- How AI will transform customer interactions
- How gathering customer feedback informs technological and human CX
- How and when to maintain human-to-human interactions for customer
Tom Martin (00:07):
All right folks, I am going to get started here. Hopefully everyone's getting a little bit of a bite to eat your flight in or your travel into Boca was an easy one, came down from Boston, and my name's Tom Martin. I am the CEO of Glance Networks. We are an in brandand collaboration platform that brings real people together in digital spaces, so you can provide genuine hands-on support at exactly the right moment. You today we are talking about evolving digital in human interactions in customer experiences with AI, it is at a time when I do not think we have seen more discussions, more topics about AI than in the past couple months. Of course, chat GPT is something that everyone is aware of. Kids going through graduation over the past couple weeks probably attempted to use some of the technology to finish up a paper whatnot. But the things that we are thinking about and having conversations with customers is really about how is AI transforming customer interactions?
(01:16)
What are people doing with it today? How gathering customer feedback informs technological and human experiences and how and when to maintain human to human interactions with customers. I am going to jump off the stage. I want this to be more of an interactive conversation because a lot of the things that I want to discuss is really about what people here are doing. The things that we have started to see out in the market have been quite broad. We have seen some of the logical things that people are doing around how do I extend support hours beyond the normal workday? How do I provide some quicker resolution to the simple things people are asking? Does this product fit with this compatibility issues, product availability, lots of information that's just readily available that AI or a bot or some system can easily grab at. And also thinking about behind the scenes with agents doing work in a call center around reducing errors, even things around when you think about some of the simple things that you are doing like a wire transfer or an AHT and being able to detect unusual activity around fraud, that may be something that you want to bring up.
(02:38)
Or it could be things like you see unusual charges with a customer or things that are happening, this is great for, or starting to see those things pop up, but also just routing calls or messages. We have seen a lot of companies trying to figure out how do I pair the right expert with the right person? And a lot of other talk that's happening, and I'd love to hear what other people are hearing, but when you think about delivering personal recommendations, how personal and what information are we basing it off of anticipating customer needs? I think of digital channels today. There is so many digital channels that are available, and it is not a question of if, it is really a matter of when does that digital channel fail? Customers will start down a digital channel and suddenly it starts to really run at its course where maybe it doesn't be able to provide the right information, it doesn't provide the right response, and you are like, okay, I've got to switch channels.
(03:37)
We know talking to Forrester and Gartner, over 79% of customers channel shift all the time, it is just the path for how they are going to actually do the task or do the business at hand. Channel shifting is just a way of life. And I think the other part of as we start to think about how we bring technology and digital into the experience is can it ever feel human? Can we ever get the right empathy? Because I think there's things around digital that digital does better, which is getting answers really quickly, but oftentimes it can be that conversation that you have with a customer that suddenly exposes other things, the simple things that you do to build rapport with a customer and realizing that what if we could take 80% of the volume that an agent is dealing with and actually remove it so that they can focus a hundred percent on that 20% where a customer's actually at a critical moment of need where there's a lack of information or I always think of, this is something that Columbia University said back in the sixties.
(04:53)
They said, humans do this better than anything, and it still rings true. Today is when the value of a transaction is really high, when the duration of that transaction is going to long has a long tail, and when your ability to understand alternatives is really low. So you think about going to the store and buying a gallon of milk, it is four bucks. You're going to use it in the next few days. If you do not like it, you can buy another one. And so you realize that when someone says, hey, so what type of milk do you buy? People are like, well, I buy milk. It's like organic or there's regular, there's 012 whole milk. These are all things that are pretty well known. So your ability to understand the alternatives is really high. But when you think about a mortgage and you go, gosh, I am going to spend hundreds of thousands of dollars, I am thinking you may be signing up for 30 years and I do not necessarily know all the different things like demystifying fees, you realize this is where humans can have a great impact on actually driving the right interaction. So a question to the audience. I am going to jump off the stage, but I am curious, where are people starting to see AI in their businesses and where are they starting to look? I'll walk around, I've got a mic that makes it easy, but where are people starting to use AI today?
Audience Member 1 (06:19):
I see a lot of it being used in chatbots. I think that that's one of the main things that people are using it a lot. I wouldn't say they're using it really well or that it works as effectively, but I think that's an area that I think it is emerging a lot these days.
