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In this episode of the Customer Service Secrets Podcast, Gabe and Vikas are joined by Rose Wang and Dan Watkins from Forethought to learn the secrets to successfully incorporating AI in CX. After listening to the experts, you’ll come to find that implementing AI isn’t as difficult as you might think. Listen to the full podcast to learn more.
Enhancing CX with Automation
Technology’s ever-improving, so why shouldn’t CX do the same? The more advances we make in technology and communication, the more CX leaders should do to keep up. It no longer makes sense for agents to be on standby, only to scramble for answers and keep the consumers on the phone for ages. Customers don’t have time for that anymore and frankly, neither do your agents. According to Rose and Dan from Forethought, a company that enhances CX with AI, automation is key to making your teams more productive for the modern customer.
“We really take the customer experience journey to a whole new level through AI.”
Who wouldn’t want to enhance their customer’s experience? What better way to do it than to implement artificial intelligence? The customer experience at its finest happens when a customer feels listened to, taken care of, and treated as a human rather than a number. Oddly enough, robots add that human element back to CX by streamlining solutions from agent to customer.
Your Employee’s Experience Matters – Here’s Why
Stellar customer experience starts when agents have a deep connection with the brand, a vision of purpose, and empathy for the consumers they serve. So, how do employees attain all of this and how can leaders create an environment that enhances these important aspects of CX? Dan explains:
If you go look at it, customer support has one of the highest turnovers of any job in any company. So the employee experience is really tough there…if you can improve employee experience, your customer experience follows right along with it.
Many C-Suite executives and CX leaders agree that the happier their agents are with their work, the better outcomes they produce, which ultimately leads to lasting customer loyalty. When agents work with leaders who value empathy and relationship building in the workplace, that trickles down to their customer interactions. After all, happy agents equal happy customers.
“You also then go in and see if a company prioritizes CX and you can see that from the beginning, their employees see it, you double your revenue growth.”
The Past Drives the Future
AI really isn’t as scary as you may think it is. In fact, it’s easy to implement when working with companies like Kustomer or Forethought that do all of the work for you. What’s important for getting AI to work is to learn from previous customer interactions, figure out what worked and what didn’t, and then to teach your AI how to distinguish those types of outcomes. Essentially, you teach your AI how you need it to work for your business. As Rose says, “People expect AI to just work out of thin air and that can’t be true because again, we need to learn from your past to understand how to act in the future.”
Data is another essential part of successful AI implementation. If you don’t have previous data for how customers responded to different situations, how can you teach the AI to distinguish between needs, solutions, or FAQ articles?
“Your AI strategy is only as good as your data.”
To learn more about the future of CX and making AI work for you, check out the Customer Service Secrets podcast episode below, and be sure to subscribe for new episodes each Thursday.
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Full Episode Transcript:
The Future of CX is AI with Dan Watkins and Rose Wang
Intro Voice: (00:04)
You’re listening to the Customer Service Secrets Podcast by Kustomer.
Gabe Larsen: (00:11)
Welcome everybody to today’s show. We’re going to be talking about the future of CX is AI, and to do that, we brought on two special guests: Dan Watkins and Rose Wang. We’ll let them introduce themselves before I go back to me. So Dan, why don’t we start with you? Tell us just a little bit about yourself and maybe a little bit about, well, let’s start with just you at the moment, then we’ll go to Rose and we’ll talk about Forethought after. So why don’t you jump in first Dan?
Dan Watkins: (00:38)
Yeah, so Dan Watkins. Joined Forethought a little about a year and a half ago as their President to lead Operations and go to market. And then before that for almost 15 years with Qualtrics and so –
Gabe Larsen: (00:51)
15 years? Were you there 15?
Dan Watkins: (00:53)
Not quite. It’s like 13 and a half. 15 to 13. 13. It’s cool. It’s an unlucky number and so 15 is a little better.
