Advanced AI Data Strategies for Contact Centers

Transcript

[00:00:10] Steve MacDonald: Welcome everyone to the Contact Center Perspective podcast. I’m Steve MacDonald, your host, and today we have on a brilliant guest, a background that none of us have ever seen before inside of a company. It’s Kimberly Agin, and Kimberly, you work at KeyBank and you run a department that has business performance responsibilities, data responsibilities, AI, contact center. Your background is in sales and training in the financial industry.

[00:00:41] Steve MacDonald: It’s amazing the collection that you’ve brought together, but the insights that you’re going to share today are all about how we make the most out of this. We know we have to make the most out of AI these days, and we all know it’s going to transform everything we do in every job, but we don’t always know what, and specifically in contact centers, how do we use our data and AI to do something that we all know we can do, which is provide tremendous value to every other department inside the organization, to the point you’ve done that where you’re actually having departments come to you and saying, “Hey, let me run this by you. Let me make sure you’re involved in what we’re doing.”

[00:01:21] Steve MacDonald: Is it the exact opposite? Usually we’re fighting, we’re scratching and clawing just to get a seat at the table with executives or any department, and it’s just a standard operating procedure for you. So, before we get started, I would love to give you a couple of minutes, if you wouldn’t mind expanding on that background just a little bit, and even if you could touch on this, you are an inventor and you are an entrepreneur as well.

[00:01:46] Kimberly Agin: Yeah. Thank you so much for having me on. I’m so excited. So, yeah, I have about 17 years in banking experience. My whole career has been in financial services, from JPMorgan all the way to KeyBank.

[00:01:59] Kimberly Agin: I’ve done a variety of different roles. I was in sales and trading for a little bit. I did some market risk activities. I worked in line business finance, worked with commercial strategy as it relates to Salesforce and Service Cloud for the bank. Spent a little bit of time in client experience, which was a fun place to be, and now, I’m in the contact center.

[00:02:17] Kimberly Agin: So, I think if you had asked me 10 years ago if I’d be sitting in the contact center, the answer probably would’ve been like absolutely not. But the cool thing about the contact center, and something that I think that we at Key have really kind of leaned into, is it’s not just a place that people call in and we count how long they’re on the phone for or how many phone calls someone answers. In reality, it’s our clients at the moment of their biggest need telling us exactly what it is that they are having a problem with, and because of the infrastructure of the contact center, we have all of this really great, robust technology behind those interactions that allow us not just to see where they went on their journey in the contact center—did they transfer, who did they talk to—but also, we record all of the conversations, which really gives us this huge treasure trove of just deep, deep client insights because we are actually hearing from our clients, in their own words, exactly what happened.

[00:03:15] Kimberly Agin: So, when I landed in the contact center about five years ago, I realized we had so much rich behavioral data that would really help us understand who our clients are and what they mean to us, and not only can that help us improve how we serve them while they’re interacting with us in the contact center, but we also can take that data, we can pair it up against enterprise data, and we can understand in totality who our clients are from both a transactional perspective as well as an experiential perspective, and it gives us really deep insights into who they are, how they bank with us, what problems they have with products or services that we maybe sell to them. And so, we use that information to give back to our product owners, whether they sit in the consumer bank or the commercial bank or the private bank, so that we can really feed product development with data and insights directly from our clients.

[00:04:09] Steve MacDonald: So, amazing. I want to unpack so much of what you just said there, but I do also want to hear just a little bit about this new business venture that you’re on, because I just thought it was so amazing. Everything you’re doing there, that’s more than a full-time job, but you’ve got something else going on the side.

[00:04:26] Kimberly Agin: I think that I just am a person that whenever I see something, I always am like, “How can this be better? How can we make this better? How can we improve the problem that’s at hand?” And so, funny enough, I was on a girl’s trip with some friends, and I needed hair rollers, which I’m sure many people that listen to you use, and many people know that traditional rollers are bulky, and you can’t bring them with you. So, I actually invented a hair roller that unrolls completely, and you can store it totally flat, and it saves you 70% of your packaging volume. So, you can take it on trips, you can keep it in your bathroom, you can use it all over the place.

[00:05:03] Kimberly Agin: And one thing that I actually found to be amazing was two years ago, I was at a conference, and I’m sure you’ve been to many conferences, and most times at conferences, you’re there all day and you’re talking all day and you’re meeting with people, and then you go back to your room and you’ve got about an hour to get ready and go out for dinner and drinks.

