{"id":6585,"date":"2026-03-16T13:12:57","date_gmt":"2026-03-16T11:12:57","guid":{"rendered":"https:\/\/shifton.local\/blog\/field-service-kpis\/"},"modified":"2026-03-16T13:12:57","modified_gmt":"2026-03-16T11:12:57","slug":"field-service-kpis","status":"publish","type":"post","link":"https:\/\/shifton.com\/service\/blog\/field-service-kpis\/","title":{"rendered":"The 12 Field Service KPIs Every Operations Manager Should Track"},"content":{"rendered":"
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<\/p>\n

Field Service Management<\/span>
\nField Service KPIs<\/span>
\nFSM Metrics<\/span>
\nTechnician Performance<\/span>
\nService Operations<\/span><\/div>\n

<\/p>\n

Most field service teams collect far more data than they act on. The problem is not a lack of metrics \u2014 it is tracking the wrong ones, or tracking too many to focus on any of them. This guide covers twelve field service KPIs that directly connect to operational performance, customer satisfaction, and profitability \u2014 with formulas, benchmarks, and practical advice on what to do when each number moves the wrong way.<\/p>\n

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Whether you manage five technicians or five hundred, these are the metrics that operations leaders consistently identify as most predictive of business health. The global FSM market is growing from $5.1 billion in 2025 to a projected $9.17 billion by 2030, according to MarketsandMarkets<\/a> \u2014 a pace driven in large part by enterprises that link operational KPIs directly to their software. Shifton’s Field Servive workforce reporting module<\/a> surfaces these metrics automatically from GPS clock-ins and job completions, without manual entry.<\/p>\n

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What Are Field Service KPIs \u2014 and Why the Distinction Matters<\/h2>\n

A metric is any measurable data point \u2014 average drive time, jobs completed this week, parts used per job. A KPI is a metric you have deliberately chosen as critical to your business goals and track consistently over time to guide decisions. Every KPI is a metric, but not every metric deserves to be a KPI.<\/p>\n

The practical test: if your team would not change their behaviour based on a number moving, it should not be a KPI. Start with five to seven, tied to your current biggest operational challenge, and track those with discipline before expanding. Field service KPIs fall into four natural categories \u2014 understanding which category a metric belongs to tells you who owns it and when to act on it.<\/p>\n

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Operational Efficiency<\/h3>\n

First-time fix rate, mean time to repair, technician utilization, SLA compliance.<\/p>\n<\/div>\n

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Customer Experience<\/h3>\n

CSAT score, NPS, response time, complaint resolution rate.<\/p>\n<\/div>\n

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Financial Performance<\/h3>\n

Job profitability, cost per service call, revenue per technician, overtime ratio.<\/p>\n<\/div>\n

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Workforce & Scheduling<\/h3>\n

Schedule adherence, jobs per technician per day, travel time ratio.<\/p>\n<\/div>\n<\/div>\n

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The 12 Field Service KPIs That Actually Move the Needle<\/h2>\n

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KPI 01<\/div>\n

First-Time Fix Rate (FTFR)<\/h3>\n<\/div>\n

The percentage of service calls resolved completely on the first visit, without a return trip. This is widely considered the single most important operational KPI in field service \u2014 it captures efficiency, preparedness, and customer satisfaction in one number. According to TSIA’s field service KPI research<\/a>, FTFR is one of the metrics most consistently correlated with customer retention and contract renewal across industries.<\/p>\n

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Formula<\/div>\n
(Jobs resolved on first visit \u00f7 Total jobs) \u00d7 100<\/div>\n<\/div>\n
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Industry benchmark<\/div>\n
Industry median ~72% \u00b7 Best-in-class 80%+<\/div>\n<\/div>\n<\/div>\n

How to improve it:<\/strong> Low FTFR almost always traces to one of three causes \u2014 technicians arriving without the right parts, insufficient job information in the work order, or mismatched skill assignments. Connecting your inventory management<\/a> directly to the dispatch workflow so parts are verified before job assignment is the single highest-impact fix.<\/p>\n<\/div>\n