Gabe Larsen: (01:02)
Yeah. That just means you and I are getting old. That’s all that means. Yeah, quite ironic. Qualtrics, congratulations on that. You guys made quite a bang in the industry and I know you’re looking excited about the next event. Rose, real quick. Give us your take.
Rose Wang: (01:19)
Hi everyone. I’m Rose. I am the head of Customer Experience here at Forethought. I joined about two years ago, which is dinosaur years for us at Forethought and as the first CSM. And really for me, it’s just helping out our customers, making sure they have the best experience with our AI platform.
Gabe Larsen: (01:36)
Dan Watkins: (01:37)
She was also the CEO of a really cool company called Chirps funded by Mark Cuban and done a lot of really interesting things before this as well.
Gabe Larsen: (01:47)
Oh, Rose. Yeah. You forgot to mention that. Don’t be shy.
Dan Watkins: (01:57)
Our co-founders have some good Mark Cuban stories, Rose. So we’re going to have to connect you at some point, he invested in their early companies. And so I’m sure you can exchange a few stories.
Rose Wang: (02:08)
Definitely. We’ll follow up on that.
Gabe Larsen: (02:09)
It seems like everyone’s got a Mark Cuban story. I need to get one of those. And then it’s always, you have me, I’m Gabe Larsen. I run Growth over here at Kustomer, and then you’ve got my colleague, Vikas Bhambri, who’s the SVP of Sales and Success over at Kustomer. So let’s dive into the topic at hand, the future of CX. Want to hear about that, but got to start with Forethought. Dan, why don’t you take us? What is forethought? What do you guys do? Why do you do it? Why would you leave Qualtrics? What appeared to be something so awesome and jump to Forethought? I’m confused. I need to hear the story here.
Dan Watkins: (02:45)
Yes. I think it goes into a really interesting story. About two years ago, I’m growing, doing all kinds of stuff with SAP, and I get a call from Deon Nicholas, CEO of Forethought, and he’s like, “Dan, we got to do something together.” And I was like, “What are you up to?” And he’s like, “Have you heard of chatbots?” And immediately my heart actually kind of sunk. I was like, “Oh, Deon. There are so many more interesting things you could have done than a chatbot. Please tell me that that is not what we’re about to go and talk about.” And he’s like, “That’s not what we do.” What we’ve done is we’ve brought through the customer experience across the entire life cycle of a customer. Everything from being instead of deflecting a ticket, we’ll actually only dress the tickets that we can solve.
Dan Watkins: (03:32)
And we only take it as it comes in, get them the right agent in time. And then we go in and assist them. And so, I don’t know. I think all of us have gone online. We’ve gone to go get money and asked a question and you get an answer. It’s like, are we speaking the same language? And then for the, do the opposite, we really take the customer experience journey to a whole new level through AI. We’ve actually, invited a ton of my friends that also wanted to also get to know because what has Kustomer done because we’ve, I actually, I know the answers. You guys are offering the best CRM out there for omnichannel, but why is it that Kustomer is growing so quickly and this meteoric rise in your space?
Gabe Larsen: (04:15)
Vikas? Vikas is the true thought leader here. Vikas can you answer that?
Vikas Bhambri: (04:20)
I appreciate that, Dan. I think what it is, in the industry, there was kind of three elements that were distinct for so long, and I would say over 20 years where we had channels in the contact center. A myriad of channels that obviously accumulated over time, everything from voice to email, then adding on chat, as you mentioned, SMS and social channels, but obviously a disparate number of systems to address all of those. The second was obviously ticketing or case management. Very rudimentary, but something that obviously every contact center’s primary mission or purpose is, but the big missing element in the contact center for so long has been this element of identifying the customer and really getting a true sense of them to offer and deliver them the best service.