[00:05:20] Kimberly Agin: So, for guys, it’s super simple, like you just change your shirt. For girls, after a whole day of walking around, you want to refresh your makeup, you want to do your hair. So, I never was able to bring products with me to refresh my hair, and this past year, I brought my actual rollers with me, and it was life-changing because I was able to just go upstairs, put some rollers in my hair, get a little bit of work done, refresh my makeup, pop them out, and it looks like I just went to the salon, or I blow-dried my hair from scratch. It made my life a lot easier. So, not only did I solve my problem on fun trips, I’ve actually really solved my problem on work trips.

[00:05:53] Steve MacDonald: Well, what I love about just the way it tells a lot about how you think about things and how you never rely on, and like, the status quo is never good enough, right? There’s always a way that we can improve things, and one of the biggest things that I would love people to take away from today is that we have a hard time, in the context of our industry, advocating for ourselves. We have a hard time proving our value to the rest of the organization, even though we talk to the highest of authority in the business, the customer, every day, way more than anybody else does, and we feel more like a support department, and I think that’s probably the polar opposite of what you’ve created here. So, I’d love to get in and just start with maybe even your philosophy about thinking about a contact center from a point of view of what kind of value you can create for the rest of the organization.

[00:06:49] Kimberly Agin: For sure. I mean, first things first, if a contact center is not able to answer calls and help a client, it is failing. So, lucky for me, we are a robust team and we have people who are focused all day long on that problem, making sure we have the right agents, making sure that they are talented, that they have the right information, making sure that our behind the scenes technology works, like everybody is playing their role and doing their thing.

[00:07:13] Kimberly Agin: So, because of that, I think it frees up capacity for teams like mine to then really go in and say, “Okay, we have all this information. What does that mean to us and how can we use it more effectively?” And so, I think oftentimes, a lot of places in the contact center tend to be a side thought. Clients are just calling, you are just answering questions, move them through your queue.

[00:07:33] Kimberly Agin: We were able to really gain a seat at the table because we were able to start to bring very rich insights about who our clients are and what they do, not just from the loudest voice in the room kind of perspective, not just because we went out there and the one agent was screaming at the top of their lungs that we have a problem over here and clients are calling about this, but because we were actually able to harness the power of all the data, put it into actual visualizations, run analytics against it, and then present it in a way that our business partners are comfortable understanding and seeing.

[00:08:04] Kimberly Agin: So, really that transformation of just operational mindset of get through the day to, “Let us take a step back. Let us really put some rigor around this. Let us make sure that the information is not only correct, but also valuable, and then let us come with data-led conversations to our business partners,” I think is what ultimately gave us that seat at the table.

[00:08:25] Steve MacDonald: And just to be clear, how many of those other departments or the C-suite were coming to you and saying, “I need more data-led conversations from you,” or you had to just take the bull by the horns and you had to prove, you had to create those conversations because nobody was asking for, nobody was expecting that?

[00:08:43] Kimberly Agin: I think people were expecting high-level call driver summarization. Nothing deep, nothing insightful, nothing strategy-driven, if you will. We really had to go on a road show, like we had to go out there and we had to say, “Not only do we have all this information, but this is what this then means to you,” and what that means to our partners in the consumer bank is totally different than our commercial partners and making sure that we understood what priorities were in each area to then be able to align our data and our kind of visualizations and our story, if you will, was really important.

[00:09:20] Kimberly Agin: Since we have done that, now those partners are knocking on our door and now I have dedicated teammates on my team who are responsible for client insights, for consumer and for commercial. So, we no longer are begging to get the seat at the table. People are coming to us asking for those types of insights.

[00:09:38] Kimberly Agin: We just had someone come to us in our consumer bank, we are looking to expand in a certain territory, and so they wanted to know from a geographical perspective, what are our clients calling about in that area and is there any insights there that can help inform the strategy for us to enhance and improve and expand our branch footprint?

[00:09:55] Kimberly Agin: We have got clients or teammates I should say, who come to us from the commercial side. They are looking to create a much more streamlined user experience from a commercial servicing perspective. Commercial banking is big and is clunky. There is a many-to-many relationship. You have many people at an organization who face off with many different people within a bank. They have complex financial solutions and problems and so how do we make that easier to service them? And part of what they needed to really understand was well, what are we even servicing them today and what are the volumes against that so we can start to prioritize where we are going to focus our time, effort, and money.

