First-Time Fix Rate

The first-time fix rate (FTFR) is the percentage of field service jobs that are resolved on the technician's first trip, without requiring a second visit to finish the repair. It is one of the most closely watched indicators in field service because it sums up, in a single number, how well the whole chain works: diagnosis, spare parts, scheduling and training.

The formula is straightforward: FTFR = (orders resolved on the 1st visit / total orders) x 100. If 78 out of every 100 service calls are closed without a return trip, the FTFR is 78%. The flip side (repeat visits) is exactly what the indicator aims to minimize, because each return visit adds fuel, technician hours and a customer who waits longer.

As an operational metric, FTFR is the kind of data that lives on a service dashboard: it is cross-referenced with SLA, MTTR and satisfaction to understand the health of the operation. Consolidating those numbers from the CRM and field systems into a single reliable view is part of what an analytics layer like Metrix solves.

Why the first-time fix rate matters so much

In any field service operation (maintenance of industrial equipment, home appliances, elevators, medical devices, HVAC), every technician visit costs real money: travel, per diem, labor hours and, sometimes, a customer's piece of equipment sitting idle. When a job is not resolved on the first trip and someone has to go back, that cost doubles or triples, and customer trust erodes. That is why FTFR works as a gauge of efficiency: a high number means the technician arrived with the right diagnosis, the right spare part and the necessary training to close the case in one go.

The indicator also has a cascading effect on other metrics. A low FTFR drags down satisfaction (CSAT and NPS), stretches repair times (MTTR) and usually means missing the committed SLAs. That is why improving first-visit resolution is one of the highest-return levers in a service operation.

What affects the first-time fix rate

The main factors that determine whether a job is closed on the first attempt are:

• Prior diagnosis: understanding the fault well before dispatching the technician, ideally with equipment data or phone triage.
• Spare parts availability: having the technician carry the right part in the van, or get quick access to it, avoids one of the most frequent causes of return visits.
• Training and skills: assigning the technician with the right specialty for the type of equipment.
• Complete order information: asset history, model, warranty and notes from previous visits available in the work order.
• Tools and documentation: manuals, schematics and special tools on hand at the moment they are needed.

A concrete example in Argentina

A refrigeration equipment maintenance company serving supermarkets in the Buenos Aires metropolitan area (AMBA) was measuring an FTFR of 64%. When it analyzed the return visits, it found that most happened because the technician arrived, identified the missing part and had to come back the next day with it. By cross-referencing failure history by model on a dashboard, they anticipated which spare parts to load based on the reported equipment and added a short diagnostic phone call before dispatching. Within a few months the FTFR rose to 81%, return visits dropped and SLA compliance with the supermarket chains improved. The key was not buying more vans, but using the information they already had to arrive better prepared.

First-time fix rate vs other service metrics

FTFR is sometimes confused with neighboring indicators, but it measures something different. This comparison helps place it:

While MTTR looks at the speed of the repair and MTBF looks at the reliability of the equipment, FTFR looks at the effectiveness of the visit. Together, the three give the full picture of a service operation.

Common mistakes when measuring FTFR

The most frequent mistake is poorly defining what counts as resolved on the first visit: if you include cases that actually needed a follow-up call or a later adjustment, the number gets inflated and stops being useful. Another mistake is measuring the overall FTFR without segmenting it by equipment type, technician or region, which hides where the problem really is. It is also common to chase a 100% FTFR, which is almost never realistic or profitable: some complex failures legitimately require a second intervention. As a reference, according to IBM (2026), the average FTFR is around 80%, and the best-performing providers, in mature or narrowly scoped service environments, report between 89% and 98%. What matters is not an absolute number, but the trend and the internal comparison between teams and periods, which becomes actionable when the data lives in reliable analytics and not in scattered spreadsheets.