Vikas Bhambri: (05:12)
And so for us, it was an opportunity to bring all three of these together in one platform and disrupt the industry because the traditional vendors that have been doing this for so long, as I said, either did not have that the channels required and kind of a point of view around the channel, as you mentioned, the term omnichannel. So many of them just rebranded what they were doing as multichannel to omnichannel. Just kind of slap that lipstick on. But the reality is for us, true omnichannel is a multithreaded conversation that goes back and forth between customer and agent or bot across all channels. So they can jump around and have that seamless experience of the true sense of knowing who that customer is. So we didn’t see anybody out there that was doing it. Some were doing it, but it took an army of developers and you have to kind of give you are firstborn to achieve it. And so for us, it was a way to disrupt the industry by allowing people to deliver that day one experience and really kind of a rapid time to market.
Gabe Larsen: (06:16)
Yeah. Good overview on that. And I think that’s why we’ve had a good conversation, Dan and Rose, is Kustomer brings such an interesting perspective, I think on a foundational core platform, bringing systems together, omnichannel, CRM, those types of words, and adding on layers of artificial intelligence. I think Dan, I think I had a similar conversation as you had with your CEO. When you first told me you jumped, I said, “Oh. You guys are just a chatbot company?” I’m like, “Oh man, another chatbot company.” And you’re like, “No, no, no, look. There’s more to it than that. And I think this AI piece really is needed. And it’s something that the market really, really wants.” So want to go into it and double click on a couple of these and Rose, maybe I’m going to start with you. Big picture, what do you think is broken in customer service? I mean, we’ve been hitting on some interesting things and I want to get to AI here in a second, but you’re obviously in the weeds. You’re playing around and you’re experiencing it with you or your customers. What do you think is broken, high level?
Rose Wang: (07:12)
High level, I would say that one of the biggest surprising things is how much decision-making is made without any data or insights. There’s just too much data, right? How, what the agent, who is the agent, the tenure of the age of the C-SAT score of the ticket, how many times the ticket has changed over hands. There are just too many things to look at and not enough time. And so how do you go forward and make sure that your customer experience is set up for the future when you don’t even really know what the past looks like?
Gabe Larsen: (07:43)
Love it. Dan, what would you add to that?
Dan Watkins: (07:46)
Yes. If you go look at it, customer support has one of the highest turnovers of any job in any company. So the employee experience is really tough there, but then when you go look at Forbes, Forbes wrote an article and said 70% of executives agree that if you can improve employee experience, your customer experience follows right along with it. Qualtrics also did a ton of research and that’s amazing. You also then go in and see if a company prioritizes CX and you can see that from the beginning, their employees see it, you double your revenue growth. And so what’s happening is too many companies are seeing customer support as a cost center. So the employees end up having a worse and worse experience. And then you go and you look at it and you can go and deploy technology that makes it even worse. If the technology that you’re deploying to take care of your customer only leads to more upset customers, employee experience, again, goes down, revenue growth, again, goes down. And so I think there’s a lot of things and we often forget how important the experience is that we provide. We’ve got to do better.
Gabe Larsen: (08:45)
Yeah, the employee and customer stuff, that just continues to be one of those tried and true principles. Vikas, last but not least. What’s your quick thought? What’s broken right now?
Vikas Bhambri: (08:54)
I think both Dan and Rose touched on it. Consumer expectations are at an all-time high. Putting together what, putting aside for a moment what we’ve, I know we’re all so tired of talking about it, but what we’ve all gone through for the last 18 plus months, right? Consumer expectations, anxiety in the consumer base are at an all-time high and brands are dealing with the reality that they themselves are dealing with new competitors. And then obviously the Goliath in the industry, the Amazons and the Walmarts of the world that just have so much distribution, they have a better supply chain infrastructure, and frankly, they have better technology even when it comes to CX. And then the last piece of it is we’re asking these people in the front line to deliver a Zappos-like experience with outdated tools and technologies and to Dan’s point about the turnover, like, why are we surprised?