[00:10:33] Kimberly Agin: So, the Contact Center really was like the leader in helping to drive that strategy because we were the forefront of collecting all of that servicing data to say here is who your client population is, and this is what they do when they call us, and this is what they talk about, and then this is what we do on their behalf when we submit a service request. So, I think it is really cool that we are not just giving out our standard KPIs of AHT or average service, any of those. We are now really helping to lead product strategy, servicing strategy, and there are so many more examples of that.

[00:11:07] Steve MacDonald: So, let me ask the question because average handle times, these are the kind of things that when we are looked upon as a cost center, those are the KPIs. Everybody looks at us and says, “How do we contain that cost?”

[00:11:19] Steve MacDonald: We are one of the bigger overhead costs in the business. But now, when you are doing that, are you getting any more pressure about you as a cost center versus a value creation center, a revenue generation center?

[00:11:32] Steve MacDonald: Have you flipped the conversation instead of how do we shrink, how do we contain, how do we have lower average handle times, get people off calls quicker? Do you have any of those kinds of conversations anymore?

[00:11:44] Kimberly Agin: We do. I think that is just going to always be the nature of a non-revenue generating organization. So, something that we are really looking at on how to solve that is we are looking at the advancements in AI technology to help us move from non-human worthy tasks to AI and upscale and up-tier our talent so that they can handle the more complex.

[00:12:06] Kimberly Agin: So, I think every contact center who is not directly aligned to an external sales team is always going to need to think through that. I think that our insights though are helping to drive bottom line revenue numbers in other areas, business cases and feeders to those.

[00:12:24] Kimberly Agin: So, I think while we are helping to reduce the cost to serve and helping to shift work from even higher touch areas, we actually have done a partnership with our private bank where we found that oftentimes clients, because they know their banker, reach out to their banker and their banker’s job is to sell, but they now have a servicing task sitting on their desk, so they give it to a junior or an associate and that then often takes a long time because that person is not necessarily familiar with how to service a client.

[00:12:53] Kimberly Agin: So, we have done some analysis to say, “Actually, we do and can handle the same type of items in the contact center. So, we could actually take those off of your hands, have a dedicated vanity line for our private banking clients, and allow you to spend your time doing what you do best, which is selling, expanding relationships and growing the revenue for our private bank.” So, I think we are not shifting necessarily from a cost-constricting type of environment, but we are also now shifting to how can we actually help you reduce the cost to serve so that you can spend time in high volume, high quality at areas within sales organizations.

[00:13:32] Steve MacDonald: Wow. So, I have to tell you of every single podcast we have done, lots of conversations about how we use AI to be more efficient ourselves, but what you just talked about there is how could we help take on the load of other departments and are there things that they’re doing that aren’t efficient, that aren’t the best use of their time, that we can really help you with. That’s a whole another level of getting involved with other departments and even beyond sharing the voice of the customer and the data and the information that you have, which I want to spend more time on.

[00:14:07] Steve MacDonald: I want to talk a little bit about maybe you can just tell us kind of your philosophy on the role, the importance of data, the emergence of AI, and how you’re using that to provide, like you said, data-driven conversations and value to the rest of the company.

[00:14:24] Kimberly Agin: Yep. So, I think everyone knows that data’s important. I think everyone doesn’t necessarily know how to cultivate and create a data-rich environment so that you can, one, leverage that data, and two, you can trust the data, and three, you can then share the data.

[00:14:42] Kimberly Agin: So, when I first came in to see, the first thing that I really made sure that I did was I didn’t just want to have an analytics team who was creating beautiful dashboards and Tableau and sending them out through Power BI tools to our enterprise. I wanted to make sure that we truly understood what our data was and the importance of our data. So, I kind of stood up a data as a product team within the contact center.

[00:15:05] Kimberly Agin: So, these teammates of mine are attached to specific applications within the contact center. They’re part of the product lifecycle. They know who the technology owner is of that application. They work directly with the product owners. So, as we’re building new products and services in the contact center, we’re able to help influence not just what is happening in those applications, but also understanding what metadata is going to be important for us to be able to use and measure, whether it be against the application itself or against the client’s journey throughout.

