Statistics

Field service management generates large amounts of data, from order data and deployment times to costs and revenues. Statistics in the FSM context means evaluating this data to gain insights that improve service and enable informed decisions.

Important key performance indicators (KPIs) and areas of evaluation in field service include:

• Dispatch statistics: How many dispatches were carried out in a given period? Broken down by order type (maintenance vs. breakdown), by customer, by region. Such statistics show, for example, which customers require particularly intensive support or which type of service is most common.
• Response and resolution times: Average time from ticket receipt to technician dispatch (response time) and to problem resolution. The SLA compliance rate is also often measured. These values are important for service quality and customer satisfaction.
• Utilization and capacity: How busy were the resources? Are there times of under- or over-utilization? This can be used to plan medium-term staffing requirements or derive efficiency gains.
• Revenue and costs: In service, revenue is generated through billed services and replacement parts. Statistics can show which customer or product generates which service revenue, what contribution margins projects have, or how high the share of travel expenses is. For example, you can determine how profitable a maintenance contract is by comparing the planned expenditure with the actual costs incurred.
• First-time fix rate: How often was a problem solved on the first attempt without a follow-up visit? This key figure reflects efficiency and skill quality. FSM systems can calculate this by analyzing tickets.
• Device or product statistics: Which types of equipment cause a lot of service calls? Which series have a particularly high number of malfunctions? This is where service and quality management come together. Service data is used to draw conclusions about product reliability.
• Technician performance: Not for monitoring purposes, but to identify training needs or strengths, key figures are sometimes calculated for each technician, e.g., number of service calls, revenue generated, customer satisfaction ratings.
• Maintenance planning: Overview of maintenance tasks due vs. completed (e.g., how many maintenance tasks were planned for the deadline, how many were actually performed, how many are overdue). This is particularly relevant in maintenance management in order to comply with contractual services.
• Ticket volume & trend: How does the volume of service requests develop over time? Seasonal peaks (e.g., air conditioning service in summer) become visible. This helps to allocate resources or plan actions.

Statistics are indispensable for users to maintain an overview of service operations and make strategic decisions. For example, statistics could show that a particular service contract consistently exceeds the agreed hours, which would be a reason to renegotiate with the customer or become more efficient internally.

Statistics also serve as a measure of success for improvements: Does the introduction of the new technician app lead to shorter writing times for reports? This could be measured by comparing the average time spent on report creation before and after introduction. The impact of AI use can also be quantified: If AI optimizes planning, the idle time per technician should decrease, for example.

Another important aspect is visualization: Tables are good, but results are often presented as charts. Pie charts for percentages, bar charts for trends, heat maps for utilization, etc. These can be viewed in the dashboard or report module. Some FSM solutions even offer real-time updates so you can see how key figures change “live.”

All in all, statistics in FSM transform data into knowledge. They form the basis for continuous improvement of the service process, underpin decisions with facts, and can also be used for customer communication. Innosoft supports this with flexible evaluation options so that service companies don’t get stuck in a jungle of data but gain clear insights.