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KPI 02<\/div>\n

Mean Time to Repair (MTTR)<\/h3>\n<\/div>\n

The average time from when a technician begins work on a job to when it is marked complete. MTTR measures what happens on-site \u2014 not how long it took to get there. It is the primary diagnostic for technician technical efficiency and is most useful when segmented by job type and individual technician rather than viewed as a single aggregate number.<\/p>\n

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Formula<\/div>\n
Total repair time \u00f7 Number of repairs completed<\/div>\n<\/div>\n
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Review frequency<\/div>\n
Weekly by technician and by job type<\/div>\n<\/div>\n<\/div>\n

How to improve it:<\/strong> High MTTR by job type reveals training gaps. High MTTR by individual technician points to a skill mismatch in dispatch assignments. Structured digital job forms<\/a> with pre-populated checklists reduce on-site decision time by ensuring technicians follow a consistent diagnostic sequence every time rather than figuring it out on arrival.<\/p>\n<\/div>\n

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KPI 03<\/div>\n

Average Response Time<\/h3>\n<\/div>\n

The average time between when a service request is received and when a technician is on-site. Response time is one of the most customer-visible KPIs in field service. In industries like plumbing and electrical, a delay does not just frustrate customers \u2014 it often makes the underlying problem worse. According to Mordor Intelligence<\/a>, scheduling, dispatch, and route optimization is the leading solution segment in the FSM market \u2014 demand driven precisely by the pressure to bring response times down.<\/p>\n

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Formula<\/div>\n
Total response time \u00f7 Number of service calls<\/div>\n<\/div>\n
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Best practice target<\/div>\n
Under 30 min critical \u00b7 2\u20134 hrs standard<\/div>\n<\/div>\n<\/div>\n

How to improve it:<\/strong> Smart dispatch that assigns the nearest qualified technician \u2014 not just the next available one \u2014 produces the biggest single-step reduction. Geo-routing has helped operations cut average response from three hours to under 90 minutes by switching to proximity-based assignment. See our guide on field service dispatch software<\/a> for how automated assignment works in practice.<\/p>\n<\/div>\n

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KPI 04<\/div>\n

Technician Utilization Rate<\/h3>\n<\/div>\n

The percentage of a technician’s working hours spent on billable service activities, as opposed to travel, idle time, and admin. This is the primary indicator of whether scheduling is converting labor capacity into revenue. Most field service teams run at 60\u201370% utilization when 85%+ is achievable \u2014 the gap is almost always excess travel time and manual administrative work that has not yet been automated.<\/p>\n

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Formula<\/div>\n
(Billable hours \u00f7 Total working hours) \u00d7 100<\/div>\n<\/div>\n
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Industry benchmark<\/div>\n
60\u201370% average \u00b7 85%+ optimized teams<\/div>\n<\/div>\n<\/div>\n

How to improve it:<\/strong> GPS-verified clock-ins, automated route chaining, and mobile job completion replace end-of-day paper reconciliation \u2014 each adding measurable billable hours back into a technician’s day. Teams that implemented scheduling dashboards have reported 18% utilization gains and 25% more billable hours per month without adding headcount.<\/p>\n<\/div>\n

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KPI 05<\/div>\n

SLA Compliance Rate<\/h3>\n<\/div>\n

The percentage of jobs completed within the response and resolution windows committed to in service agreements. SLA compliance is both a customer satisfaction metric and a financial one \u2014 breached SLAs trigger penalties, erode renewals, and are the most reliable early warning of systemic dispatch problems. Teams that move from reactive to proactive SLA monitoring consistently push compliance from the 60s into the 90s.<\/p>\n

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Formula<\/div>\n
(Jobs within SLA \u00f7 Total jobs) \u00d7 100<\/div>\n<\/div>\n
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Target<\/div>\n
90%+ standard \u00b7 95%+ for service contracts<\/div>\n<\/div>\n<\/div>\n

How to improve it:<\/strong> SLA compliance requires proactive alerts \u2014 warnings that fire before a window closes, not notifications that a breach already happened. Shifton’s instant alerts module<\/a> flags at-risk jobs automatically, giving dispatch 30\u201360 minutes to act before an SLA is broken rather than reacting after the fact.<\/p>\n<\/div>\n

