Using Data Analytics to Measure the Customer Experience and Earn Customer Loyalty

In case you’re new to the party, the Syncron team is here at Field Service USA 2018 live blogging some of the keynotes, panels, and breakout sessions throughout the event. It’s been a great week of insights from some of the top dogs in the industry, and we’re not even done yet!

Tuesday, we heard from an expert panel on “Transforming Your Service Business Model From Selling A Product To Selling An Outcome,” as well as Michael Mendoza of Hitachi Solutions America and Ben Vollmer of Microsoft on lessons learned from real-world customers, with strategies for the people, processes, and technology needed for success. And yesterday, Zach McGuire, Group President of MasTec Advanced Technologies, joined Syncron CMO Gary Brooks on stage for a fireside chat to dive deeper into how to shape your future service and business strategies.

The excitement didn’t stop there – Brooks was later joined by another panel of thought-leaders talking about “Turning Data into Information You Can Use.” Steven Tungate, VP/GM of Service, Supply Chain and Innovation at Toshiba Business Solutions, Steve Norgaard, VP of Global Field Service Operations – DUV of Cymer, Manas Pattanaik, Managing Director at PwC, Steve Wellen, CEO at FieldAware, and Gillian Wright, Chief HR and Administrative Officer at Southern California Gas Company all had something to say about using data analytics to measure the customer experience and earn customer loyalty.

Creating order from the overwhelming isn’t always easy, but connected data analytics doesn’t have to be complicated. All it really takes is ensuring you have accurate, clean data from the start, and determining the best ways to analyze that connected data. By using your data analytics effectively, you can start exploring possibilities to provide better service and increased revenue. But, the key to it all is understanding your customers’ business better and anticipating their needs as you go.

When it comes to data analytics, there are a few bumps in the road that most organizations first face when trying to parse out their metrics, specifically around four major areas: data acquisition, data quality, data management, and data analysis.

Data Acquisition

When it comes to the data acquisition, the data you want to collect starts with the outcomes you want to create. And when it comes to field service, “employee and service technician data, as well as job, work order, and financial data are all important,” says Wellen, “but what’s important changes, and ultimately depends on the goals of the company.” Think about social data: That’s a metric that even just a few years ago wasn’t important, but today can be crucial to a companies success measurement.

“There are outcomes that we want, and those outcomes should drive the data that we need.” – Steve Wellen of FieldAware

Data Quality

But what about data integrity – how do you ensure data quality across your team? While it’s an ongoing challenge, Norgaard has seen the biggest gap in aligning incoming data with outgoing data reports accurately. “Being able to weed through what really happened and what really fixed it and then being able to match that up with the data off of the product is an ongoing challenge we face every day,” says Norgaard.

Data Analysis

Let’s talk about the customers – some customers take issue with data collection (see: Facebook-gate 2018) and resist its analysis on the solution vendor side. But, as Wright as recently seen, in-depth data analysis has been the best way to get customers to understand what exactly they’re paying for, down to the unit. “By having the ability to explain high bills through data analytics,” says Wright, “we’ve reduced bill inquiries by 70% since before we had advanced meter data.”

“Being able to help customers understand what’s driving their bill, but to help them predict what their next bill will be is truly what’s driving customer service perception.” – Gillian Wright of Southern California Gas Company

Data Management

In field service, data usage can really be broken down into two areas of importance: uptime and downtime. “Equipment, materials, pumps, and pressures… those are all running uptime, and they’re the reason we use things predictive maintenance,” says Pattanaik, “but let’s not forget about downtime. How you turn around downtime could arguably be more important, because it directly linked to revenue and cash flow.”

They say you can only improve what you can measure, and when it comes to data that measurement is a moving target every day. But, when it comes to field service, being able to acquire high-quality data, manage it successfully, and use its analysis for future organizational and customer benefits is key to turning data into information you can actually use to measure the customer experience and earn customer loyalty.

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