Is Your Data Strategy Future-Ready?

Jim Marous:
Hello, and welcome to Banking Transformed, the top podcast in retail banking. I'm your host, Jim Marous, Founder and CEO of the Digital Bank Report, and co-publisher of The Financial Brand.

Jim Marous:
Customers expect their financial institutions to use data insights collected over time to deliver personalized recommendations based on transactions and behavior in the past. Fortunately, banks and credit unions have access to solutions that can drive insight discovery, outcome predictions, and task automation. The challenge is to implement these capabilities at speed and scale that the customers expect.

Jim Marous:
My guest in the Banking Transformed podcasts are Greg Gruning from Segment, and Brian Lindenmann from Busey Bank. Greg and Brian will discuss how banks are using data and AI to drive digital transformation and grow revenue, by optimizing the customer journey across channels.

Jim Marous:
Welcome to the show today, gentlemen. Banks and credit unions really need to understand how valuable each customer is and where these customers are across their customer journey. However, truly understanding the customers, and marketing to them appropriately, is still difficult for most institutions, despite all the technology and data that's available. The challenge is closing the loop on the data, insights, and the deployment in such a way that's most effective and efficient. This must be done at speed, and at scale.

Jim Marous:
Greg and Brian, before we start, could you provide our listeners a short bio, a little backgrounder, on who you are and what your rules are?

Greg Gruning:
Yeah, thanks Jim, and Brian, thanks for joining us as well; appreciate it. My name is Greg Gruning with Segment, Chief Revenue Officer, which is everything from the client success team, to business development, to the sales engineer side of things. Been with the company, employed '12 or '13, eight plus years doing this. Segment's been in existence almost 15 years. We are focused in on the financial vertical, and we are a data company that drives, exactly Jim, what you talked about. We've cracked that nut of being able to make that data, that rich core data, relevant and actionable very quickly. We're a data company that drives relevant marketing.

Brian Lindenmann:
I'm Brian Lindenmann. I'm from Busey Bank. Busey Bank is a mid-sized bank. We've just gone over a $10 billion threshold, so we are an ever-growing bank. We're a mergers and acquisition bank. Over the past five years that I've been here, we've acquired at least five different banks, and so with this need of growing, we need to look at our data. They hired me on as a Business Intelligence Manager to overlook our data, be it the reporting and storing for master data management, and pulling insights.

Jim Marous:
So we have both of you on the show today. It provides perspective of how Busey Bank uses data to create better experiences, as well as how you drive revenue. While we have Segment on this show to discuss the challenges and opportunities that they see in the marketplaces and what stands in the way of doing things the way we really like to see them done. Brian, your responsibilities include overseeing the use of data at Busey Bank. Your organization decided to use a third party provider to assist in maximizing the value of this data. What has gone into that decision, from the perspective of speed, scale, trust, security.

Brian Lindenmann:
Yeah, we tried doing this internally, and we quickly found out that the scalability of our teams was not there. We needed to have an expert that understands how to manage large data sets, and analyze that data, so we looked out to vendors, and Segment was one of them. They were the leader of the various different vendors that we looked at. The keys that we looked at with our vendors was how they treated our data from a security standpoint. Can they take in our data, secure it, and also not be able to pull insights from our own data that we want to hold onto? We want to make sure that our customers information is safe, and it cannot be sold by others.

Brian Lindenmann:
Segment was great with that. Their ability to understand how to do the analytics, they are a powerhouse of understanding of pulling data in, connecting to different systems, how to normalize that data, how to cleanse that data, and then how to categorize that data to make sure that it's meaningful for what we need.

Jim Marous:
You talk about the size of your organization that's right around $10 billion. The vast majority of organizations are really around that $10 billion amount, dollar mark in assets. We have smaller ones. We certainly have bigger ones, but when you're at your size, I would imagine, and you're an acquisition bank as you've reference, so you've taken in new data sets as you're going along the process. Have you found the fact that one of the challenges is resources, both monetary as well as human resources? How does Segment, and how does a partner like this work with you to be able to standardize your data set and actually get you to be able to use it faster than maybe you would've been able to use it yourself?

