Advertisement

Turning Your Contact Center into an Insight Center

This past week, I had the opportunity to moderate a fascinating discussion at the ICMI Expo in Ft. Lauderdale with Tim Donchez, Continuous Improvement Director at Lutron Electronics. Tim told the story of how Lutron has transformed its service organization from a department that provides great service to its customers into one that provides great service to customers and critical insights to internal and external business partners.

For those of you who don’t know Lutron, they’re a very cool company with a fascinating history. In fact, almost all of us use their products every day and likely have never realized it. A young physicist named Joel Spira invented the dimmer as a way to control the intensity of light that was emitted from a bulb. The company was incorporated by Joel and his wife, Ruth, in 1961 and what ensued was a long string of innovations—the linear dimmer, infrared lighting controls, window shading solutions and many more. Today, Lutron is a major player in the “Internet of Things” and “connected home” market—producing control systems for lighting, ceiling fans, shading and other products that can be controlled from a smartphone, voice assistant or other device.

insight center

Lutron’s contact centers take calls from many different types of customers—from retail partners that sell Lutron products to Dealers, Distributors, Electricians that buy/sell and install products, to professional lighting designers, architects, home builders and from individual consumers (i.e., the DIY crowd—people like you and me who buy a Lutron product and install it on our own). And, as you can imagine, Lutron carries many different types of products in their catalog.

Add to all of this the fact that their products need to integrate with a whole host of other products—whether it’s your iPhone or your Alexa voice remote or just that cool-looking LED bulb that you bought at the hardware store [little-known fact: not all LED bulbs will work with all dimmers; call Lutron and talk to one of their agents and they’ll explain this to you and coach you through how to pick a compatible dimmer and LED bulb…which I did myself a few weeks ago while standing in my local Home Depot store]. What all of this means is that Lutron agents are operating in a very complex environment—and are often in a position where they’re having to troubleshoot issues not just with Lutron products, but other companies’ products as well.

Suffice it to say, Lutron agents are really good at what they do. They have to be. Lutron is very careful about who they hire into a service role—they’re looking for career professionals who will stay with the company for a long time. After all, longer tenure means more knowledge that’s accumulated and, ultimately, better service that can be delivered to their customers. As Tim explained to me a while back, “We hire great people, and we trust them to use their judgment to serve our customers.” In other words, you won’t find a lot of the trappings of a typical contact center when you visit Lutron’s centers. No scripts, no rigid quality assurance checklists, no handle time clock hanging over the reps’ heads. In fact, up until recently, Lutron didn’t even record their phone calls. Lutron’s contact centers feel less like the “factory floor” model you see in most companies and more like a knowledge work environment similar to what you’d see in a marketing, finance or engineering department.

So, it might come as some surprise to learn that Lutron made a decision about a year ago to start recording phone calls and implement a speech analytics solution. After all, historically, these technologies have been used to performance manage the frontline—to automate quality assurance--to make sure agents are using required scripting (greetings, closing statements, disclosures mandated by compliance, etc.). This sort of thing seems very much at odds with an organization that prides itself on not telling its agents what to say and do but instead letting them determine the best approach to handling a customer issue.

But this is where the story takes an interesting twist, and a company built on innovation does what it does best. In full disclosure, Lutron is a customer of my company, Tethr (they use our conversational intelligence platform to analyze their voice data). But when Lutron reached out to us, they made a very interesting request: they weren’t interested in using our analytics solution to monitor what their agents were doing…but rather to listen to what their customers were saying. They took a technology built for one purpose and applied it to a different purpose.

Why did Lutron do this? The company’s leadership realized what many companies are now realizing: customer survey response rates are on a steady decline, and the verbatim responses collected through surveys are getting lighter all the time. Customers are over-surveyed, and they just aren’t willing to take the time to provide detailed feedback to companies anymore.

But, while Lutron was seeing surveys decline in the value they provided, the company talks to its customers all the time across its contact centers. And in those conversations, customers are providing incredibly rich feedback on their products, their brand, their website, their installation experience and almost every other thing that leaders at Lutron needed to make better products and deliver a better experience to their customers. So why not record those calls and analyze the data instead of fighting the losing battle of getting customers to reply to surveys and provide actionable feedback.