Vikas Bhambri: (09:54)
Like, imagine you’re basically setting up an entire group of your team that is the frontline of your brand to these consumers and saying, “We’re going to set you up to go and fail every day.” So, I mean, who wants that job? So I think those are still challenges in the industry. And they’re only being heightened by the fact that the consumer is expecting more and every, the consumer just says, “Well, it’s so easy when I do it on brand Amazon or brand Y. Why can’t it be this easy with you?” And so their mindset is very purposeful from day one, from the moment they reach out and they expect a high quality of service.
Gabe Larsen: (10:33)
Yeah. I mean, there have been some bars that have been set on this, I think from an Amazon perspective that as small companies, it’s hard to deliver, but this is where I think some of these technologies, some of this intelligence can obviously help drive that. Let’s turn and shift a little bit to AI. It does seem like a lot of people are quick to dismiss AI for customer service. It seems like it’s maybe too much. It’s too, maybe it’s unreliable. We can’t quite figure out where to go. Why do you feel like people are not adopting AI right now Dan? Is it too expensive? They don’t know what it is? What’s your take on that?
Dan Watkins: (11:09)
I think a lot of it is, if you think about the last time you thought of AI, it was most likely in a movie and it was highly likely some version of a horror film or a dystopian society. And it’s easy to look at that. And then you go say okay, well companies that have bought AI or have implemented it in their company, they’re calling it AI, but it’s often not. It’s actually like [inaudible] lots of different companies we’ve been affiliated with. They go and they claim it. We go, we buy their software. It’s actually not AI. And then you get disappointed. It’s like even in the business world is dystopian. So there’s naturally a lot of mistrust, people not believing that the science is there to be able to go and deploy AI. And then if it is there, it’s probably only for one of those companies that can afford a seven-figure purchase.
Dan Watkins: (11:51)
And so our job at Forethought is to go and say, actually A, it doesn’t need to be that expensive. And B, it’s got to be real. And so one of the things that I was, when I was even thinking about joining Forethought and man, I called up the CFO, a good friend of mine, [inaudible]. And I wanted to ask him the impact that it was having inside of their company. And what was AI doing versus rules-based logic and what they’d seen? And it was the same story as I’ve talked to others, is that if you’re not incorporating the past, if you can’t go and look at what are your very best agents do across these hundreds of interactions or thousands of interactions, and instead you have to go and put if-then statements, you’re likely to be wrong. If you take your best agents, what they said, how they answered the tickets, how they routed the tickets, now all of a sudden you have real artificial intelligence. You have something that’s going in and understanding the sentiment and answering correctly.
Rose Wang: (12:44)
Yeah. I want to jump on that because I see it every day in the field, working with customers. Gabe when you were asking the question, there was this, lots of anxiety and there was like a hard to put a word on it, right? What is AI? Because even practically speaking, not even in the movies, we have AI that can barely answer the right question or the right content article. And then we have AI that’s driving your car and it’s all happening at the same time. So what AI are we really talking about? And so at least today for us at Forethought, when we talk about AI, it really is what we call deep learning AI. And I really want to focus on that learning piece because in the implementation process, if we’re not looking at your data, if it’s not, “Hey, we’re ingesting our data to look at the past, to understand how these many variables are correlated,” as Dan talked about. Whether that’s C-SAT score, agent tenure, et cetera. And we can say, “Hey, in this past, this type of interaction, there was this negative outcome and a positive outcome,” to keep training on the negative and positive outcomes. Then in the future, when there’s an interaction that’s a lookalike of that past, then we’ll know, “Hey, this is most likely the right future outcome.” So if you’re creating workflows, things like that, most likely not really, you want the AI to be telling you what to do.
Gabe Larsen: (14:03)
I like that. We’re getting tons of comments. Please keep the comments coming. I’m trying to get them up there as fast as I can everybody so please, please do it. But this one just came in from Karen on this. Rose, maybe you can finish here. As a rookie, Karen, we’re all rookies. Don’t worry. You don’t have to preface that. But how do you distinguish between that real AI and other attempts? You’ve mentioned workflow, you mentioned automation, you mentioned looking at the past and the future roads. Do you want to try to double-click on that?