[00:15:37] Kimberly Agin: And so, making sure that we always have our client-centric view. So, what is the widget that defines our client? How do we ensure that it’s in every application that we touch a client with, so that we can start to track that information. That was pivotal and that was huge for us to be able to do.

[00:15:54] Kimberly Agin: We then also wanted to make sure that it didn’t take my team days and weeks and hours and months even to get to that data. So, we created a very strategic, comprehensive data strategy to say, “Okay, let’s make sure that for non-critical data, so this is data that we wouldn’t spend a ton of money to put into our data supply chain. We still have a space for it to land that is automated, that is not regulated, but well-governed, has rules around it.” So, we built a data asset that allowed us to start to capture all this different data.

[00:16:30] Kimberly Agin: So, now we’ve got a data asset. So, now instead of having to go to all these different sources or some on-prem, some in the cloud, some in the data supply chain, we’ve got a single source for us to find our data and we also have the people that know the data the most working with that data. So, now we trust it and now we understand it.

[00:16:46] Kimberly Agin: So, we really spent a lot of time kind of cultivating our data environment. What that now has allowed us to do as we kind of move into the next iteration of the world, which is emerging AI technology is we have a very trusted source of what we’re going to put this AI against and so now our validation efforts and our efforts to leverage new technology isn’t going to be spent wondering where that data is, wondering if it’s correct, hoping that the answer it just gave us is right or wrong. We know.

[00:17:15] Kimberly Agin: So, I think for a lot of people, whoever owns the data and knows the data is going to win the AI future. So, we spent a lot of time doing that and I think that I’m incredibly proud of my teammates, the people that have worked on this, the people that every single day care deeply about those data things, are great assets to my team.

[00:17:36] Kimberly Agin: And the other thing that we also kind of noticed in this whole journey of spinning up this team who was dedicated and focused to this concept was it took a lot of pressure off of our application owners and our product owners because at the end of the day, when you’re in a product life cycle and you’re trying to make something work, you’re doing, like engineers are coding and product developers are giving requirements and then, it seemed like they’d get to the end and then everybody like, “What do we do with our data?” and after like project fatigue, it kind of was an afterthought. But we now can pull that up in the process and they don’t have to think about something that they’re not necessarily comfortable thinking about because we’re going to think about it for them and we’re going to give them the right data requirements for what we need. So, I think it really kind of turned a shift on how we develop products at the bank so that we can harness that data more effectively to be able to use it in the future.

[00:18:23] Steve MacDonald: You know, you said in there so many important things. I looked over and I wrote it down. I was so impressed by it. You said that you treated data as if it were its own product and by doing that, all of a sudden that raised its level of importance.

[00:18:40] Steve MacDonald: If you were to go and look up the importance of data in a world of digital transformation, which that terminology has been around for a long time, but like Deloitte talks about it as the difference between the business of today and tomorrow. That digital transformation has to have an underlying foundation of data underneath it that isn’t fragmented, that isn’t siloed, that can all talk together, and that is what’s going to help us take advantage of the AI revolution. If we don’t have the right data to feed, no matter what AI tool we’re talking about, if it doesn’t have the right data, it’s useless. Right?

[00:19:19] Kimberly Agin: Yup, and I think a lot of people don’t want to spend the time going in and making sure that it’s right, and I think oftentimes people kind of shy away from data because it seems really complicated, and it seems like you need to have some crazy math degree to be in analytics, and it’s not.

[00:19:35] Kimberly Agin: I think once you demystify the fact that it is just like any other product that we use, and you start to get to the fundamentals of what it is you need and how it looks and how to interact with it, it takes away that scary factor. So, I think a lot of people could probably put that discipline against a lot of different products and applications and get to a better place with their data. But I think that they really need to realize that it’s not scary and everyone’s just learning, and if you ask the right questions to the right people, you’ll find the answers and you’ll be able to move that strategy forward. So, maybe it’s my background in sales and trading, I don’t know, but I just look at it as a widget, and it’s just a different type of widget that’s super important.

[00:20:19] Steve MacDonald: Well, it breaks it down so it’s not scary because we could think like, “I don’t have a background in data analytics,” but you don’t either. Right?

[00:20:26] Kimberly Agin: I don’t. This was my first role ever in analytics.

