Fuel costs are up. Jobs are running longer than scheduled. A few clients have complained about response times. The problem is clear – but without data, there's no way to tell whether the issue is routing, scheduling, technician performance, or something else. Field service KPI tracking turns that vague picture into specific, measurable gaps. KPI tracking in field service doesn't require a dashboard of 20 metrics – the right four cover most of what operations managers actually need to make decisions.
On-time completion shows whether scheduling and time estimates hold up in practice. Repeat visits show whether jobs are being prepared and executed correctly the first time. Average job time shows whether technician performance and workload distribution are balanced. Mileage shows whether routes are efficient or whether technicians are covering unnecessary distance between jobs.
Field service KPI improvement starts with visibility into these four – together they cover scheduling quality, execution accuracy, technician efficiency, and operational cost. Planado's Reports section surfaces all of them in one view: completed jobs, on-time rate, individual technician stats, and travel time – without building a separate tracking system.
On-time completion measures the share of jobs finished within the promised time window. A low rate points to one of three operational gaps: time estimates in job templates are too optimistic, schedules are overloaded with back-to-back jobs that leave no buffer, or routing between sites adds travel time that wasn't accounted for at dispatch.
Planado flags problem jobs automatically. Overdue jobs – those not started at their scheduled time – appear in a dedicated section of the dashboard without any manual filtering. Prolonged jobs, running beyond the planned duration, appear in the same place. Managers see exceptions as they happen rather than reviewing a report at the end of day.
Repeat visit rate for technicians measures how often a second trip to the same site was required to complete work that should have been finished the first time. A high repeat visit rate typically traces to missing parts on arrival, a job scope that wasn't clear in the work order, or a technician assigned without the right skills for that job type.
Planado captures why jobs weren't completed through resolution codes – "need parts," "need additional specialist," "client not home" – selected by the technician at job close. Those reasons are logged and visible in the Reports section, which makes repeat visit causes searchable across the full job history rather than buried in individual records.
Average job duration shows whether the time estimates in your templates reflect how long jobs actually take – and whether workload is distributed evenly across the team. A technician consistently finishing well ahead of schedule may be skipping steps. One consistently running over may have a heavier job mix or need additional support.
Planado logs start and finish timestamps on every job automatically. Average duration by technician and by job type is visible in the Reports section – no manual calculation required. Comparing actual duration against the estimated time set in the job template shows where the gap between planning and execution is largest.
High mileage per visit means jobs are sequenced inefficiently – a technician crosses the city twice when both assignments could have been handled in the same area. The cost shows up in fuel and vehicle maintenance, but the root cause is a dispatch decision made without visibility into geography.
Automate mileage tracking field service employees through GPS: when a technician marks "En route" in Planado, the app begins collecting location data and calculates the travel distance and time to the job site. Travel data feeds directly into the job record and becomes part of the performance history without any manual trip logging. Map-based dispatching in Planado shows all active technician positions, so dispatchers assign the nearest qualified available technician at the point of scheduling rather than after the fact.
Planado logs timestamps, travel data, and job outcomes automatically as your team works. See how the metrics look in practice.
KPI tracking in field service through Planado happens as a byproduct of normal operations. Every status change – En route, Started, Finished – logs a timestamp. GPS captures travel distance and route. Resolution codes record outcomes. No one fills in a separate tracking sheet.
Field service performance metrics are available in the Reports section without manual calculation:
Data is exportable via API or CSV for teams that use external BI tools like Tableau, PowerBI, or Google Data Studio.
If your field team's performance data currently lives in spreadsheets or doesn't exist at all, Planado is worth exploring – the metrics collect themselves.
How is on-time completion calculated in Planado?
Planado compares the scheduled start time against the actual start timestamp logged when the technician marks the job as started. Jobs that miss the scheduled window appear automatically in the Overdue section – no manual calculation required. Field service performance metrics like this are available in the Reports section.
What counts as a repeat visit?
A repeat visit occurs when a second job is created for the same client site to complete work from a previous visit. Tracking repeat visit rate for technicians in Planado means reviewing resolution codes on closed jobs – unsuccessful resolutions with reasons like "need parts" or "need specialist" identify the visits that required a follow-up.
Can I see KPIs per technician, not just for the whole team?
Yes – Planado's Reports section breaks down field service KPI data by an individual technician. On-time rate, completed jobs, average duration, and failure reasons are all available at the technician level, which makes it straightforward to identify where performance differs across the team.
Does Planado track mileage automatically?
Yes – when a technician marks "En route," Planado begins collecting GPS location data and calculates travel distance and time to the job site. Travel records are attached to each job automatically, with no manual input from the technician or dispatcher.
