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The Gist
- VoC evolution. Voice of the customer programs are shifting from manual methods to AI-driven analytics, drastically cutting costs and time.
- AI impact. AI in customer experience has made data collection and interpretation more efficient, notably in automating responses that often outperform human-written ones.
- Speech focus. Speech analytics in customer service is becoming a game-changer, particularly for capturing real-time data during customer interactions.
Editor’s note: this transcript is edited for clarity.
The importance of customer experience (CX) and artificial intelligence (AI) cannot be overstated. These pivotal fields are not only redefining how businesses operate but also setting new standards for customer satisfaction and engagement. As companies scramble to adapt, understanding the synergies between CX and AI becomes crucial.
Here to shed light on this dynamic intersection is Brianna Langley Henderson, regional manager for customer experience and marketing at Waste Connections, who offers her expert insights and strategies. Brianna is a CMSWire Contributor.
AI Revolutionizes Voice of the Customer Through Speedy, Cost-Effective Analytics
Dom Nicastro: You’re back and you didn’t let maternity leave stop you from filing an article, this one on voice of the customer and AI, it’s so crucial. I mean, all of our analytics. Our readers are just like, super high on this. You put CX and AI in a headline, you’re gonna get some interest. So I’ve benefited from that. ChatGPT articles always do wonderfully. But for a practitioner like you, it comes down to making things actionable. So, you know, what’s the big takeaway here with with VoC and AI?
Brianna Langley Henderson: So, I mean, this is gonna sound really obvious, I think. But traditionally, VoC has been very manual. And it’s a lot of manual data mining. It’s a lot of manual data structuring. And then I think that’s the biggest thing. I mean, again, not to sound super elementary, but I think the biggest thing is just going to be the cost and timesaving aspect that AI is going to bring to VoC as far as actually collecting and making sense of of the data, especially when it comes to like speech analytics; I think that’s really the big one. Because obviously, you can gather data from emails and chatbots, and social media and written text. But the speech analytics part is what I’m especially excited about.
Related Article: 5 AI Trends in Voice of the Customer Practices
From Excel Sheets to AI: How the Approach to VoC Evolved
Nicastro: Yeah, if you could look at your own program, your own voice of the customer program as a CX leader, say, a year and a half ago, right? Versus now like, so. What did it look like a year and a half ago? And how’s it look now?
Langley Henderson: Oh, for me, personally? Oh, that’s a good question. Well, a year and a half ago, we were kind of guessing.
Nicastro: Excel sheet.
Langley Henderson: Exactly.
Nicastro: You’re not alone.
Langley Henderson: Well, a year and a half ago, my territory was different. But right now it spans from like Chicago, down through all the way through Mississippi, Alabama, Georgia. I would travel from site to site, which of course I still do. I still visit with my districts, but I would I would just have this question. I would ask the call agents like, OK, what’s the main thing you’re hearing every day? What are the customers saying? What are they having issues with? And that’s kind of how I built strategy was just that very anecdotal information. And I was literally just boots on the ground.
Related Article: AI in Decision Making: The Revitalization of Voice of the Customer
Transforming Customer Insights: How AI Tools Are Revolutionizing VoC Analytics
Nicastro: That’s a great effort.
Langley Henderson: And I mean, not to diminish the insights of the CSRs or the salespeople because obviously, there’s always going to be a place for that. But gathering that information now is just so much easier. I don’t know what the audience uses but we use software that actually is tied into our Google reviews. And so it mines all of this information that’s coming in through Google .. and it tells us exactly what the No. 1 thing that customers are frustrated by, the No. 1 thing customers are delighted by. And it’s just a dynamic constantly updating report.
Related Article: How AI and Data Analytics Drive Personalization Strategies
From Insight to Action: How AI Platforms Integrate with CRMs to Revolutionize VoC
Nicastro: Yeah. Does it let you also take action from those insights to within the platform or connect to like a CRM or connect to other data systems like kind of thing?
Langley Henderson: Yes. So I know for a fact that our CRM is actually internally built and managed. So connecting our CRM with a lot of outside vendor software is really difficult, but I know that it does, like for the main ones, and it also has an AI response generator that is better than the responses that I would write to customers. So it’s really quite something, and that’s just one example. I mean, there’s a ton of different platforms out there that are just completely changing the way the game is played.
Related Article: 5 AI Analytics Trends for CX Personalization
Decoding Customer Feedback: How NLP Drives Evolution in Voice and Chatbots
Nicastro: Yeah. You’ve talked a lot in the article about natural language processing. So what’s the impact there, you know, in terms of the role of NLP and sort of understanding, interpreting customer feedback.
