the dispatch log if available, and divide by total paid hours for the period. It takes 20-30 minutes to build a monthly utilization number in a spreadsheet.

Jobber is similar to Housecall Pro. Job timestamps are stored and exportable. Jobber's time-tracking feature (enabled separately) logs time per tech per job and is the cleanest way to get to utilization. Without time tracking enabled, you are calculating from job duration estimates, which are less accurate. A custom report or export is required; there is no native utilization dashboard.

Workiz provides partial utilization data. Job duration and dispatch records are available. Drive time tracking depends on whether the mobile app GPS tracking is enabled and how consistently techs use it. The numbers are directionally useful but less precise than ServiceTitan's native tracking.

Text Clint: "show me the total on-site hours logged by tech for the last 30 days."

What Causes Low Utilization

Low utilization is almost always caused by one of four things, often in combination.

Geographic route gaps are the most common cause. Techs are driving 30-45 minutes between jobs because the schedule was built around availability, not geography. A tech who does a job in the north part of the service area at 8am and another in the south at 11am has a 45-minute drive in the middle that produces no revenue and costs fuel. Route optimization that groups jobs by zone cuts this gap significantly; see how to improve route density for the playbook. The standard target for inter-job drive time is 15-25 minutes.

Excessive administrative burden is the second cause. If techs are spending 45-60 minutes per day on paperwork, invoicing, or job note entry, those hours show up as non-billable. Moving administrative tasks to the field (documenting during or immediately after the job from a mobile app) reduces the batch at day-end and keeps techs in front of customers longer.

Too many short jobs with long drives is a specific version of the routing problem. A $95 drain clearing that takes 45 minutes on-site preceded by a 40-minute drive has a utilization contribution of less than 45 minutes on a trip that consumed 85 minutes. Batching short jobs in the same area and building longer jobs (water heater swaps, major repairs) around them improves average utilization per trip.

Under-scheduling is the fourth cause. A tech with two jobs on the calendar for a day has structural idle time between them regardless of how well the route is built. Consistent under-scheduling indicates a capacity utilization problem: you have more field labor than you have work to fill it. The fix is either more lead volume or reduced field headcount, and the when to hire next technician framework lays out the decision.

Text Clint: "what is our average drive time between jobs per tech this month?"

The Revenue Impact of a 10-Point Improvement

The utilization calculation directly translates to dollars.

Baseline: 3 techs, each paid for 40 hours per week, at a $90 billable hourly rate. Current utilization is 65%, meaning each tech produces 26 billable hours per week. Total weekly billable output: 78 hours. Weekly revenue at $90/hour: $7,020.

After a 10-point utilization improvement to 75%: each tech produces 30 billable hours per week. Total weekly billable output: 90 hours. Weekly revenue at $90/hour: $8,100.

The difference: $1,080 per week, $4,320 per month, $56,160 per year. From the same 3 techs, the same payroll, the same trucks. The improvement comes entirely from filling the gap between paid hours and billable hours.

The math scales with team size and billing rate. For 5 techs at $110/hour, a 10-point utilization improvement produces $91,520 in additional annual revenue.

Text Clint: "what would our revenue be if tech utilization increased by 10 points based on our current billing rate?"

Utilization vs. Schedule Fill Rate

These two metrics measure different things and are frequently confused.

Schedule fill rate is the percentage of available schedule slots that are booked. A company with 40 available hours per tech per week and 38 hours of jobs on the calendar has a 95% schedule fill rate.

Utilization rate is the percentage of those booked hours that produce billable output. If the schedule is 95% full but jobs run long, drive time between jobs is high, and techs are doing 90 minutes of admin per day, utilization might still be 65%.

A high fill rate with low utilization is an efficiency problem. The business looks busy but is leaving money on the table through route gaps and admin overhead. A low fill rate with high utilization is a volume problem. The techs are efficient when they have work, but there is not enough work to fill the week.

The combination to optimize for is 85-95% schedule fill rate with 70-80% utilization. That represents a fully loaded, efficiently operating field team. Getting fill rate right without tracking utilization means you may be scheduling for appearance while the actual revenue-generating hours stay low. Roll it up with the rest of your operational view in a home service business KPI dashboard.

Text Clint: "what is our schedule fill rate versus actual utilization rate this week by tech?"

How Clint Calculates Utilization From Your CRM

Clint connects to your CRM and answers utilization questions in plain language. When you ask "what is my average tech utilization this week by technician?", it pulls job timestamp data, calculates billable hours per tech, compares it to scheduled hours, and returns the utilization rate per tech for the period.

That same connection lets you ask where the gap is coming from: drive time between jobs, short on-site time on jobs that should run longer, or jobs with no time logged at all. The output surfaces the coaching signal, not just the number. A tech at 60% utilization with long inter-job drives has a routing problem. A tech at 60% utilization with short on-site times relative to invoice has a documentation problem. The right fix depends on which one it is, and the broader read is in job profitability for home services.

Sources

• ServiceTitan Field Operations Benchmarks Report (2024)
• Aberdeen Group, Field Service Management Benchmark Study (2024)
• Software Advice Field Service Management User Research (2025)
• US Bureau of Labor Statistics, Occupational Employment, Installation, Maintenance, and Repair Occupations (2024)