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KPI 06<\/div>\n

Customer Satisfaction Score (CSAT)<\/h3>\n<\/div>\n

A post-service rating measuring customer satisfaction with the service experience. Aberdeen Group identifies customer satisfaction as the leading measure of success in field service management, cited by 68% of high-performing organisations ahead of pure operational efficiency. Organisations that actively track KPIs show 20% higher CSAT than those that do not, alongside 15% higher technician productivity. Read our overview of field service operations<\/a> for how the full workflow from dispatch to sign-off connects to customer experience.<\/p>\n

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Formula<\/div>\n
(Positive responses \u00f7 Total responses) \u00d7 100<\/div>\n<\/div>\n
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Review frequency<\/div>\n
After every job \u00b7 Trend monthly<\/div>\n<\/div>\n<\/div>\n

How to improve it:<\/strong> CSAT is most sensitive to arrival time accuracy and technician communication. Automated ETA notifications \u2014 «your technician is 20 minutes away» \u2014 consistently lift scores even when wait time itself has not changed. Customer signature capture and job completion proof reduce post-service disputes that drag scores down.<\/p>\n<\/div>\n

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KPI 07<\/div>\n

Travel Time per Job<\/h3>\n<\/div>\n

The average time a technician spends traveling between jobs. Excess travel is the most visible sign of inefficient scheduling and the most direct lever for reducing overtime without cutting headcount. McKinsey research confirms that reducing daily travel time by even one hour can free capacity for one to two additional jobs per technician per day \u2014 a direct revenue impact without adding staff.<\/p>\n

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Formula<\/div>\n
Total travel time \u00f7 Number of jobs completed<\/div>\n<\/div>\n
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Improvement target<\/div>\n
15\u201325% reduction with route optimization<\/div>\n<\/div>\n<\/div>\n

How to improve it:<\/strong> Route chaining \u2014 grouping nearby jobs in sequence rather than by arrival time alone \u2014 is the primary lever. Teams using location-aware scheduling have reduced travel time per job by 15\u201325%, enabling one or two additional completions per technician per day. Our field service route optimization guide<\/a> covers the mechanics of intelligent job sequencing in detail.<\/p>\n<\/div>\n

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KPI 08<\/div>\n

Schedule Adherence Rate<\/h3>\n<\/div>\n

The percentage of jobs where technicians arrive within the promised service window. Schedule adherence is a direct driver of CSAT \u2014 customers who experience on-time arrivals rate their service significantly higher regardless of technical complexity. Customers who experience missed windows rarely complain directly; they quietly switch providers. The signal shows up in renewal rates, not complaint logs.<\/p>\n

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Formula<\/div>\n
(Jobs within arrival window \u00f7 Total jobs) \u00d7 100<\/div>\n<\/div>\n
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Target<\/div>\n
85%+ \u00b7 Depends on window width<\/div>\n<\/div>\n<\/div>\n

How to improve it:<\/strong> Low schedule adherence is usually a buffer problem \u2014 windows are too tight or job duration estimates are inaccurate. Analysing actual vs. estimated completion times by job type and updating scheduling templates accordingly is the most reliable fix. Real-time GPS visibility lets dispatch adjust proactively when a job runs long, rather than reacting after the next appointment is already missed.<\/p>\n<\/div>\n

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KPI 09<\/div>\n

Jobs Completed per Technician per Day<\/h3>\n<\/div>\n

The average number of jobs each technician closes in a working day. This is the core productivity metric that tells you whether scheduling is converting available hours into completed service efficiently. Four or more jobs per day is a broadly cited healthy benchmark, though the right target varies significantly by industry and average job duration. Track your own baseline trend over time rather than chasing a single industry number.<\/p>\n

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Formula<\/div>\n
Total jobs \u00f7 (Technicians \u00d7 Working days)<\/div>\n<\/div>\n
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Note<\/div>\n
Benchmark against your own baseline first<\/div>\n<\/div>\n<\/div>\n

How to improve it:<\/strong> Compare this metric against travel time per job. Low jobs per day with low travel time means the issue is job duration \u2014 look at training and tooling. If travel time is high, the issue is routing. If both are acceptable, look at admin time \u2014 paperwork and manual reporting that mobile job completion can eliminate entirely.<\/p>\n<\/div>\n