Tom Martin (06:35):
I think that's a common denominator. Chatbots are used a lot in many different places. The question is where does it start and when does it stop? Do you have the ability to control how far it goes or does it, or do you just find out that it just runs its course to till the point where the customer's like this is no longer useful? You start to ask it. Some things that are very specific or the conversation or questions become really complex. Is anyone using AI to really help the agent? And I think about one of the things we are doing with a client of ours, Intuit, they started looking at AI as a way to interact with a customer, and they realized that the challenges were is that it became a runaway train. The train started really well, everyone was happy. Then the speed started to get a little uncomfortable, and then the train fell off the tracks.
(07:30)
And they realized that as they're looking at things like we are talking about TurboTax, we are talking about tax that changes. It's complex, and they've got certified EEAs and CPAs that are helping. But oftentimes someone will come in and be like, I've never filed a K one before. And suddenly you are like, well, I've got this question about a K one using AI through natural language processing to be able to do that translation and go out into a knowledge base and start in real time pulling up information for the agent. So now the agent at their fingertips isn't suddenly like, Hey, I got a swivel chair. Go do some research, come back. Do you mind if I put you on hold? Actually right there in the same screen, information is pulling up.
Audience Member 2 (08:17):
Right now we are using AI for summary. Let's say you have a call and your call is with that agent CPS agent, for instance, we call them, and you will have summary that was typed by our agent before and you would have typos and things like that. But now the AI looks at the whole transcript and put the summary for, and also the transcript is written as they speak. So yeah.
Tom Martin (08:45):
Is that summary also logged back into the system as well? So then it is now searchable and so a link to the case as well. Yeah, that's great. Other people, other use cases that people are starting to use, people are using AI behind the scenes. Anybody using it for looking at customer feedback, direct customer interactions. Just curious because part of this is one of the things in talking to some of the banks that we have been working with, there's a lot of interest, but they're worried that they're going to be out of compliance, they're worried about regulation, and they're starting to figure out where's a good place to start? Is anyone starting to look at customer data in terms of a customer data platform like a CDP, organizing all the data that you have around a customer and putting it into one system?
Audience Member 1 (09:52):
I was just nodding my head saying, yeah, we do see that. I also see maybe it is a different form of AI, but a lot of models that run in underwriting loan applications are also done with a lot of that knowledge. Because again, going to your point, it is about getting that data into one central place, looking at what a human would do and trying to mimic that behavior over time and get it right. So that's another area we see it being applied.
Tom Martin (10:19):
And that's something that's not being directly presented to the customer, it is being brought back and the agent can then review it and then send it off to the customer. Yeah, I think one of the things that we are seeing is that there's, AI can be looked at as, hey, there's some short term gains, but there's also a large term, longer term strategy I'll talk about in a minute. Sure.
Audience Member 3 (10:43):
Hello. Just circling back regarding your question about leveraging a bunch of customer member data, I think it is really about predicting, predicting why that person is standing in front of you if you are a teller or if you have an MSR or a CSR on the phone. And so there are a couple core platforms that are trying to leverage AI to look at the past behavior or past significant events that have happened on a member or a customer's account, whether it is an overdraw or perhaps a denial at a point of sale. And then that way it is popping up to that teller or that MSR customer service agent. This might be why this person is calling. And so it is about capturing that data and then presenting it back as a possibility. So that's been really useful.
Tom Martin (11:31):
And is this being used where in the business directly with banking agents or?
Audience Member 3 (11:38):
Any bank or credit union really for their frontline staff or member facing staff?
Tom Martin (11:45):
Right. Yeah. I think some of the things that we have seen with this type of interaction is when you take a look at data and start to analyze it, you can say, Hey, here's a customer who's got charges that are higher than normal. If the customer does call, could I actually provide information? Their minimum payment is adjusted or they may need different options in terms of payment plans, but you are providing all that information inside the workflow so that when you actually get in touch with the customer, if they do call, you are suddenly presented with all that type of data in real time. The long term strategy that I am talking about is thinking about the total experience and not just like, Hey, can we solve one channel using AI? But really looking at the total experience and thinking about how do we, how we actually want to interact with the customers?