Rose Wang: (14:28)
Yeah, absolutely. It’s a really hard question to answer, but I really want you to look at the implementation of the AI solution. That’s a really good signal to look at. Okay. Is this old AI or not as the deep learning AI is. And so there is, I’m going to give you two scenarios. So scenario A, is essentially older AI. It’s, hey, there’s this question. And I have to write this question ten different ways to then potentially guess at what is the right article because we want to hit on one of these ten ways. New AI is we don’t need you to write ten different ways to write that based off of the synonyms and the content, the actual contextual learning. We can just serve up the right article because as long as there’s enough data in the past, we can tell you what to do.
Rose Wang: (15:18)
So you’re going to see a much more technical team that’s implementing. You’re going to see that usually, the team’s going to come back to you with the recommendations about what to do first, once we ingest your data, versus you creating these if-then statements first. And so those are really good signals of this is the new wave of AI and it sits across all your intelligence and all your data layer so that as we build in the future, it’s not just your customer service data, but potentially your sales. It’s all connected.
Gabe Larsen: (15:48)
I like that. I think that was a good explanation because it is, we sometimes keep that higher level and we’re like, I don’t, it’s so, AI is such a big word. We don’t understand it. I love this from Jeremy, if you guys didn’t see this. I need in general, some PR Jeremy, I do. I think I agree to that. Vikas, what would you add for this one? Why is AI still just, Nah? It’s kind of just Nah. Why is that?
Vikas Bhambri: (16:12)
Well, I think like with a lot of monikers that we use in the tech industry, it gets kind of commandeered to mean a lot of things. And so there’s no true definition of what it is. And I think that’s what Karen was alluding to. I would say the other piece of it is, Rose mentioned this, your AI strategy is only as good as your data. And what are you actually, and I want to kind of tie this back to what I was talking about earlier in one of them, kind of the things of why we took our approach to the contact center market is because of all those silos. So if you’re only going to run your AI logic on just your ticketing information with no context of who your consumer actually is nd you’re not even taking into consideration, perhaps, the orders that they’ve placed with you, know you’re doing it on a very siloed set of data.
Vikas Bhambri: (17:06)
So your answers are only going to be as good as what you’re kind of targeting the machine at. So quick example, you create an AI algorithm or kind of thought process all around just your tickets with no concept of who your customer is. Well, guess what? The machine is going to learn that and say, “Okay. This is the resolution that worked. If somebody has this inquiry, this is the right response.” But they don’t identify with whether that was a high-value customer or a low-value customer. The high-value customer comes to your website and they serve up the same answer they would as a first-time buyer and that person’s like, “Hey.” That’s when the frustration starts to build. So now extrapolate that out. What if we targeted the machine to learn about the customers and their tickets or conversations, now you’re broadening that set. So now we’re creating the machine around, ‘Hey, our high-value customers, this is the solution that works for them. A first-time buyer, this is the solution works for them.” So that I think is also a failed attempt in the early runs at this is the machines only is going to be as good as the data that you offer to them. And I think people were really kind of feeding in some really bad data into the machine and then wondering why it wasn’t working.
Gabe Larsen: (18:26)
Yeah. It’s like garbage in, garbage out. Just popped this is Vikas. And maybe it’s one for you, but clarifying, when you talk about the article that served up in response to an inquiry, is this an article that a customer would see in a company’s FAQ or similar type section?
