Field Service Palm Springs 2025. Case Study -- Using Your Data to Better Prepare for Service Calls and Improve CX

Alright. Hey, how's everybody doing? No, it's been a long day. Everybody's eager to have some after activity and I'm looking forward to it also. But the last couple days have been really interesting. As we started to navigate and meet different folks and talk about different industry challenges we found that we all have a lot of things in common. And so it's very interesting dinner last night, meeting some great, some good folks to have some collaborative conversations with, has actually been very beneficial.

So some good takeaways for me. But I will say this though for my TMD team I promise I won't ask any questions, right? And if I have any backs or team members in here there's two things I've committed to. I'll only say tariffs once and I'll use, and I'll say AI once, right?

So that was a kind of a running joke last night about tariffs and AI and the commitment not to ask any questions. So today we're gonna talk a little bit about preparing our technicians for service calls and so I'm going to bucket this to the three areas. We'll talk about preparing technicians for service calls. We'll talk about some of the tools that we're using to help technicians along. We'll also talk about analytics in general, right? How do we prepare for KBIs and different types of data that we use for that. So I'll bucket 'em in those three areas.

The one thing I was gonna call out with this—one of the first things—is we try to identify kind of the problems and challenges that we're finding, particularly on the Coca-Cola service operations side. And I won't go through all these, but I'll just frame it up. Making sure that our technicians have the right tools to be able to perform a service call was really critical. It all points back to customer experience. The data they're using, right? So are we pulling and providing the right kind of data sources, right? For technicians?

Often we found that it's really fragmented data, a lot of different data resources, a lot of sources that we're trying to pull from, convolutes what we want technicians to know. There's the wrong data, right? And so I guess there's a lot of conversations about what's the right data, what's the wrong data? And we had to determine exactly what that looked like. Limited tech accessibility, right? And that was a big challenge about how do we, what platform are we gonna use to make sure our technicians have the right data for 'em to help them to really execute and deliver on service.

The other thing is quick fabs data. So those are some of the challenges that we were finding when you think about all the different data sources that we pull together. And we can go to a lot of conversations about where they come from, whether they're using Power BI or Tableau or Oracle or Smartsheet. It could be a few of those different types of resources, but we tried to figure out how we were gonna pull those together.

The path forward for us was really about how do we collaborate from a leadership standpoint to find out exactly what we need and who we need it from? And I'll tell you guys a just a funny story because whenever we would go from a leadership standpoint and talk about data solutions, they're very smart, savvy people, right? They know the integration, they know the different tools, they're smart. And we'd go to those teams and say, Hey, listen, I like to see an input and I'll give you an example. I'll say I like to understand the time on location by a technician and compare that to fix, right—first visit—meaning if technicians spend longer times on location, that as equate to a faster fix, right? A more adequate fix that reduces the chances of a repeat service call that a solution says. That's great, Dave, but I think you should look at it this way. And the challenge was like no, I really, I wanna see the output. I wanna see X, Y, and Z. And the data solution team says, I just don't think that's the right way to look at it, Dave.

And so we always have this kind of back and forth with our data solutions team to kinda go back and forth—healthy dialogue. A little cumbersome, but at the end of the day, we were able to come up with, here's what we like to see. And I gave you that example between time of location and fix first visit 'cause we thought that was really important. It's the most simplistic way to describe it to you. But we looked at a lot of different data points working with our cross-functional teams, right? Very smart, savvy people. They helped us go to the table and says, we like to see this type of data. They gave us insightful feedback about what we should be looking at. We said we'd like to see it this way. We came together and figured out what we wanted to look at.

One of the things that came out of it was some tools for our senior tech. So we were working with an AI team and we said, Hey we would like our senior technicians to be armed with information based off of predictive analytics, if you will. So AI to help our senior technicians understand what may be driving the issue of the service call, which led to if a technician would've called for support, the technician would be able to get adequate support from a senior tech.

So we started working with that team quite a bit to understand what do we need, what type of systems do we need to make sure our senior technicians have the proper information based off of historical trends, right? Break fix activity. Potential parts needed in order to help our technicians. So we had to pull all that data together. Our team, we worked with that, that helped us with AI. That's twice I've said AI now, so I broke my promise already. So those tools were really great and we actually were able to get some really good tools from that team to help.

So it was all—when I say leadership—once we identified all these problems and challenges we were having, it was really key to work with the teams who could help us. Obviously get what we needed from an analytics standpoint, from a reporting standpoint.