(12:38)
So many channels were built and then we are iterating on them, we are trying to improve them, but then there's the challenge of like, Hey, is this actually the way we want to communicate with our customers? What if we actually looked farther back and said what we actually want to interact with them differently? And I think the other part is that there's a few things that when we take a look at AI and thinking about us using AI is how many people actually want to talk to their customers and let them know that we are actually using AI. I think this was last Thursday, Tim Cook was up on stage and he is talking about all the different ways that they're making things better in terms of how they're predicting pictures, how they're predicting information in terms of you are making texts and language suggestions. These are all things that are part of AI and they're talking about it from a machine learning perspective versus Google just did a presentation and all they're doing is talking about AI, AI 143 times. I think a lot of people are sort of questioning of, should I even mention AI or should we just really think about how do we improve the experience for our customers?
(13:55)
This happens to be a personal one here, communicating with the bank. One of our employees captured this the other week and basically was asking a very simple question, but they never got a response. And so this is the challenges of sometimes the question seems simple, but the question just can't seem to get answered. So back to the data piece, I am just curious, what are people doing? Are people thinking about data as a strategy as a way to start to think about? One is trying to understand what our customers are doing. And when I think about what our customers are doing, many times when we ask to say, Hey, can you actually draw out what the customer journey is looks like? Can you tell us what the customer is doing? And then we start to analyze the data and we said, well, you know what? The data is saying something different.
(14:48)
And so when we look at AI as a tool for improving customer experience, for improving the way humans are interacting, part of this is trying to understand exactly what are our customers doing and where can we actually insert AI to actually improve it. Anybody talk, want to talk or just mention about their data strategy or a little bit about how they're thinking about this? I mean, one of the classic things is that you could have a inside of a bank, you can have a customer who is a business customer, but also a personal customer. But those two parts of the business do not ever talk. And so you never get to understand the information. That could be really helpful to understand what that customer actually needs, where they are, and how you bring those things together to deliver a better experience. Not specifically about omnichannel, but talking about digital channels in general. And really just thinking about how you think about applying AI into the digital channel.
Audience Member 4 (16:08):
Well, we are looking at disseminating the customer journey and really understanding where AI really fits. It doesn't necessarily fit holistically at every endpoint, but we are trying to understand where the friction is and remove that friction with AI. So to your point, if there's communications between a customer and a systematic process or a human, is it repetitive and it is, what is that undifferentiated response? And if it is, then proceed with an automation response. But if it is truly differentiated, then you kind of need that personalized touch to really understand a big more macro understanding of that customer journey and why they took that deviant path instead of taking that golden path. So really just getting down into the customer experience. Is that kind of where we are going?
Tom Martin (17:03):
Yeah. And so when you think about a customer that gets to that point in time, do we actually, do you actually have a way to get a customer out of that automated journey and actually talking with a real human being?
Audience Member 4 (17:20):
Well, it depends on the outcome of the customer's journey. What are they trying to do at the end of the day and why did they interface with us in the first place? So that requires way more information, way more, and again, but when you talk about information, what information can you technically store release and contractually share? So there's different levels of data you are talking about here. So we are just talking about conversational, but conversational could have been NPI or API information in there. Do you share that or do you share a total count? Right. I honestly do not have an answer to that today. That's why I am here.
Tom Martin (18:03):
Yeah, I think there's a part which is the business today collects information from all different channels and from all different businesses. And so if you can find ways to bring that data together so that when you actually understand where a customer is, there could be a different response based on who they are based on the products that they bought in the past, based on the fact that maybe they've touched three times on the same product. And now we actually want to say, Hey, there's intent here. There is buyer intent. We actually want to get them connected. Go ahead.
Audience Member 3 (18:35):
Sure. Hi again. In my previous life there was some really great transaction aggregator companies that would essentially have really cool metrics that would analyze where a customer might be swiping their card, the types of purchases, and then based on some of their algorithms, it would then try to determine, Hey, if this person is shopping at Home Depot and they are spending many in a couple of other places, they are doing some home renovations and maybe you should be applied going after them with some sort of a home equity loan. So really leveraging that through predictive analytics based on their buying habits, what possible marketing opportunities might be available to the bank or the credit union that would resonate with what they're going through in their personal life?