Vikas Bhambri: (18:40)
It could be. It absolutely could be. You know, it’s interesting. We have a lot of conversations about the future of the FAQ or the knowledge base. One, for most companies, they were extremely static and in a dynamic world, that’s useless. And it goes back to also the consumer. The consumer doesn’t want to have to go and search and find that article. So once again, how do you use AI to serve it up to them at the right time in their customer journey, with the issue that they’re trying to tackle? And you, a great example. If I own a certain product of yours and I go to your knowledge base and I look, maybe I own a mobile phone. If I was talking to a Telco and I go and I try to solve a problem, don’t show me all the articles for every phone that you sell. I own a Samsung. Show me the issues of how I resolve it for a Samsung, not iPhone and Motorola and everything else. So just that, even that mindset of how are you kind of tailoring that experience to the consumer is really important. Once again, do you know the orders that they’ve placed with you when you’re serving up the AI?
Gabe Larsen: (19:49)
And it looks like you got that one right. Thanks, Grace. Normally Vikas doesn’t get it right. So thanks for actually responding on that one. No, I just wanted to say, I think one of the challenges is I feel like AI is so much more prevalent in the consumer space. We see it in Netflix, we see it in Google Maps and stuff like that. But boy, that experience that you were talking about, Vikas, it’s so seamless in my personal life. And I feel like sometimes still in the B2B world, we have to train our own model. We got to hook up too many things that articles don’t come to me. I have to, it still feels like sometimes it’s clunky. And I think that’s one thing we, as B2B companies, could double click on, is this man, how do we make it so much easier so it feels like I just download Google Maps and I’m like, AI is working versus check, check, check, set up, set up, set up, train, train, train? Nobody wants to do that. One other question and then we’ll go on. How does AI put that personal touch that matters most to customer’s needs in a response? Can customers tell they’re talking to AI versus live? Dan, you’d better take this one. This is above my pay grade.
Dan Watkins: (21:00)
Yeah. So if you look at it, there’s a lot of research out there. I think once again, Qualtrics is leading in the forefront and showing what actually matters. Customers care really about two things. First off they care, do they have a self-service option? It’s something like 67% of people today prefer that they have a self-service option. So the first do you have that? Second off, if you have that, do you get the right answer the first time immediately? It’s about time to respond and accuracy of response so that you get that first ticket closure. And so will people be able to go and tell it’s in AI? Probably simply because of the speed. Human beings can’t type that fast. And so certain things that you go in and you ask the question, you say, “Hey, how do I get a refund?”
Dan Watkins: (21:43)
And there’s an instant response, a human being can’t copy and paste an article as quickly as AI can. So there are certain components of it that do that, but there’s also branding. If you’re working with the right partner, you’re going to be able to go and put your own brand in there. There’s the language that you use that will go and indicate that the AI can go and adopt. Maybe you have a funny lingo. Maybe you have a more serious lingo. Maybe you have more legal lingo. The AI learns from your agents. If you don’t have AI, it can’t learn your language. It can’t learn your tone. You have to go and type it in in every single case. And so I think that’s one piece. The other thing is I think a lot of people when they think about customer success or customer support today, they’re thinking AI is only limited on the front end.
Dan Watkins: (22:30)
So if an AI, just like a human being doesn’t know the answer, the AI never has hubris, so it shouldn’t be answering it. It goes in, and at least in our case, we then go and tag it. All that data that Vikas is talking about, it should now immediately be tagged. How angry is this customer versus happy? How long have they been a customer? And then who do I route it to? The customer should never know, other than once again, it’s faster because an AI can do all of that. In traditional customer support teams, you have to have a human being read the ticket, add all of those tags manually, then route it to the person and maybe they get it wrong. The AI, again, the customer will know an AI because it will be done faster, but they’re going to love that.
Dan Watkins: (23:10)
And then the AI should then go in and say, “Okay, what were the best answers we’ve ever given in the past?” Any human beings are ever given and recommend that to your customer support agent. Because just as much as you don’t want your customer to know if it’s an AI or a human being, you also don’t want it to know, is this a brand new employee or a veteran? AI should be enabling your new hires to act like veterans from day one when they start.