One of the other things that came outta that meeting was a Tech Connect portal, right? The Tech Connect portal for us was really key because technicians arriving on site to do a repair—what do they need to know, right? So the Tech Connect portal allows a technician to use a QR code and it gives them detailed information about that dispenser, right? Really critical, right? So we wanna make sure we give them all the tools they need to be able to make a quick, fast repair, right?

And what we found is that the technician, the feedback so far is very positive, right? Go to an outlet. In our case, I'll use let's say Burger King. Burger King, freestyle platform. Technician can scan the QR code, and at that point on his phone, it'll give him all the relevant information about the prior service call history associated with that dispenser. Right now, the technician's armed with previous technician notes, potential problems associated with the dispenser, right? And it also gives him other information too that allows him to really quickly reference technical information about how to facilitate the fix.

So really great stuff. That all came because we were able to get with data solutions, we worked with our freestyle team, we came up with a reasonable solution to help technicians, right? The adoption rate has been fair, right? And the feedback is good so far, and we wanted to make sure our technicians had the right information, right? So that information is being fed in through data, right? The right data, the data that points to the functionality of the equipment. So very good stuff.

The other piece that we looked at is high incident outlets, right? Very critical. We've had some really good experiences with identifying high incident outlets, right? So nothing more frustrating to a customer than to have frequent issues associated with their dispenser pouring.

From a service operations standpoint, the feedback from customers really points to a really uncomfortable situation, right? When you talk about the reliability and uptime of a dispenser. High-incident outlet reporting also gives us an opportunity to understand the service call history associated with that outlet—the parts used on that particular outlet. It really kinda gives us the information to know how do we get in front of issues—quickly identify that this dispenser's experienced X amount of service calls in a 45 day period, right?

So proactive look that we've identified an issue, we can go in and try to resolve the issue, right? And we can work with our CU technicians, but all that data pulls in and it helps our technicians to be able to prepare better for a service call. So very critical things we've been working on.

When you think about those challenges I think most of 'em are fair, right? And we tried to come to solutions. Now, to be honest with you, that list could—when I talk about challenges—that could've listed 20 other challenges, right? But for the sake of time, I just kept a few. Some of the other path forward activities—there's other things too, but for the sake of time, I didn't wanna go through and take you guys through a bunch of slides, although I had up to 10 slides to do. I said it's so late in the day, let me scale this back from 10 to maybe four. Just looking out for you guys, actually. I know you guys appreciate that.

But yeah, so I just wanna highlight the ones I highlighted in yellow are the Technicians Connect portal and high outlets. Those are really important. And they really tie back to the total customer experience. So really good stuff. And what I will go back to and talk about—us working with our cross-functional data teams—they can be challenging conversations with those teams. But at the end of the day, right, when we all came together and talked about what we were really looking for, they came through for us. They came through and, little back and forth, a couple of sessions, and we were able to come up with some reasonable requests about what we were looking for from a data standpoint.

What can we put into a system to look at Tech Connect portal, look at our high outlets, and they all yielded a better response rate. So ultimately we always talk about the total customer experience, but we really wanted to talk about the technician experience as well, right? And so the feedback's been pretty good.

I'll talk a little bit about the Tech Connect portal. And you think about—and I listed there as a tech experience, right? And we talk about employee engagement, right? Our listening sessions and we have a large third party network. And for us, we certainly wanna listen to our partners, right? Here are our challenges. Here's the feedback coming from our technicians. Here's what they need. They say they need to execute on the service call.

So that portal that was created really allows the technicians to do some critical things. And there's a couple areas that I'll point out. So you think about the automation, right? There's a host of data available to the technicians that helps 'em to execute, right? So we thought that would be great for technicians to have that information at their hands, right? Helps 'em execute, reduce repeat service calls, giving them the information they need to do a great job.

One of the other areas you'll see down there is just working work in queue, right? It will show you technician notes, previous technicians, resolution codes and things of that nature, and it kinda goes on. I have to go through all those, but you can see those are the different, all the information packages that it'll provide to the technician.

One of the key pieces is it also will map the technician to documentation, right? Which is really critical. There's a lot of resources, a lot of data available. There's also a feedback form. So if a technician runs into an issue, he believes he has a key recommendation—maybe there's a recommended part that he believes could help reduce the chances of additional service call—he has feedback for our engineering team. He can complete a form, submit it, take pictures, and submit it to our senior tech team. That ultimately goes back to our engineers based off the feedback, right?

