Field service software: Balancing user needs to drive team success

Despite the significant investment service organizations have made in Field Service Management (FSM) software and its adoption across the industry to increase productivity and smooth operations, many technicians remain frustrated by what they perceive as excessive data collection and overly complex interfaces. To improve technological adoption of field service software and customer satisfaction, service leaders must fully understand the needs of different user groups and strike a balance guided by larger organizational priorities.

Ben isn’t your typical field data specialist. A former elevator technician, his journey started with problem-solving. The hours he spent troubleshooting complex control panels and documenting system faults in the field drove him to look for a better way. He began examining his service calls, seeking patterns in breakdown frequency, recurring issues across building types, and client response times. Driven to make his job more predictable, he sought management’s attention. His extensive field expertise and data analysis skills helped support his move to a more data-driven role in service operations.

Ben’s quiet satisfaction with spotting data trends is abruptly interrupted one morning. His phone buzzes, flashing the name of the VP of Operations. “Ben, we’re starting to see a problem. Customer satisfaction has dropped since we rolled out the new FSM – complaints about longer wait times and unresolved issues are pouring in.” Ben’s nagging suspicions echo the VP’s urgent tone, “I need you to see where the system is adding delays and frustrating our customers.” To him, this call isn’t an isolated incident but indicative of a larger potential problem with their FSM strategy. It’s a critical moment that demands immediate, concerted action.

How does collecting the correct data help improve field service management?

Initially, the problem appeared complex. Since implementation, efficiency metrics have barely moved.  Ben compares call patterns against technician experience levels and equipment age using the data. He also examines critical metrics like average repair time, callbacks due to unresolved issues, and mean time to repair (MTTR). Slowly but surely, a troubling picture emerges. Unscheduled repairs of older elevator systems are climbing steadily, even as overall averages remain stable. A concerning thought took root in Ben’s mind: the FSM wasn’t just failing to help technicians; it might be limiting their ability to resolve complex issues. To find the root cause, Ben needed to see the system in action.

From the dashboard to the field

Determined to get to the heart of the problem, Ben’s next call is to someone he trusts—Sarah, a skilled technician he mentored in the field. Her reputation for technical expertise and no-nonsense feedback is precisely what he needs.

Their ride-along is a repair of a freight elevator, an unexpected job that’s far from straightforward. Although the mechanical parts are original, the control panel is new. On-site, Sarah reviews readings and inspects wiring, cross-referencing her findings with the FSM.  “Okay, I get they want to track average load weight over time for maintenance, but why now?” she remarks, pointing at a field in the FSM app. “And do I need to record the current floor? The system already should know where I am! It’s starting to be a bit much.”

The various spectrums of data: How much is too much?

Back in the office, Ben feels like he’s getting closer to the cause of the problem. To help him visualize, he maps out the concerns from different user groups:

Data completeness:

• Range: Absolutely nothing / Everything you need (ideal) / Absolutely everything
• Ideal: The FSM streamlines technical data collection for repairs, removing unnecessary fields such as building address for regular maintenance visits. This allows technicians to concentrate on their work.
• Issues: Technicians collect what looks to them as excessive data, slowing them down and limiting access to crucial equipment details.
• Impact: Unnecessary data collection wastes time, and missing vital information, such as equipment-specific error codes, can lead to errors, delays, and technician frustration.
  

Data usefulness:

• Range: No data / Useful data (ideal) / Useless data
• Ideal: The FSM collects only data essential for troubleshooting and service outcomes, including specific error codes, parts used, and resolution steps.
• Issues: Technicians waste time collecting metrics unrelated to their core work (e.g., average run cycles per day) or are forced to re-enter data already available elsewhere.
• Impact: Data collection efforts don’t aid problem-solving, hinder efficient repairs, and create the perception that the system isn’t designed for the realities of fieldwork. On the other hand, collecting no data can result in a lack of insights and poor response to emerging trends.
  

User utility:

• Range: Field / Back-office, with a point of technician falloff anywhere between the two ends of the scale
• Ideal: The FSM balances data collection during service calls, minimizing technician workflow disruptions while gathering enough data points for insights.
• Issues: Re-entering information and cumbersome interfaces burden technicians.
• Impact: Prioritizing back-office needs over technician workflow can lead to data collection issues (asking for too much), system frustration, and lower-quality client interactions. Oversimplifying the interface can hinder in-depth analysis, while incomplete data capture can cause missed business insights and hinder proactive service.

How to design your field service software deployment for success

Ben recognizes that fundamental transformation involves moving away from a top-down design and adopting a collaborative approach, putting appropriate weight on the concerns of each user persona. He aims to understand their unique challenges and identify the core issues that hinder progress by bringing technicians like Sarah, dispatch, and back-office staff to the table.

Despite differing approaches, he discovers that technicians and back-office staff share a common goal: delivering efficient services that result in satisfied customers. This helps him identify misalignments and areas where the system creates more work instead of streamlining it.

• Data completeness: Audit relevant data fields, collaborate with technicians to identify missing equipment details, and integrate FSM with equipment databases for seamless access to accurate information.

• Data usefulness: Review tracked metrics and cut vanity metrics. Prioritize data that empower technicians to diagnose issues, track repair effectiveness, and identify trends. Identify sources of duplicate data entry and explore automation or integration solutions to eliminate redundancy.

• User Utility: Involve technicians in the redesign process, gather feedback on interface pain points, prioritize mobile-optimized input methods, and pilot test interface changes with a small group of technicians before full rollout.
  

Ben presents his findings to leadership with a clear action plan. Rather than focusing on technician frustration exclusively, he frames the problem as missed business opportunities, inefficiencies, and risks posed by unreliable, incomplete data. He highlights the potential for lost revenue due to repeated callouts, decreased customer satisfaction resulting in churn, and recruiting and training replacements for burnt-out technicians. Executives see the value in investing in a system that empowers their employees.

With leadership buy-in secured, Ben puts his plan into action. In the short term, they streamline data collection by removing unnecessary fields and automating repetitive tasks. This brings immediate relief to technicians, who feel heard and supported. To eliminate data silos, they update metrics to reflect actual service outcomes and integrate the FSM with other systems, creating a more holistic view of their operations.

In the long term, Ben collaborates with a team of developers and a third-party mobile field service app provider renowned for its user-friendly interface and intuitive data collection tools. This partnership allows for a deeper integration of field and back-office needs, creating an experience that prioritizes efficiency and actionable insights for all users.

While these steps provide a strong foundation for improvement, Ben acknowledges that digital transformation requires ongoing feedback and adaptation to meet the field’s ever-evolving needs.

Balancing field service software priorities across different user groups

Ben’s journey reveals a critical lesson: effective field service management isn’t about collecting more data or having the latest technology; it’s about collecting the right data and making it work for everyone involved.

Ask yourself:

• Does your current tech stack truly empower your technicians, or does it burden them with unnecessary tasks and irrelevant information?
• Are you prioritizing metrics that drive meaningful outcomes, or are you simply tracking data for the sake of tracking it?
• Is your system designed with the technician’s workflow in mind, or does it prioritize back-office needs at the expense of the field?
  

If your answers reveal a disconnect, it’s time to reevaluate. A successful transformation requires a shift in perspective – one that acknowledges the unique needs of each user and actively seeks their input.  A user-centric, data-driven approach isn’t just about fixing problems; it’s about unlocking untapped potential. It’s about fostering collaboration, listening to the voices in the field, and building a system that works for your team, not against them.

Ready to transform your field service software into a powerhouse of efficiency and satisfaction? Contact us today and discover how our expertise can help you design for success.