[00:20:29] Steve MacDonald: Well, congratulations. You’re doing pretty good. You talked about in our previous conversation that there is literally a goldmine of data inside of the contact center that most companies ignore. Tell me a little bit more about what your point of view is on what that gold mine is.

[00:20:48] Kimberly Agin: So, I have kind of coined this as like behavioral analytics a little bit. So, I think most analytics teams think of analytics, they think of numbers. They think of very structured data points that they can look at and say, “Okay, we’ve had account openings go from 50 to 500,000. What happened there?” Like it’s a very linear number.

[00:21:10] Kimberly Agin: So, in the contact center, all of our data that we’re harvesting that’s super important and really valuable in my opinion is unstructured data, and it’s conversational data, whether that be voice conversations, chat conversations. That type of information I think is our huge asset in the contact center.

[00:21:30] Kimberly Agin: So, to get to the root of that data, because it’s 30,000 conversations or whatever, that seems to be scary and tedious, and it’s a different type of analytics that you need to go against that with. So, we like to call it topic modeling light, where we do some topic modeling to get to the root of what’s going on in those conversations. My team has built that, worked with that type of data now for the last four years. So, we’re super familiar with it.

[00:21:56] Kimberly Agin: It also actually, funny enough, transcends I think in a lot of other areas and organizations that we don’t really realize. So, think about every freeform text box that’s out there that you have a structured intake form, and then there’s that comment section at the bottom, and it’s like, tell me about blah blah, blah. That is all the same type of behavioral analytics data feed that you get from voice and chat conversations.

[00:22:18] Kimberly Agin: So, we didn’t shy away from the fact that it’s not numbers, but it’s words, and how do you start to bring structure to words? And I think what’s really cool is that with the advancement of generative AI, that’s going to make it so much easier for people to get to the heart of those insights because now you’ll use generative AI to classify, summarize, categorize all of that unstructured data and bring structure to it.

[00:22:42] Kimberly Agin: So, while my team has manually been doing that for years, we are running proofs of concept now to be able to do that with generative AI to get speed to market. But I think a lot of organizations, just because of the way that their analytics structures are set up, and most analytics areas that I’ve seen, especially in finance, are really finance driven, so they’re really looking for those structured tables with transaction numbers and numbers of clients and numbers of households, and we just really flip the script and we’re like, “We care a lot about those numbers as well, and that’s what we look at as our transactional data, but we also care really deeply about our experiential data.” So, that I think has been a huge shift in how we’ve been able to gather all that information out of the contact center, and again, we do that for our servicing application.

[00:23:29] Kimberly Agin: We help to inform how we make an easier servicing journey through our servicing application by doing topic modeling against all those freeform text box comments around other. Like teammates can go through and they can select this pick list, that drop-down list, et cetera, and so forth, and then at the bottom, there’s always a comment section that they write whatever didn’t fit into that free form. So, we’ll go through that, and we’ll help our product owners understand what else are you missing in a structured form to make it more standardized and easy to use.

[00:23:56] Kimberly Agin: And one example I can use for that is we wanted to look at rejection requests. So, when something comes into the contact center and our contact center teammate puts it into our servicing platform, that then goes to a back office operations team to fulfill, and we were getting a ton of rejections back on a certain type of product.

[00:24:12] Kimberly Agin: So, when we went through that topic modeling, we realized the reason we were getting a lot of rejections was because people were actually submitting things for dates that were in actual business days or not valid days because we didn’t have that blocked out in the pick list. So, then we added in there bank holidays, federal holidays, days that business could not happen, and our rejection reduced on that one example by I think 60%, just because we were able to then see that people were writing back and saying, “You can’t do this today because the bank’s closed.”

[00:24:45] Steve MacDonald: The simplest things, right? But if we don’t go looking, we don’t have the data and we don’t know how to look. And what’s being impressed upon me is how you are not accepting the norms. You are going a step further and a step further, and another way that you’ve done that is you talked about creating cross-functional teams that work together. If you could tell me a little bit about what that means and why that’s been a big part of the success.

[00:25:14] Kimberly Agin: Yeah. So, honestly, one of the reasons I came to this team was because of the structure of this team. KeyBank is a large organization. There are 17,000 employees, I think. We’ve got very siloed lines of business, technology, et cetera, and so forth.