Langley Henderson: Right. So I think the big thing for NLP that we’ve seen over the last couple of years is the evolution I guess, is the word for it, the evolution of the chatbot, right? I mean, we’re like literally on our fifth or sixth iteration of our chatbot. Because all this AI tech keeps coming out. And machine learning now is like way faster and more efficient than it used to be. And a lot of that is because of the natural language processing algorithms that are built into the software. So that’s probably the biggest one is just being able to make our bots, voice bots and chatbots a little more human sounding, and a little more conversational. And obviously, that’s also great for data mining.
Navigating the Ethical Maze: How AI in VoC Raises New Concerns and Solutions
Nicastro: Yeah. Now, with all those insights you’re gleaning from that Google integration in the software that you use. Are there any, like ethical concerns or anything like that, that comes up? Because everyone’s talking about ethical use of AI, responsible use of AI? So what’s your priority? What’s your focus area when it comes to ethics, responsibility processes and procedures with AI?
Langley Henderson: Right? Um, I wish we had like, a hard and fast process with this stuff. But it’s honestly all coming up so quickly, that it’s hard to really get like, procedural.
Nicastro: The world doesn’t have that yet. The global leaders have been trying to figure it out. Europe is trying to figure it out with the you know, AI Act and the United States? Forget it, we don’t even have a federal privacy law yet, much less an AI law.
Langley Henderson: Yeah. Yeah. And I think I mean, for me, the biggest thing, obviously, there’s like, privacy issues and things like that. But here’s the thing, we’re not, we’re not having to buy data anymore. And that always feels kind of slimy, right? Anyway, when you’re out there in the data buying world. And we’re not really having to do that. Because we have all this organic information at our fingertips. And so I think what customers sometimes maybe don’t think about when they get a little antsy about the privacy concerns is the fact that it’s actually a lot more proprietary now than it used to be, in a lot of ways.
Enterprise ChatGPT: A Cautious Approach to Infusing AI
Nicastro: Yeah. Yeah. And I know, you know, OpenAI with ChatGPT just came out with an enterprise level subscription. So you can imagine the privacy concerns that are there and OpenAI promises that, you know, we’re not gonna let the systems learn about like Waste Connection. Because that’s crazy to me: it’s like the whole internet is on ChatGPT right. So anything that’s been said or done is like up for grabs. But on an enterprise level, do you see those tools infused into your workflows yet? Or is Enterprise ChatGPT kind of something you really need to sort of dig down, dig deep and figure out for the future?
Langley Henderson: Um, yes to both questions. So actually, yeah, we don’t use OpenAI. I don’t really have a lot of experience with that particular software vendor. But we do use very similar tools. And even at the enterprise level, I think we’re just now starting to see how they can be effectively distributed across the enterprise.
And when you’re the third largest of anything in the world, that’s quite a feat. Right. So I think we’re just now getting to that threshold and thinking about how to best carry this out to the whole company. But I think there’s always gonna be those, you know, there’s always gonna be the let’s hurry up and wait mindset when it comes to this kind of stuff, because you want to keep your finger on the pulse of what’s going on and what’s trending. But at the same time, sometimes, you know, taking a step back and observing for just a minute, just a beat before you jump on the latest thing, we found that to be pretty valuable, especially when it comes to unchartered territory, like this.
Speech Analytics in VoC: The Future of Real-Time Customer Satisfaction Insights
Nicastro: Yeah, yeah, exactly. So well, I guess we can end on, you know, just the whole, the future of VoC with AI, you know, where do you see it going? And what are some things that excite you as a CX and marketing leader about infusing AI into a voice of the customer type of program?
Langley Henderson: Yes. Great question. So I mentioned earlier speech analytics. And when I say speech analytics, I mostly mean our contact centers. So a lot of our customers, as I’m sure is the case with a lot of other industries as well, are still calling us as; with the many tools and stuff as we give them digitally to reach us, they do still like calling us. And so that’s historically just been like mountains and mountains of recordings, that if we really wanted to make sense of any of that data, it’s not possible. Like that’s not feasible, right?
So now with the onset of speech analytics, I’m really excited to see how that develops. And to kind of, obviously, capturing data in real time with real conversations between two real people, and then structuring that data in such a way that, you know, maybe my thought process is we can look at our largest customer accounts and know exactly after each phone call where they are in terms of their satisfaction with us and with our services. And that will give us a lot of insight into what we can do proactively to really help our big accounts and, and all of our customers really.
So I think that’s the thing I’m the most excited to see develop. Because, yeah, like I said before, it’s one thing to have to go through a bunch of text and mind stuff. That’s, that’s not nearly as difficult as having to go through hours and hours of recorded phone calls. Yeah, so I am really excited about that.
Nicastro: Yeah, of course, with bringing in the human at the end of the loop after like sharing the AI data with your with your contact center leaders and agents, because, you know, does this jibe with what you have heard? Like those conversations you said you used to have?
Langley Henderson: Yeah. And on a very practical level, I think it’s gonna really help with retention and even acquisition.