Tom Martin (19:31):
Sure. I think of companies like Person Attics or others that are trying to do that type of predictive, like, Hey, they're actually doing a home renovation. Maybe they need a he lock. I think there's another part of looking at today, business processes have gotten really good, especially in the machine to machine. A good example, anyone watching a movie, and let's say you are watching something on Amazon Prime and you get an interruption midway through the movie, the movie continues after the blip, but it triggers a whole response to say, Hey, there was an issue. Can we figure out what the issue issue was? If we can't, we are still triggering this response, which is maybe we are going to give the customer a credit, we are going to generate an email. Before you've even finished watching the movie, there's an email saying, Hey, we saw there was an interruption. We are really sorry. We are crediting your account for the cost of the movie. Please enjoy this credit on us, and you realize this is a way that we can predict and respond to customers. The question now is how do we look at human data, not just machine data, how do we look at human data and try to do the same thing?
(20:49)
One of the things that Gallup did a recent study in a poll that basically just said, humans still want to connect with other human beings, but they do not want to waste their time and thinking about this even further, when you look at the National Business Research Institute, they basically said Human interactions are slow interactions, but they generate happy customers. And when we think about this is how do you create that time space and realizing that what we want to be able to do is what if AI has the ability to identify when you actually want to put a human being in front of the customer to actually inject that human experience in part of a journey because we know it is going to drive the right types of outcomes. And so thinking about not just having people just jump out of a channel, but being able to say, gosh, you know what? We are seeing a customer go down this channel, we are going to inject it here. And this is back to that building rapport, being able to listen, validate the customer's feelings, their concerns, solve a problem, but then to listen and find out what they need.
(22:03)
Can anybody willing to give us a little example of some of the things that people are doing with AI again, or where they're thinking about it or where do you see an opportunity in your business?
Audience Member 8 (22:23):
I'll chime in. When we are looking to help, we really have financial advisors and as they're calling in and their branches are calling in to ask questions, making sure that the person has the right information. So how can we have AI to surface the right information to the support desk so that that person is empowered and has the right information so they can answer the question without passing the advisor on to the next person, the next person, the next person, sort of back to whoever's calling in, wants to talk to someone, but they want that person to have the information, which is very difficult to know everything, but how can we leverage AI and help educate that service personnel to be able to answer the question efficiently?
Tom Martin (23:17):
Yeah, I think the call it first call resolution or just recognizing that the customer doesn't want to suddenly be handed off because oftentimes handing off means that you have to sit there and say, what's your name? What's your social security number? What's your account number? You have to kind of restart that process, so the desire to actually solve that problem. Other examples that people are using AI that would be helpful.
Audience Member 9 (23:45):
I can chime in on a couple use cases we have used. Our predictive analytics team has used the data to analyze customers that are having cashflow issues that may need a small dollar loan, and we have a automated small dollar loan program that they can apply for and have money in their account within five minutes. And we have targeted to those customers that may need that or have and helped some people get out of payday lending cycles that they've got themselves trapped into. Another one is somebody mentioned underwriting. That team also built an underwriting, predictive, predictive model of for loan approval that the bank is now using as an alternative way of approving loans as opposed to credit score. And then we have also done a checking account optimization project to get customers into the right checking account to maximize their rewards. And that's been successful.
Tom Martin (24:48):
Yeah, one of the other areas as an example that we have seen is in really understanding customer feedback, and that feedback to me is probably one of the most important pieces because one, oftentimes feedback is pretty low. So one is, can you actually improve the quality of the feedback? And many people are reluctant to provide feedback because one, they do not necessarily know the amount of time or investment that they have to put in or invest to getting that response. There is sort of a sense that maybe the feedback is not going to be actually listened to. I've got a good example. I sent a note. They asked for some feedback from an airline from a recent flight. I gave them a long letter and I asked some and they said, would you like someone to follow up? I said, yes, please. It's been two months. No one's ever followed up.