Vikas Bhambri: (23:34)
Yeah, if I think just to jump in there. I think Dan brought up a great point, which is a lot of people when they think about AI, and it goes back to your comment when you first spoke to your CEO, Dan, which is they think about bots. There’s so much more to it, right? It’s yes, that point of interaction that first happens with a consumer might be happening via a bot. But then the second point is how do you use the data that you have gained in that interaction to actually use it for routing? If it does have to be routed to a live human being. And then the third piece of it is how do you kind of empower that agent to be a super agent? And I think this is where one of the things going back to Karen’s question earlier, like where do I store it? You don’t always have to start at that front end. You can actually start out. And this is why your brand identity is so important where if you want that heavy, personalized touch upfront, while you’re trying to figure out the overall strategy, start with the agents, you don’t need to start with the bots. If you’ve got a team, if the resource is not a problem, and you’ve got a well-staffed team, then start with the agents and empowering them because that will also help the learning of it, where you can just allow the agents to add that personal touch, give them the right answer so they can move more efficiently and effectively. And then the machines learning from that as you kind of move your strategy back to the front-end customer experience.
Gabe Larsen: (24:55)
Yeah. I’m glad you guys went into this because it is. It’s almost unfortunate that I think AI has been, it’s now synonymous almost with like a chatbot on the front end. And I’m glad that you’re highlighting some of these other things because in some ways it’s great because I think there is some focus there and it’s an easier way to start. But man, that doesn’t mean you have to start there. It does look alike. Brooke gave you Dan, it looks like you answered that, right? We’re keeping, Dan has got one. You’re up next Rose. Let’s see how you do on this question. I want to keep going into a couple of questions though. I thought this one was interesting. Rose, if you want to try to take it. If a company wants to get to a point where they can better utilize AI, what steps could they take now to ready their organization to use AI? You mentioned data was important to make AI work. I’m expecting a score on this one so you got to say if she gets it or not.
Rose Wang: (25:49)
Be kind, it’s a Friday. So I love this question because as you all have said, people expect AI to just work out of thin air and that can’t be true because again, we need to learn from our past to understand how to act in the future. And so what are we talking about in terms of your data then? And so, I love the question about the content. That’s actually one of the best ways you can get yourself ready for AI because essentially, you don’t want a human to respond. You want to have content already there and you want to build it on a platform like Kustomer where essentially, we can hit an API. So every time that content is updated, you don’t have to tell us. We know we’re pulling or constantly updating with you. And so the more content you have, the more answers you have. And then the more questions we can go answer. So there’s that one aspect.
Rose Wang: (26:40)
The second one, which is you essentially want to have labels on your data. So we’re right now in the era of AI where structured data is better than unstructured data. And what that really just means is its kind of like an Excel spreadsheet. It’s better that you tell us, “Hey, this article is under this name, under this category.” It just helps the AI learn faster. And so these types of infrastructure architecture that you can build on your data really do help. And that’s why we love working with help desks like Kustomer, because you guys naturally build that in. If you just follow along and you add the fields, you add the comments, we can just work with your infrastructure and pull that data. It’s already structured for you. And that’s why we love partnering with companies like Kustomer because you guys help the customer get ready for AI.
Gabe Larsen: (27:35)
Yeah. That data flows. All right. You got to give us, you got to give her a thumbs up or a, well, just give her a thumbs up. I think that’s what we want here. We’re looking to slowly wrap here. A lot of questions. I love that. We’ll continue to answer them as we can if possible, but want to kind of move to close and maybe you guys can bring in a couple of things in your summary statement. So wanted to make sure we got to see how people were using AI. And I love that it’s not just chatbot. It’s routing. It’s agent enablement. It’s effectiveness on how you communicate with the customer, et cetera. But people are struggling with AI. It gets hard. It’s hard to understand. We don’t know where to start. It still feels like some people are further than me, some people aren’t. Where would you guys leave customer service leaders? What advice would you give them, leave them with if they want to start that AI journey and really take it to the next level? Let’s start with you maybe, Rose, if you don’t mind.