Brian Lindenmann:
Yeah. Segment is a great partner to work with. They have a standard approach when working with these banks like us. We tried working on setting up our own connections with our core system to get the data and analyze it in a way that is beneficial for us, or valuable for us. Like you said, we may not have the resources. At the time when we started this project, we didn't have that. It cost a lot of money to have a data scientist, to have a manager over the data scientist, to have data analysts, which are totally different from data scientists. You have to have this big group.

Brian Lindenmann:
Bringing in Segment, they had that template that said, you know what, we've got this group of folks that will help build this out for you, and help build the KLIs, the Key Lifestyle Indicators that will give you that insight on what you're looking for. We already have it set up for you. All you need to do is have one resource on your end that understands how to map the data from your core system into our system, and then you can call it a day. So for us, it was real beneficial.

Jim Marous:
Greg, as we look the power of data and insights, how do finance institutions get a deep understanding of account holders and target them with relevant offers without the use of personally identifiable information?

Greg Gruning:
Yeah, it's a great question. Again, almost been doing this for 15 years. What is core to everything that we do is, we don't take any person identifiable information. We don't know Brian as Brian, what we do is we tokenize each one of the customers, and I promise not to get tech, but we call it, it's a Segment word, UCIC, a Unique Customer Identification Code.

Greg Gruning:
When we extract that rich core data, and any third party data that Busey Bank would have, we have the ability to take that, it's all tokenized and assigned to that token, that UCIC. That gives us that holistic 360-degree view, as often as the core refreshes, in this particular case it's a [inaudible 00:08:20] Silver Lake, right? To be able to do that as often as it refreshes and even up to real time, the ability to take all of that messy, transactional data, which Brian has talked about, and we talk to institutions small, big, anywhere in between; that's the first problem, is okay, I realize I'm sitting on all this rich data. One is, a lot of times it's in silos. Can you help me garner all of that and give one single source of truth? That's number one.

Greg Gruning:
Number two, I can't make sense of that. We built out a taxonomy and this taxonomy, every minute as we're talking, is evolving. It's a lot like fashion; it never stops, it evolves. Every day, every minute, the hundreds of institutions in the platform, we're seeing all those transactions, we're cleansing it, we're tagging it, and then we're going to make it easy for an institution like Busey Bank and Brian, to be able to understand it. That's put into the metadata tags, or what we call KLI or Key Lifestyle Indicators.

Greg Gruning:
Jim, to your point, that then brings to life not personas, but each customer in this particular example that Busey has. We'll know competitive KLIs, what's leaving the institution to whom, to where, the frequency down to the brand. We'll know the intent where they're spending time on the various pages. Is there intent to purchase a mortgage because they're spending time on particular pages within their public and private sites? We look at activities and interest, who are the extreme athletes, who are the shoppers again, all to make that emotional connection, that relevant connection, the customers.

Greg Gruning:
Then finally, we look at how they're utilizing the products and services of Busey Bank, just because they've opened up a HELOC, or a debit card, and they haven't activated, much different message than somebody that's drawing upon that. We bring the life [inaudible 00:10:18] in a really simple high categorical way through our KLIs. Then Brian, with his team and the Client Success Manager of Segment, you can then cross-reference those KLIs to build relevant one-to-one messages in a matter of minutes.

Greg Gruning:
Then you can deploy those to assisted and unassisted channels, and then my favorite area of it, I can't help it, I was driving my parents nuts when I was going to college, finance, marketing, salesperson, is the financial piece of it. When I talk to institutions, we're not a marketing company, we're a data company that drives relevant marketing. I want to make sure that's very clear. We can show the demonstrated ROI because we're seeing the conversions by ingesting, nightly, that data. We see the conversion. We know and mark that based on the financials of Busey Bank and the first year economic value. All in a box, the end to end piece of it, we can provide not only that cleansing, tagging, the KLIs, building the audiences, the delivery, but then the demonstrated ROI.

Jim Marous:
So Brian, big picture, how does Busey Bank use data intelligence to drive operational strategy and marketing strategy?

Brian Lindenmann:
Yes. Busey Bank is using data intelligence to target relationship growth for prospecting on our current customers. We wanted to drive into that customer behavior, and see what are the next steps to really build out our relationship with them, because without that relationship, like you've mentioned before, we would dwindle away. Busey Bank would no longer have customers. We're not here to create new customers and gather them. We want to first gather them and then build them out. With Segment, we're able to do that. We're able to look at that information and understand that behavior, and that marketing that we have driven is all driven around that behavior. What are their next needs? What's the next best product?