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KPI 10<\/div>\n

Overtime Hours Ratio<\/h3>\n<\/div>\n

The percentage of total labor hours worked as overtime. A creeping overtime ratio signals that scheduling is unbalanced \u2014 some technicians are overloaded while others sit idle \u2014 or that job duration estimates are too optimistic, causing workdays to run long. Forecasted scheduling reduces overtime by 10\u201320% in most operations by matching capacity to demand in advance rather than reacting to it. The benefits of field service scheduling software<\/a> are most tangible here: planned vs. actual hour comparisons make overtime trends visible before they become a payroll crisis.<\/p>\n

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Formula<\/div>\n
(Overtime hours \u00f7 Total hours worked) \u00d7 100<\/div>\n<\/div>\n
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Target<\/div>\n
Under 10% \u00b7 Flag if trending above 15%<\/div>\n<\/div>\n<\/div>\n

How to improve it:<\/strong> Workload balancing across technicians \u2014 visible in a planning dashboard \u2014 prevents the pattern of one technician finishing late while another sits idle two zones away. Reviewing planned vs. actual hours weekly catches imbalances before they compound into sustained overtime.<\/p>\n<\/div>\n

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KPI 11<\/div>\n

Job Profitability<\/h3>\n<\/div>\n

Gross profit per job after accounting for labor, parts, and direct overhead. Tracking job profitability by job type, technician, and customer reveals which work actually makes money \u2014 and which looks busy but erodes margin. A healthy target for most field service operations is 30\u201350% gross profit margin per job. According to Fortune Business Insights, the FSM market’s rapid growth is partly driven by enterprises focusing on profitability analytics \u2014 moving from volume metrics to margin-level visibility.<\/p>\n

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Formula<\/div>\n
(Revenue \u2212 Costs) \u00f7 Revenue \u00d7 100<\/div>\n<\/div>\n
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Benchmark<\/div>\n
30\u201350% gross margin \u00b7 Review monthly<\/div>\n<\/div>\n<\/div>\n

How to improve it:<\/strong> If repeat visits are adding unbilled labor cost, that directly crushes margin. Accurate invoicing tied to actual work order data \u2014 including all materials used \u2014 is the foundation. Shifton’s invoicing module<\/a> auto-generates invoices from completed work orders with labor, materials, and customer signatures pre-populated, eliminating billing lag between field and office.<\/p>\n<\/div>\n

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KPI 12<\/div>\n

Repeat Service Call Rate<\/h3>\n<\/div>\n

The percentage of completed jobs that generate a return visit within 30 days for the same issue \u2014 the inverse of FTFR at the job level. Every repeat trip means double the labor and travel cost on a job already quoted once. A high repeat rate signals that jobs are being closed without genuinely resolving the underlying problem, whether due to misdiagnosis, incomplete repair, or missing parts on the first visit.<\/p>\n

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Formula<\/div>\n
(Repeat calls within 30 days \u00f7 Total jobs) \u00d7 100<\/div>\n<\/div>\n
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Target<\/div>\n
Under 5% \u00b7 Best-in-class under 2%<\/div>\n<\/div>\n<\/div>\n

How to improve it:<\/strong> Track repeat calls by technician and by job type separately. Patterns by technician point to training gaps. Patterns by job type point to systemic issues \u2014 wrong parts specification, insufficient diagnosis time in the job template, or a recurring equipment fault that needs a different resolution approach.<\/p>\n<\/div>\n

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How to Build a KPI Tracking System That Actually Works<\/h2>\n

Identifying the right KPIs is only half the work. Collecting clean data, reviewing it consistently, and acting on it is the other half \u2014 and where most operations fall short. Grand View Research projects the FSM market reaching $11.78 billion by 2030, with analytics and reporting cited as one of the primary drivers of platform adoption. The six practices below separate teams that use KPIs to drive improvement from teams that build dashboards no one reads.<\/p>\n

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Start with 5\u20137 core KPIs<\/h3>\n

More metrics create noise. Pick KPIs tied to your current biggest challenge and track those consistently before expanding the set.<\/p>\n<\/div>\n

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Automate data collection<\/h3>\n

FSM software that captures clock-ins, job completions, and signatures automatically gives you data you can trust without extra forms for technicians to fill in.<\/p>\n<\/div>\n

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Review weekly, not monthly<\/h3>\n