Given the work climate Lutron had built in its contact centers; there was some initial skepticism from agents—especially those who had worked in other contact centers before and been subject to the micromanagement and “screen-popping” that often accompanies speech analytics. So, they took the interesting step of anonymizing agent names in the system. They can see team-level roll-ups, but not individual agent performance. As Tim explained in our session at the ICMI Expo, “It was critical to us that we show our people we aren’t going to ‘weaponize’ the data. The whole goal of this effort was to do a better job listening to all of our customers so that we could solve problems—product issues, installation challenges, you name it—upstream with our business partners.”

Lutron has been at it for almost a year, and the wins are really starting to pile up. During the session, Tim shared numerous examples of insights gleaned through their new approach to customer listening. He told the story of one particularly vexing installation problem that drove a lot of their call volume (and no small amount of customer frustration and returns) that was solved by addressing some confusing language in the installation instructions. Another story he shared was about their partnership with the engineering team on new product launches—their new analytic approach allowing them to spot problems with new products (which often go undiscovered until after customers start buying and installing them) much faster than in the past when they’d have to wait for customer survey responses to start coming in. Lastly, Tim spoke about how the new insights they’re surfacing are helping them to provide better guidance to their retail partners—for instance, which LED bulbs stocked at a retailer generate compatibility/performance calls into Lutron Technical Support.

Today, the contact center at Lutron is seen as much more than an organization that resolves issues and answers product questions for customers. They’re seen as the primary source of customer insight about products, the brand, the digital experience, marketing campaigns, etc. “We’ve got a line of business partners out the door who want to get access to the data because it allows them to deliver products and experiences to our customers—to learn quickly what’s working and what’s not and then to act decisively.” He also says the contact center team has really embraced the new approach and no longer fears that the data will be used to micromanage them: “Our agents now see that what we’re doing with this data is ultimately helping us avoid driving frustrated customers into our service centers because of avoidable issues.” Tim joked that when his team started showing agents some of the things they were learning and working with their business partners to address, the response was something along the lines of “It’s about time you guys fixed this stuff!”

Tim offered a number of pieces of advice to other organizations looking to transform their contact centers into insight centers:

1) Start small: Tim explained that once you deploy a solution, there will be so much data and insight coming at you that there will be a natural tendency to want to boil the ocean. To avoid this, teams should establish some ‘focal points’ right out of the gate—in other words, what is the shortlist of priorities (e.g., getting a better sense for actual contact or repeat contact drivers, diagnosing the specific issue with a product seeing high return rates, understanding why customers are struggling on your digital channels, etc.) that you can dig into, work the data, find an answer, and drive action against?

2) Establish a data governance model first: Having a line of business partners dying to get into contact center data is truly a “high-class problem,” but without a solid data governance model established in advance (specifying who can access the data, what they are able to do in the system, whether they can shape and add to the data, what sort of training or certification is required for higher levels of access, etc.) you’ll end up in a situation where you’re making it up as you go and likely will frustrate your business partners—which really negates a lot of the internal benefit from doing this sort of thing at all.

3) Business perspective over data science skills when staffing your team: As Tim explained, taking advantage of AI and machine learning today shouldn’t require data scientists or heavy statements of work with consultants and vendors: “We found that teaching the ML required some rudimentary Excel-like formula writing skills…not a Ph.D. in AI or any sort of formal data science training.” But while you don’t need data scientists, having an expert on the business (somebody who is deep on the product, understands how customers talk about and use the product, etc.) is absolutely critical to know what to look for.

4) Avoid false positives like the plague: The thing that will immediately derail your efforts to become a purveyor of insights to business partners is false positives—that is, showing data to the business (e.g., “we found 48% of customers calling about this product are frustrated by this feature”) that isn’t validated and bullet-proof. “We’ve come a long way from agent-reported call reason codes—which business partners, understandably, didn’t put much stock in as they’re notoriously unreliable. But, that doesn’t mean we can just ship insights to the business from our current solution without carefully vetting the hits we’re getting to ensure there are no false positives in the data. As soon as you put a data point or insight in front of a business partner and they start to find flaws in the data or conclusions you’ve drawn, you’re sunk, and they won’t come back to you for help.”

There’s no better source of customer voice data than the conversations we have with our customers every day. Becoming an insight center for the enterprise is a goal all contact centers can and should embrace.

Comments

Leave a comment

Please sign in to leave a comment. If you don't have an account you can register for free here.

Forgot username or password?