So what we're hoping is we receive a lot of that feedback—hopefully we'll go back, we'll look at a dispenser and if the general feedback is consistent about, I've received let's say a hundred forms back to the engineering team. Our hope is the engineer will take that feedback from the field and go, Hey, this is what we're running into. And we can go back and track that activity with those dispensers and come up with kind of a cause of issues, right? And then hopefully engineer can look at a redesign, anything they can do differently with. So we thought that was a good feedback channel for technicians.

But really powerful tool. As I said, the adoption rate has been fair. We're trying to get technicians to use it more. And it also points us to high incident outlets as well. So really great stuff.

Now, the reason I wanted to show you this particular slide—'cause you could always think what does that really kinda do with analytics and data and stuff like that? So there's a lot of different types of data we can pull and you guys all agree that there's no shortage of data points, right? What we wanted to do is we wanted to pull data and say, we can certainly send out a spreadsheet, and I was working with a gentleman yesterday. We talked about spreadsheets, right? We can certainly pull all this data together and try to point to a picture about what's the root cause and how do we dive into it. But we said what does the technician do? You're not gonna, obviously not gonna send a technician a spreadsheet, right? You're not gonna send 'em this matrix of everything that's going wrong. But this tool allows them to give them quick, fast information that helps them to be able to solve an issue quickly.

Good stuff. So really powerful tool. We are gonna capitalize off it. We think there's opportunities to enhance it. Anything we can do to reduce service calls, particularly repeat service calls—this is the way to do it and we'll continue to grow this.

One of the final pieces I was gonna talk about was—and I struggled with whether, how to stack this up—'cause originally I was gonna stack the KBIs, right? How do we measure, what do we do? How do we formulate a plan? How do we use our data? I said let's start out with KBI tracking. And I decided to talk about problems, path forward, some of the things we're using to help our technicians out 'cause I wanted this to be about how are we helping our technicians. But I thought it was equally important to talk about KBIs in general. And although we don't always share this with technicians and get 'em in a room and talk about spreadsheets and trend charts, I thought it was important to talk about some of the things that we've done from a service operations standpoint.

How do we use our data? Lots of data points. Some of this stuff, I think most of you probably are doing this already, but I wanted to show some of the stuff that we've taken to kinda look at data. How do we use data? We were in a meeting early today and I heard control tower twice within 30 minutes. Two different groups, right? And we looked around and we kinda laughed a little bit. We said control tower, right? Lots of data sources. Lots of information flowing. And the question was, how do we manage all this influx of information, the flow of information across the board?

And it was one of the members in our group—small group—said, do we have a control tower? And just to his comment was, I'm not that close. I'm not sure if he's in the room or not, but I'm not that close to field operations. I don't really understand field operations. This is new to me. I'm being pulled in, but this is not really something—my space—but I can't help from thinking about a control tower. And we all looked around and we said, control tower, what do you mean by control tower? And he started describing, he was like, oh, so that's what you mean by control tower.

So very good information because it all ties to this. If you have a lot of information flowing back and forth between functional groups, who's controlling the data, right? Who's controlling the flow of information? Is there one single source or single person that really controls where the information is heading? Where is it going? And so we thought that was a really interesting way to put it about control tower. And he framed it up about, this is how I think of a control tower when it comes to data and information flowing.

But you can see there—some of the tools that we've used, and this is probably not new to many of you in the room—is that we thought it was really about identifying performance gaps, right? And so we've defined exactly what we wanna look at, right? So there's all kinds of charts and scales and PowerPoint presentations. There's executive PowerPoint presentations, there's field supervisor level presentations that point you in a lot of different directions. At the end of the day, we really wanna try to identify where the gaps are based off of the key KPIs that we've established.

And some of this information is really important for us because we have a large third party network. How we share and socialize the data is really important. So it's not about being preachy. It's not about an aha moment, as I put it, right? It's about truly identifying where your gaps are and sharing it with your partners. Coming to an understanding, obviously being strategic about the decisions you wanna make that's collaborative—enhanced accountability, right? So I mentioned to one of my TMD teams, I said, it is great partnership. We partner, we collaborate, but there's still gotta be an element of accountability, right? We wanna hold people accountable, individuals accountable for performance, right? So I always say there's a segmentation between partnership and accountability, right?

So we talked about that and it was about driving continuous improvement. That was one of the things we talked about too. And about how do you continue to drive continuous improvement through some of your analytics, some of the tools that you use to push your providers forward to actually execute and drive customer experience, right?

So I just wanna share those with you. Quick session. I got about a minute left, so I wanna see if there's any questions at all. All right. Good deal. Thank you everybody. Appreciate it. Thank you.