Technician efficiency: billable vs. paid hours

The efficiency formula

Labor efficiency = billable hours ÷ paid hours, for a tech or the whole field team over a week or month.

Paid hours are everything on the paycheck, including overtime, shop time, meetings, training and, depending on your policy, paid time off. Billable hours are the hours spent on work you invoice: diagnosing, repairing, installing and maintaining at customers' sites. If you use flat-rate pricing, you can also track flat-rate efficiency, which is the task hours sold in the price book divided by the hours the tech actually clocked on those jobs. Both are useful; they answer different questions.

• Labor efficiency asks: how much of the time we pay for reaches a customer?
• Flat-rate efficiency asks: when the tech is on a job, do they finish within the time the price assumes?
• Utilization, a related measure, asks: how much of the available schedule did we book? See capacity utilization.

Decide whether to include paid time off in paid hours and keep the rule for the whole year. Including it gives a truer cost picture; excluding it gives a cleaner measure of daily productivity. Most companies report efficiency on worked hours (paid hours minus time off) and handle time off in the labor burden calculation.

Why efficiency matters more now

When techs are easy to hire, you can grow by adding trucks. That is not the market today. US construction job openings were 289,000 in September 2026, against 194,000 a year earlier^[2], and average hourly earnings at specialty trade contractors reached $39.05, up 4.7% in a year^[3]. Each hour you pay for costs more than it did, and each new hire is harder to find.

Peak seasons make the problem visible. HVAC techs may be on call and work overtime or irregular schedules during peak heating and cooling seasons^[4], and overtime raises the cost of every hour without adding billable capacity. Under the Fair Labor Standards Act, non-exempt employees must be paid at least time and a half for hours over 40 in a workweek, and hours cannot be averaged across weeks^[5]. An extra billable hour per tech per day from better routing is worth more in July than any overtime shift.

Where the hours go

Drive time deserves the most attention, because it is both large and controllable. Federal rules treat travel from job site to job site during the workday as hours worked^[1], so every unnecessary mile is paid time that produces nothing. A dispatcher who keeps a tech in one zone all day can recover hours a week without anyone working faster.

How to measure it

1. 01

   Get clean time data

   Use field app clock-in and clock-out per job and the payroll record for total paid hours. Timesheets filled in from memory are not good enough.

2. 02

   Define billable consistently

   On-site time on invoiced jobs counts. Drive time does not, even though it is paid. Callbacks and warranty visits do not, because they are not invoiced.

3. 03

   Calculate weekly by tech

   Billable hours ÷ worked hours for each tech, and for the team.

4. 04

   Add the cause data

   Beside each tech's efficiency, show drive minutes per job, parts runs, callbacks and idle time after cancellations.

5. 05

   Compare like with like

   Maintenance techs, service techs and installers have different patterns. Compare techs within each group.

Example: two service techs, one week

Example (fictional): an electrical contractor reviews two service electricians for a week. Both worked 45 hours. Marcus billed 29 hours, an efficiency of 64%. Leah billed 34 hours, 76%.

Example diagnosis: Marcus drove 11.5 hours across 14 jobs, made four supply-house runs and had one callback that took 2.5 hours. Leah drove 7 hours across 15 jobs, made one supply run and had no callbacks. Marcus is not slower on the job; his flat-rate efficiency is actually slightly better than Leah's. His schedule sent him across the county three days in a row, and two of his supply runs were for breakers the office could have identified on the call.

Example fix: the dispatcher assigns Marcus to the north zone for the next month, the office adds panel brand and age to the intake questions, and his truck gets two more breaker types in stock. Four weeks later Marcus averages 31.5 billable hours on the same 45 worked hours, 70%. At an example billing rate of $135 an hour, the extra 2.5 hours a week is worth about $17,500 in revenue a year from one tech, with no extra payroll.

Efficiency in different trades and roles

The formula is universal, but what good looks like depends on the work. Compare techs only with others doing similar work, and expect these patterns.

• Install crews usually show higher labor efficiency than service techs, because a full-day install has one drive in the morning and little travel during the day. Their risk is overrun against quoted hours, so watch flat-rate or quoted-hours efficiency more closely.
• Service and repair techs lose the most to drive time and parts runs. Routing and truck stock matter more for them than speed on the job.
• Maintenance techs on agreement visits can be very efficient when visits are clustered by neighborhood, and very inefficient when they are booked wherever the customer prefers. Group plan visits by zone and season.
• Route-based trades such as pest control and lawn care measure efficiency per stop and per route, where drive time between stops is the main variable and density is everything.
• Apprentices pull down a crew's efficiency while they learn. Track their hours separately so the cost of training is visible and not mistaken for poor performance by the lead tech.

Seasonality also shifts the numbers. In the busiest weeks, efficiency often rises because the schedule is full and gaps are rare, even as callbacks creep up. In shoulder seasons, gaps open and efficiency falls unless the office fills them with maintenance visits and follow-ups on unsold estimates.

Setting targets

Published targets vary widely and rarely share a definition. Build yours from your own data: take each group's best four weeks from last year as the stretch target and the median week as the floor. Review targets each season, because summer HVAC and winter HVAC are different businesses.

Tie the target to the price book. If your prices assume a certain number of billable hours per tech per day, efficiency below that assumption means every job is underpriced. Our revenue per technician guide shows how efficiency, jobs per day and average invoice combine.

Coaching without gaming

Efficiency measures can be gamed. A tech can rush diagnosis, skip a maintenance step or avoid difficult jobs to raise billable hours. Watch for these side effects:

• Rising callbacks or warranty visits after efficiency rises.
• Falling close rates, because rushed techs present fewer options.
• Customer complaints about rushed visits or incomplete work.
• Techs refusing jobs far from home or late in the day.

Report efficiency next to callbacks and close rate, and coach on causes rather than the number. Most of the fixes in this article sit with dispatch, the office and the warehouse, not with the tech. Telling a tech to be more efficient without fixing their schedule only frustrates good people. Pairing efficiency with membership metrics also helps, because maintenance visits that are scheduled well fill otherwise idle hours.

The phone call shapes the visit

Many lost field hours start at the first phone call. A tech who arrives without knowing the equipment brand, its age, the symptoms or the access details spends the first half hour finding out, and often makes a supply run afterwards. A short structured intake on every call, whoever answers it, is one of the cheapest efficiency gains available. It also improves the funnel upstream; see our guide to the service call funnel and to ROI by lead source, since channels that send poorly described jobs cost more in field time.

Review a handful of job tickets each week with the dispatcher and ask one question: what did the tech need to know before arriving that the ticket did not say? Add it to the intake script. Within a few months the information gap that drives most diagnosis delays and parts runs closes on its own.