Rose Wang: (28:29)
Yeah. I think the first thing to do when you are looking at AI is, going back to what Vikas said, is there are many points in which you can start AI. It doesn’t have to start with a chatbot. So really identifying where in your business you need the most help. Is it with your agents? Or is it that agents aren’t actually properly routing your tickets because they’re so overwhelmed? So really digging in, identifying the problems there, and seeing what solution can map to that.
Gabe Larsen: (28:56)
Okay. Vikas, let’s go to you and Dan we’ll have you end.
Vikas Bhambri: (28:59)
I’m a big fan of the crawl, walk, run approach. It just oversimplifies things for somebody like myself. And where I often kind of steer customers towards is what are those top ten items that customers are repeatedly coming to you about and how can we best address them? And so a lot of times we work with, for example, a lot of retailers and Wizmo. Where’s my order. It’s kind of a hot topic for a lot of folks, especially as I said, the consumer anxiety built up. And people have been trained now to, I order something from Amazon, it shows up in two hours. So that mindset now, and so how do we tackle that? And it might be a multi-pronged approach. It might be about, yes, we need to serve it up to the customer at the point of interaction so when they reach out to us, if they can give us some identifiers, but maybe for whatever reason that doesn’t work, okay. Then how do we make sure that the agent is armed with that same data at their fingertips if it does escalate to that? So I think it’s very kind of in a way tactical to say, “Hey, let’s start with what is the volume that’s hitting your contact center? How do we start tackling this one by one?” And that in itself will morph into a strategy that you can then kind of put together more cohesively as you tackle these things.
Gabe Larsen: (30:21)
Love it. Love it. Yeah, you’ll never hear me argue against crawl, walk, run. I think we use that term like 50 times a day, Vikas.
Vikas Bhambri: (30:30)
It’s only because you and I can only tackle so much at a time.
Gabe Larsen: (30:33)
Yeah, it’s an excuse. All right, Dan. Bring us home. Bring us home.
Dan Watkins: (30:39)
Yeah. So I think that the best way to go and look at your customer journey, doesn’t matter really where you’re at, because you’re going to have some history unless you have no customers yet, AI is probably not a fit for you. And I don’t even know if a CRM is yet a fit for you. But other than that, there’s past data that your AI partner and your CRM partner should be able to look at. And so, no matter where you’re at, the first thing you should do is you should work with a firm that goes and looks at all of your past data. Where are tickets getting escalated? Where aren’t they? What is the sentiment across your journey? How effective are your agents? How quickly are you responding and where do you have gaps in your content knowledge? So work with a firm that can go give you those answers as a service, then use their technology to go on a journey with them. And so it doesn’t need to be overwhelming and it doesn’t need to be a bad customer experience. And before [inaudible], it was a partner with great companies like Kustomer to make it to where you start great from the beginning.
Gabe Larsen: (31:34)
Yeah. Yeah. Well, I love it, man. Especially if you can get that diagnosis part. You’ve got to find a way to diagnose it correctly. And I think if you diagnose it correctly, you can always attack the right problem. That’s a wrap for today. Really appreciate everybody joining. Dan, we’ll come back to you. If someone wants to learn a little more about yourself or Rose or Forethought, any quick comments for the best way to do that?
Dan Watkins: (31:58)
Yeah. Easily reach out to us on LinkedIn. We’d love to go in and help you. Anything we can do to help out your teams, whether you just want some advice, whether you heard something that’s interesting, whether it has to do with Forethought or not, we would love to go help.
Gabe Larsen: (32:10)
Awesome. And I did just post a link from the team here in the comments to maybe dive a little deeper into how Kustomer and Forethought are working together to use AI to transform customer service. So again, Rose, Vikas, Dan, thanks so much for joining. Everybody be safe, have a great weekend. Take care.
Rose Wang: (32:29)
Exit Voice: (32:37)
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