Jim Marous:
You're cultivating all the opportunities and challenges to try to not only act on opportunities that just jump out at you, but also to predict some things that may happen. I think it's interesting, Brian, that when we talk about this often, we realize this is a real change in mindset, when it used to be product driven. We would have an equity credit program in the Spring. We'd have an auto loan program in the Fall. We'd do one big deal, but we wouldn't take advantage of all the opportunities that happened in between.

Jim Marous:
I'm not seasonal. Yes, there may be more things in the Spring, but in that process, I would've lost the Summertime equity credit user, or the Fall equity credit user, or the person that buys a car in February, let's say. Really what you're doing, both operationally and from a marketing strategy then, as I understand it, is trying to find those opportunities on a second-by-second, minute-by-minute, day-by-day scenario, correct?

Brian Lindenmann:
That's correct, and what we refer that to as drip marketing, because this allows us to continuously set up a market strategy and then let it run all throughout the year. Whenever a customer is ready to hit that campaign, then we can market for them, like with customer KLIs from Segment, we can see multiple different facets that can drive a specific strategy. Specifically, like HELOCs or home equities, we can use various different KLIs from Segment to not only identify if that customer has a current mortgage, either with Busey or with a competitor, we can combine that with their spending habits. We can see if they're purchasing from a home improvement store, like a Home Depot or a Lowe's, and they're spending a lot. That combined with that other mortgage KLI, we can set up a marketing campaign to identify if that customer is eligible or not really eligible. It's a prospect of getting a home equity.

Jim Marous:
That's great. What's interesting, Greg, let's talk about the elephant in the room. What I mean by that is, I don't know of one financial institution that doesn't think, you know what, I'd love to do something with you, but now's not the time, my data sucks. It's in numerous silos. It may not be as clean as it should be, and it certainly isn't deployed across the whole organization. What do you say to organizations like that, that say I'm just not ready?

Greg Gruning:
Well, I draw back as a consumer. Every day banks and credit unions are being [inaudible 00:15:28] by FinTechs. It's not a question of, it's nice to have, it's a got to have at this point to be able to survive, and to be able to prosper in this particular area. I always like to draw back, as a consumer, and say, well, that brand emotion... You think about that emotion to some of the brands that you select in your everyday life, whether it be Amazon Prime, which is timely now, or Target, or Netflix. Why do you choose them? Well, it's because there's an emotional connection. Why is there an emotional connection? It's the product, and service, and how they communicate to you. So you think about that for a second.

Greg Gruning:
And I love turning it around to say, these companies are doing it with a sliver of data. The data that Busey Bank and institutions larger and smaller than Busey, and their core, all those rich transactions is a treasure trove of opportunity to be able to be relevant, to be able to make that emotional connection.

Greg Gruning:
There is study after study that says customers of institutions want to have that connection. I want my bank or credit union to be relevant to where I'm living my life, whether it be generational, whether it be gen X, gen Y, Z, baby boomers, millennials, that message is loud and clear. Really, as Brian talked about, those KLIs that we produce is the great equalizer. I love even talking about a couple others, Brian, just to put you on the spot a little bit, but you just crossed the threshold of $10 billion, right?

Greg Gruning:
As you acquire, think about the acquiring banks. Operationally, and what you learn from the data, the acquiring banks, when you start determining branch locations, products and services that survive are great examples, or, I know one that we've uncovered recently, and one of my favorites is, we talked to institutions of all sizes and they're like, we're a retail bank.

Greg Gruning:
I guarantee you, I will find retail customers that are paying small businesses. So you better be looking at that because there's a risk, but I turn the risk around to an opportunity and being able to mold and maybe even branch out into new products and services that could be additional revenue streams for a particular bank.

Greg Gruning:
I go right back to, as a consumer, looking backwards and saying, why I'm a consumer of the brands. Then I turn around saying, you have to do this because if you don't, you will not survive, because other folks are doing it. Great news is, they're doing it with a sliver of data, and Busey Bank and others are just sitting on a ton of data. The problem is as we've talked about, it's hard to get at. It's hard to make sense. That's what we help institutions like Brian's to be wildly successful.