(25:41)
So I am like, wow. I mean, I took probably 20 minutes to write a really detailed response. I thought it would be very valuable to them that they'll be like, wow, this is great feedback. Someone actually decided to invest this amount of time. And so thinking about social listening, but using that AI to really do something that humans do not do very well, which is how do I take a massive amount of data, distill it down, figure out how do we respond, and then respond really quickly to then be able to get more feedback. So it is not just getting the initial feedback, but it is like, Hey, we heard you. We want to engage you, and now we have the opportunity to flip because anytime we have a customer that suddenly we can improve their opportunity, I think that becomes super helpful. And so looking at companies that do things like social listening, like Sprinkler, that does a huge amount of, social listening, but being able to use AI as a way to take customer feedback and use it as a tool for re-engaging customers really effectively. I think that's something that's becomes really important.
(26:45)
How many people are thinking about how the digital and human interaction works? I am going to assume anyone who's working for a business that's dealing with customers, you are predominantly using humans to interact with them today. And the question is, how do you see that interaction evolving? How do you see the future of that human capital as well as some of the examples that people have given, which is how do we make those agents or representatives of the company happy? Because if we can give them information to do their job, we know that they're going to have better outcomes, they're going to deliver better experiences to our customers. So how are people dealing with that change and what is their vision for how the human being is going to be at the center or at a critical role of the experience? Someone's got some ideas. I know. I think here we go on.
Audience Member 5 (28:02):
So we invested into a Medicare company and our call center has implemented a technology called Balto, and it was just a widget that sat on the side of the desktop, but it listened into the conversations and pulled out keywords for the agent to hit on, and when they were going down the wrong path, it brought them back on track. We noticed this huge trend. They started to close more deals or they got to faster nos, and so they were spending less time on the phones. Agents started getting larger paychecks becoming happier, and we were able to recruit more based on just that simple tool being implemented. Although I am not sure it was ai, it wasn't learning off. It was still a simple script that a human had to put in there, but it is the start of something, right?
Tom Martin (28:50):
No, I think that's the very first step that suddenly is like, wow, we actually are saving something. We are improving. We are creating some level of consistency, but it is the beginning of something. Other thoughts on humans? I think of we are all consumers, we are all consumers. We all have our own experiences. Maybe I'll ask the question of where have you actually had a great experience where you suspect that maybe the human is being assisted by AI or where you are starting to see some of those?
Audience Member 6 (29:29):
I just build out a digital bank, and one of the things that I actually struggled with early on was people still want to do business with people. You had it on your slide, but how do I do that in just website format, in just a cold website? Because I want to build relationships, not just transactional business. So I found, in fact, I say I put the humans in the technology, people looking for a digital bank experience, for the most part, want to self-serve why they went there that do not want the old traditional brick and mortar location, but at the same time, they're going to want to interact. So it is like, okay, how do I replicate as close as I can that in branch experience of a brand new customer coming in and being welcomed, but yet do it in a digital platform? So I put that in some of the technology and that's what we use. So it is building those kinds of relationships.
Tom Martin (30:29):
Question was where in that journey was the AI?
Audience Member 6 (30:33):
I did a lot of AI at that point. So the whole AI conversation, it was out there, but it wasn't as now my, that's part of why I am here is my next thing is how do I take that and move process?
Tom Martin (30:57):
Yeah, AI.
Audience Member 4 (30:58):
AI systematically learns over time. It gets smarter, but it is only as smart as, it is only as intelligent as the programmer themselves. And it sounds like you are building a digital platform, meaning that you are going to build funnels and tests, so you are going to split legs. If you split legs, the AI would technically hop the legs and converge. How would you prevent that though? I am just curious. I want to know myself.
Audience Member 6 (31:25):
I probably haven't delved in to that level at this point. It's a little part of the learning journey, but my first goal was to get this thing live and actually bring business in. Okay, now I will refine as I learn new technologies. This stuff is happening like that and stuff. So it is an ongoing process, but we can talk offline if you want. I'd love to chat with you. Yeah. Okay, great.
Audience Member 7 (31:54):
To touch on what you said, I think it is not about the developers, but I think it is about the knowledge base and what information they're provided with. So I am customer experience.
Tom Martin (32:11):
Yeah, you think about digital banks, Axis born in the internet. They've never had a branch, they do not have any physical location, but what we have seen them doing is using AI and about to start the journey to be right there. So as someone is going to do, say a loan origination, they're going to go through that loan process, but that bot is sitting kind of dormant, and then it'll pop up and be like, how are you doing? They're doing a little question of how are they doing? I am doing great. And so you are giving a score, and so they're going back and it could be like, Hey, this is a difficult part. Do you need some help? They're like, yes. How would you like to connect with us? And it can start to give them, Hey, bot will walk you through a knowledge base, or it could be like, Hey, actually want to speak to someone, and then they're going to elevate that call.