Monthly reviews catch problems after they have compounded. Weekly reviews on five core KPIs give enough lead time to adjust routing or dispatch rules before a bad trend becomes a bad quarter.<\/p>\n<\/div>\n

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Segment by technician, region, job type<\/h3>\n

Aggregate numbers hide the signal. A 72% FTFR looks fine until you see one technician at 55% and another at 91%. Segmentation turns a number into a specific action.<\/p>\n<\/div>\n

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Make KPIs visible to the team<\/h3>\n

Dashboards only managers see do not drive behaviour change. Technicians who see their own FTFR and schedule adherence consistently outperform those without performance visibility.<\/p>\n<\/div>\n

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Pre-define your response triggers<\/h3>\n

Define in advance: if FTFR drops below X, review parts staging. If travel time rises above Y, audit routing rules. Pre-defined responses make KPI tracking operational, not just informational.<\/p>\n<\/div>\n<\/div>\n

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The bottom line:<\/strong> You do not need to track all twelve KPIs from day one. Pick the four or five that match your current biggest operational challenge and track them with discipline. As those numbers improve, expand your KPI set. The best-performing field service teams run on a small number of metrics tracked consistently \u2014 not a large dashboard reviewed infrequently.<\/p>\n<\/div>\n

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Frequently Asked Questions<\/h2>\n
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What is the most important KPI in field service management?<\/h3>\n

First-time fix rate (FTFR) is most consistently cited as the single most important KPI. It captures operational efficiency, preparedness, and customer satisfaction in one number. The industry median sits around 72%, with best-in-class organisations reaching 80% or above.<\/p>\n<\/div>\n

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What is a good first-time fix rate benchmark?<\/h3>\n

The industry median is approximately 72%. Anything above 75% is considered strong, and best-in-class operations in predictable service environments reach 80\u201389%. Below 65% almost always means parts availability, job information quality, or dispatch skill-matching needs attention.<\/p>\n<\/div>\n

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What is the difference between a field service KPI and a metric?<\/h3>\n

A metric is any measurable data point. A KPI is a metric you have intentionally chosen as critical to a business goal and track consistently to guide decisions. The practical test: if your team would not change behaviour based on a number moving, it should not be a KPI.<\/p>\n<\/div>\n

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How do I improve technician utilization rate?<\/h3>\n

Most teams run at 60\u201370% when 85%+ is achievable. The gap is almost always excess travel and manual admin. Location-aware dispatch, GPS-verified clock-ins, and mobile job completion forms each add billable hours back into a technician’s day without extending working time.<\/p>\n<\/div>\n

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How many field service KPIs should I track?<\/h3>\n

Start with five to seven. More than that creates noise and dilutes focus. Choose KPIs that are directly actionable, tied to your current biggest challenge, and measurable with data you can realistically collect. Expand the set incrementally as performance improves.<\/p>\n<\/div>\n

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What is a good SLA compliance rate for field service?<\/h3>\n

A target of 90%+ is standard for most service operations. Teams with formal contracts typically target 95%+. Below 80% is a serious warning \u2014 it usually means windows are unrealistically tight, job durations are under-estimated, or there are no proactive alerts before windows close.<\/p>\n<\/div>\n

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How do field service KPIs connect to customer satisfaction?<\/h3>\n

First-time fix rate, response time, and schedule adherence are the three operational KPIs most directly linked to CSAT. When technicians arrive on time, resolve the issue on the first visit, and respond quickly, satisfaction scores rise regardless of technical complexity. Aberdeen Group identifies customer satisfaction as the primary measure of field service success, ahead of pure operational efficiency.<\/p>\n<\/div>\n<\/div>\n

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Field Service Management Software<\/div>\n
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Track Every KPI Automatically \u2014 Without the Spreadsheets<\/span><\/h2>\n

Shifton captures FTFR, travel time, SLA attainment, utilization, and overtime from GPS clock-ins and job completions automatically. Start free, add modules as you need them \u2014 $5\/employee\/month each. Up to 55 days free trial, no card required.<\/p>\n<\/div>\n

\ud83d\ude80 Start Free<\/a>
\n\ud83d\udcc5 Book a Live Demo<\/span><\/a><\/p>\n

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