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Field service management (FSM) may not be familiar to you, but it's an old concept that has been repackaged in...
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a new form: customer experience management.
Field service management automates and optimizes the information gathered on staffers sent into the field. With improved field service management, companies can better understand how and where they lose money. By collecting data on each instance of service and on each technician, then analyzing the data, information emerges that answers basic questions about your business operations.
Ultimately, this data can translate into business efficiencies, lower costs and an improved customer experience. Let's explore these principles in the context of the experience of Cincinnati Bell, a telecommunications provider based in Cincinnati, Ohio, that services Ohio, Kentucky and Indiana.
Predictive analysis for better customer service
For Cincinnati Bell, the process toward improved customer experience through FSM began when it partnered with TOA Technologies. After years of capturing data on the field service work in a few vertical industries, the company generated a huge database that enabled it to predictively model service calls, even for customers Cincinnati Bell had not previously worked with. Over time, the predictive algorithms churned away, collecting data from technicians to build a predictive data model for businesses.
The process of collecting data using these predictive models is called learning, which Cincinnati Bell didn't begin using until September 2012. However, the company made reasonable progress with TOA models. By the end of 2012, it experienced a 30% reduction in overtime.
But why wait six months to turn on the learning function? It was an attempt to factor in the human element. Cincinnati Bell spent the first six months training technicians to comply within the program. It was useless to capture data to build models if technicians forgot to enter crucial information or fudged data. Technicians needed to be trained on good data practices first.
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Jay Kurtz, vice president and general manager of field operations at Cincinnati Bell supervises a group of 700 people and a large budget. His overtime budget alone was more than $10 million when he first began working in his current role. So, he decided to focus on overtime initially. If he could reduce overtime costs while keeping service levels high, the effort would be a success.
Managing overtime had several benefits. Saving money was one, but so was ensuring work-life balance and meeting customer commitments. Kurtz reduced overtime costs by about 20%; along with achieving other benefits.
Along with gaining a better understanding of time spent on workflow, TOA's algorithms brought a new understanding of the other ways time was spent, including time spent traveling, having lunch or getting lost in the field. Predictive models helped, too, although they were idiosyncratic. You can't compare travel time in Cincinnati with travel time in Cleveland, for example, and it changes daily. With learning turned on for different geography, aspects of the work are now managed the same way.
Everyone has heard the dreaded words that the technician "will arrive between 1 and 5 p.m." Thanks to FSM, that four-hour window is rapidly collapsing to two hours, but it's still a work in progress. With these improvements, the technicians are hitting their targets more consistently, getting more done in an eight-hour day and reducing overtime costs.
The early returns suggest that predictive modeling with field service management is making a real difference. "TOA has enabled us to get our arms around driving efficiency in the organization," said Kurtz, adding that bringing on a new GPS capability will only bring further improvements.
Field service management has matured. Data collection and analysis combined with analytics improved the bottom line for Cincinnati Bell and reliability for customers.