(33:12)
But the idea of sitting there and saying, you know what? We are going to have an assistant ride with you. They're going to be kind of dormant, but that assistant's going to always be there. And because they're there from the very beginning, they're collecting information, customers are doing well at this stage, they're not doing so well at this stage, they actually need help, and what type of help do they need here? And so that's where that AI suddenly becomes, Hey, we are learning enough so that maybe they're going to change the response, and they're finding out that we now get customers through a loan origination process all the way up to this point, and then we are actually connecting them with a person versus starting them from the beginning. A lot of times people want to be able to get started. They're digital native, and so they do not want to bug someone.
(33:58)
They feel like, ah, I am going to bug someone, but how do I do it? So we have seen that work really well in talking with folks that like AX oss about how they're doing that and it is improving loan origination, loan Depot, another one that's doing that, but they're going through the process of recognizing they're doing loan origination across the country in many different states that are using AI to help agents understand all the different fees and associated things that are happening in different states, different regulation, because there's tons of stuff. So how do you actually get access to all that type of stuff? So back to being a super agent, realizing that those are important things. We are seeing that with, we are working with companies like Humana or other health insurance companies that are providing service and care across all kinds of many different states, and you realize each state is different, radically different.
(34:58)
You thought the regulations or the requirements were the same, they're not, and it is amazing how different they are. So getting someone who's actually certified to be able to speak about the state of Oregon or the state of California isn't easy because suddenly you are like, wow, I got to look at who this person is, where are they coming from? And then do I have an available resource to be able to do it? So what if the thing is I can actually use the AI to get them all the specific information and then I understand exactly what they need? Just back to the super agent,
(35:39)
One of the things I was trying to draw out of the crowd a little bit is just thinking about the larger experience of AI. A lot of people are like, where do I start? There can be things that you start off with just what I'll call the simple tasks of, Hey, we are going to start to experiment with some of these things to help a very specific journey. And I've started to look at all the different use cases. The other way is to really think deeply about the long-term vision, which is how do I understand everything about my customer? So I am going to really look at becoming a data company inside our business, and we are going to start collecting data from all different walks of life to really help us re-envision what those journeys could look like and what the future of service with our customers really is.
(36:26)
And once I start to have that data, then I can really start to attach it to the types of intents and outcomes that I want to have with our customers. Oftentimes, I always sit there and say, when you can meet a customer in that human to human engagement, oftentimes you know, solve a problem much quicker. You then create an opportunity to expand the conversation. What else can I help you with? And every human being in this room, you have a perception of what an experience is going to be like. And when you over index, when you exceed the expectation, you are like, wow, that was easy. Hey, you know what? I have another question. Maybe there's an opportunity to grow, wallet share, or expose them to other parts of the business. I think the other part is that humans want to do business with other human beings.
(37:16)
They want to do business with humans that they know and trust. And so how do you make sure that at the forefront, you are injecting those pieces together? And so thinking about how you bring digital and humans back together and really rethinking about these touch points, if you go back and talk to some businesses that are deep into this, you are like, well, how many use cases do you have for this line of business? They're like, we have 324. We have looked at every little possible machination of how a customer's going to go through us. Other people are like, well, we are just starting on our first five or 10.
(37:54)
The part about maximizing every customer service interaction is really looking at every touch point as an opportunity. There is so few times to be able to touch a customer and you think it could be a bill, it could be a chat, it could be a service engagement, but really thinking about how to bring those things together so that you are really delivering value for the business. One of the things that we are trying to figure out at Glance is the part which is can AI understand enough about a customer's journey to say, you know what? This is when we want to be able to engage them back to the point where so many digital fail, it is just a matter of when. It's not a matter of, but if you could really think about this from the standpoint of what if engaging with a human being is not about helping solve a problem that they weren't able to solve through another channel, it suddenly became an intentional channel to say, we want to be able to inject the human being at the right moment.