[00:25:27] Kimberly Agin: Our team within the contact center actually all sit within one leader. So, we’ve got our head of workforce management who sits under that leader. We’ve got our head of line of business operations, we’ve got our head of technology, we’ve got a head of product and analytics, all to one leader. So, I often look at our team as a factory where our workforce management team is like that bubble in the factory, making sure that everyone shows up to work on time. If someone lost a finger, we’ve got a backup person there making sure that the numbers are being met by the end of the month, like they’re that glass bubble looking over the factory floor, and then our line of business operations partners are on the floor making sure that whatever the widget is, it’s getting through the factory and teammate morale is up and we’re getting through our queues, and at the end of the day, the factory is ready to close because there’s no work left behind, if you will.

[00:26:15] Kimberly Agin: And then we’ve got an entire strategic arm who is focused totally from a technology, analytics, and product area to make sure that the factory is firing on all cylinders for the modern era. So, are we migrating things to the cloud? Have we modernized our CKA system? What are we doing with our data? So, our factory continuously works because no one has to step out of their lane to worry about what another area of the factory is doing. So, when we operate like that, I think we can hit our numbers we need within the contact center because we’ve got dedicated teams focused totally on that, and we can modernize because we have teams focused on that, and then we can really harness the power of the data to help influence what’s going on in the factory, but then also help influence what’s going on around outside of the factory.

[00:27:01] Steve MacDonald: And this cross-functional collaboration, it also extends out to the different departments, right? Explain that portion of it as well, because that’s fascinating.

[00:27:11] Kimberly Agin: Of course, we are tied deeply into, I would say, our digital product teams on consumer and commercial. So, what are our clients doing from a digital perspective that they can’t do within the digital space, and they’re calling us in the contact center? How are we giving them information about what those calls are, what people are talking about, how they’re building a better product and service over there? We’re tied really closely into our branch teammates.

[00:27:35] Kimberly Agin: If you leave the contact center and you go out to the branch, why did you make that jump? Why were you not able to satisfy what was happening in your need in the contact center? And that information is helping us to inform, do we need to have a better way for us to authenticate in the contact center for people to do certain things? Do we need a better way to be able to accept signatures or present documentation? So, all of those things, in partnership with those external teams, are not only helping to build their channels and their products better, but are also helping us to build our strategy and our product better as well.

[00:28:07] Steve MacDonald: You know, we’ve talked about so much that I want to bring it back to a singular focus, and if there was one takeaway that you wanted everybody that’s been listening today, all of your peers, the executives, if there was one thing that you wanted to make sure that they took away from our conversation today, what would that be?

[00:28:29] Kimberly Agin: I think internally, like within my own four walls of the contact center, I think really understanding the importance of what happens in the contact center and the data of the contact center and embedding that in every area of the organization, I think, is important, and that’s selfishly for myself.

[00:28:46] Kimberly Agin: However, if I really could have every executive at KeyBank listening right now, I would hope that they would think about the importance of data and data strategy as it relates to not just finance and not just accounting and not just analytics, but really building comprehensive data strategies, creating omnichannel views of who our clients are and how our teammates interact with them, and spending the time and the money and the energy to really develop this data as a product across the organization. I think that they would be blown away with how much we would learn and how much we could help influence strategy, revenue, events, client interactions, teammate interactions, and I just would love for people to take data a little bit more seriously.

[00:29:39] Steve MacDonald: Words of wisdom, well said. You and I talked before: if it’s garbage in, it’s garbage out. It’s a very simplistic way to think about it. But we’ve got to work hard that we’ve got the right information, the right data that we’re putting into and analyzing, and taking advantage of all the things that AI and agentic AI and everything can do.

[00:29:57] Steve MacDonald: So, here’s what I know. People are going to have questions. I guarantee you that. Is it okay if we give out a link to your LinkedIn profile that people could reach out and contact you?

[00:30:07] Kimberly Agin: Yeah. That’d be great.

[00:30:09] Steve MacDonald: Well, fantastic. Our mission here is to have a podcast that brings contact center CX support leaders from all over the industry, all over the world, together, because if we can each share our own unique point of view about what makes us successful, then that’s like the rising tide that lifts all boats, and I just feel like you were such a super important part of that rising tide today. I just wanted to say thank you.

[00:30:35] Kimberly Agin: Thank you. No, I appreciate it, and I thank you so much for having me on.