3 Methods for Improving First-Time Fix Rates
In today’s “Uberized” world where customer expectations are higher than ever, improving the first-time fix rate is a top market demand for service organizations. Customers today not only expect service fast, but they want issues resolved immediately. When you send a tech with the wrong skills or tools for the job, you are upsetting a customer and costing your organization money.
The average first-time fix rate for an organization is approximately 77%, according to data from The Service Council. That means that the field technician has to do at least one follow up visit on about 23% of all service calls, and the customer has to set aside more time to wait for the technician. Not to mention, at least 23% of the time you’re facing extra service costs.
This “success” rate just does not match today’s consumer expectations. In fact, many customers have reported leaving a brand due to a bad customer service interaction. As such, first-time fix rate should be a key metric for your service organization.
Here are a few ways your team can improve first-time fix rates, reduce costs, and delight customers.
A commonly cited reason for not fixing a problem the first time is because the technician does not have the right equipment or skill set. In fact, parts unavailability was by far the most cited complaint by customers, as reported by 51% of field service executives.
The scheduling functionality in a field service management solution enables your organization to identify the technician that has the correct equipment and skills to make the repair the first time. An intelligent scheduling solution considers all available resources and qualified skills before booking the appointment.
By making sure that the right field employee is sent to the right place at the right time, with the right tools and equipment, productivity will improve with greater first-time fix rates. Not to mention, customers will have a better experience.
Gartner forecasts that 11 billion connected devices, or “things” will be in use worldwide in 2018, and will reach 20.4 billion by 2020. Internet of Things (IoT) sensors are playing an increasingly invaluable role in helping to fix problems before they even start.
In conjunction with machine learning, organizations can develop service plans based on expected failure rates gleaned from the available data. Your team can also monitor assets to detect any unusual patterns and dispatch a technician to diagnose any problem that might occur. It’s even possible to dispatch a technician to repair the asset before anything goes wrong.
This practice is called predictive maintenance and it leads to reduced downtime, better utilization of technicians, and happier customers.
Mobile Workforce Management Tools
Mobile workforce solutions give remote service employees and their managers the ability to both receive and transmit work-related information and guidance in real time via their smartphones, tablets, ruggedized devices, and wearables.
With field-based equipment becoming more advanced, it’s important that technicians have access to technical experts or a knowledge base to help them solve a problem the first time. A mobile solution enables access to these resources instantly.
The “uberization” of service has put even more importance on enabling your teams to fix field service problems the first time. Failing to put in the right resources in place not only increases costs but could also lead to customer attrition. Focus your KPIs and analysis on first-time fix rates and move to increase them if you feel they are falling short of your standards.
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The costs from repeat visits can really add up. See how much in the video below.
Categories:Artificial Intelligence, Big Data & Analytics, Field Service Management, Scheduling & Dispatch Management