(39:01)
And so instead of being sort of a tow truck that pulls a customer out of a ditch on their journey to solving X or buying X product, what we are going to do is weave it into that journey. So that becomes the most important part. It's the catalyst that actually improves the parts of the business that they want. I will tell you we are working with a retail customer that does high-end product, and they're giving customers the option of like, Hey, how do you want to communicate with us? You can start here no matter where you are. You can always say, Hey, you do not actually want to have a concierge experience when they touch a human being, they're having a 10.1 x conversion because suddenly they're going, gosh, if you are going to buy that, you need these things. You know, think about digital, especially if anyone shopped on Prime.
(39:52)
You're like, customers have bought this, have also bought that, but is it smart enough to know what I am actually doing and what I need? I think the other part, the last piece that I am trying to really bring home is many people are inside of a contact center or inside of a digital team, and we are working with a bank that's like, Hey, our goal is to actually bring, we have got three digital teams. They handle this, they handle this. We actually are trying to bring those teams together, but they're all working on their own initiatives. And so how do you bring a total experience perspective to the business to bring all those elements together to really reimagine and think it through? I know we are winding down here, but those were some of the final thoughts I had. Any other thoughts that people have that they want to share?
Audience Member 2 (41:06):
I think to bring it all back together, at the end of the day, your AI is as good as your data management, your data governance. So moving forward, it is like how do you leverage AI but also leverage data? And how, I do not remember, I think it was first the company you said that was triangulating data. I do not know them, but yeah. Anyway, I think at the end of the day, looking forward maybe in five or maybe in one year looking at data, how we can exchange it, but in a very secure way so we can better triangulate those data point and better serve our clients.
Tom Martin (41:51):
I think that's a really good point. Just to reiterate the fact that data, bad data in bad data out, garbage in, garbage out, but realizing that the thinking about data as a strategic asset of a business and how you use it and leverage it before you really start diving into AI because you want that information to be really good. We know that customers that have a bad experience go away, and AI can be a bad tool or it can deliver bad outcomes when you are putting it in place to have Good, any other thoughts? Yeah. Oh yeah.
Audience Member 1 (42:35):
So I think we talked a lot about collecting data digitally. So in our company, we record all of our customer service phone calls and lots of data is in there in form of speech. So when you convert that into text, one example was we were trying to find out in a particular use case, what was the consumer's pain point? And when we looked at all of those recorded phone calls, the mention of, I wish this could be available online, or I wish this was available in an email, stood out so many times they were like, okay, all they want is an email to confirm this interaction. And that was a really easy way to solve a pain point that was sitting in all of these phone calls recorded all this time, and it really took some time to take it. Okay, let's convert that into text and figure out.
(43:21)
And then there's so many minor products out there nowadays that can take that parse that going back to someone who said, triangulate that to really tell you what to solve for. The one other challenge you see nowadays is that when you say people like interacting with other human beings, and this was again in a previous life, but we used to have this amazing technology where we would answer emails within minutes of receiving something, and that was a pain point for some of our consumers. How can you read this so fast? Obviously you didn't take it into account what I was trying to say. So I think balancing that out and figuring out, okay, what is too quick and what is something that takes time, at least from a consumer standpoint, I think is also important. And then the last thing that I'll add is that this concept of consumers, like talking to other human beings is really about the difficult situations. So my goal, at least for my company, is to take out the easy things that I know as a consumer, I wouldn't want to spend time talking to somebody about it. But then there are interactions that require a proper conversation. So how do you facilitate that and make it easy to get to? So again, you are not clogging your phone lines, you are not clogging all the different ways to do that, but I think balancing that out is the key in my mind.
Tom Martin (44:46):
Yeah, I couldn't agree more that 80 20 rule of how do I get rid of the stuff that can be handled better? And in fact, customers are looking for it. They recognize that they want it better, but it is the deeper, more thoughtful moments of need that are critical to a customer where having that human interaction and you get to spend time, it is a slower experience, which becomes a happier consumer. Alright, I know we got to wrap up, but thank you for joining me today. Thanks. Hopefully you had a good lunch. Any final questions? We are going to be out here in the booth in the trade show floor, so please feel free to stop by continue the conversation. I know this is going to be recorded for other people to be able to view at another time. Thanks everyone.