Jim Marous:
So, Greg, how do you take, let's say I'm a $10 billion bank in Ohio and my data is all over the place. It has transactional data someplace. It has savings data someplace, credit card, loan data, all in different silos. We haven't done a really great job at cleaning it. Are these services that you provide at Segment where you actually make it so the data is usable then?

Greg Gruning:
Yeah. I laugh. I've yet to see an institution's data that's perfect. It's not. It's almost, you have to get over the fact how bad it is. It's not good, and I understand why it's so difficult. Therein lies the challenge. The process of bringing on a Busey Bank, we walk through that with the core provider, to be able to extract that data, to be able to map the product and services of Busey to make sure that it matches what the Busey Bank has in marketplace, and that's a repeatable process. They don't have to worry about doing that. Brian doesn't have a department of 10 people. Efficiency is a big play here, is number one.

Greg Gruning:
The second thing is, at that point, we're doing it. We're taking all that data and we're cleansing and tagging, and then applying those categories, or those Key Lifestyle Indicators that gives Brian, on a daily basis, a true source of data across all of his customers on what they look like, and what those opportunities are. The big piece of it is also that resource that we provide with Brian. We've got marketing data specialists, it's not, here's the solution. You need to be a partner. You need to be able to understand not only the data, but understand what the Busey Bank is trying to accomplish.

Greg Gruning:
More importantly, instead of a hunch, you go out of business on hunches. Data doesn't lie. We can go in and be able to see what the opportunity is, is it big is it small? Also, provide some best practices as some other institutions and what we're seeing, and apply some of that best practices to our partners.

Jim Marous:
Brian, you mentioned that your acquisition strategy probably is as much an acquiring organizations than it is going out there and knocking on people's doors, bringing them into your financial institution. You spend a lot of time on the cross-sell, upsell, and building of relationships through opportunities. When you bring a new bank into the fold, what have you seen as the impact of your system that you have built at Busey Bank with regard to building relationship stronger? Do you have any measurement on how well these brand new institutions that have not used your system, have not been under your umbrella, have done once they come on board and what the impact's been from a share of wallet perspective?

Brian Lindenmann:
With bringing on another bank, in the past we've seen retention rates drop. Typically, we see about a 30% drop in customer retention. We use this data from Segment to help cross-sell and upsell some of our products to those customers to help retain them. Over time, we're starting to see that those numbers increase or improve. That's one of the powers that we have with Segment and the insights that they provide to us, is retention. That's one important thing.

Jim Marous:
Yeah. I would imagine also, you're trying to build integrated marketplaces. It also helps out, I imagine, using data from the acquired organization and integrate it to make sure there's overlap of customers that may have both used your organization, and the organization you acquired. This is also in that case, correct?

Brian Lindenmann:
That's correct.

Jim Marous:
Greg, recently I read that Segment has been cooking up some new ways to use artificial intelligent models, to cultivate opportunities that can deliver results quickly, and at scale. Can you share some of these examples? There's been some things around attrition...

Greg Gruning:
Yep.

Jim Marous:
... Some modeling, financial wellness, cross-selling.

Greg Gruning:
Yeah. It's a great question, and I know Brian could talk about attrition because I know they'd deploy that. I won't steal your thunder, Brian there, but I would take a step back. For us, the AI modeling platform for us is, the future's now. Taking all those rich transactions that we see, and we've analyzed over 80 billion transactions so we've been doing this for some time, being able to cleanse tag that, put those into KLIs. That's what allows us to develop at scale at speed efficiency, very efficiently, excuse me, these AI models. You mentioned attrition, I'll let Brian talk about those. We've got loan delinquency that's forthcoming, we've got financial wellness. We have AI predictive models that are out there. I will tell you, the models for us is a big competitive advantage.

Greg Gruning:
We've heard this from institutions, where they want something off the shelf that they can deploy that's back tested, that's accurate. That's exactly what we do in the wheelhouse. If you don't do the dirty work of the cleansing, tagging, and then categorizing that efficiently, there's no way you can deploy at scale the AI predictive models. Competitors can be three months, semi-annually deploy models, so it's absolutely the future. It's absolutely something we're continuing to build out.

Greg Gruning:
I'll let Brian talk about one of our models that's been very successful in the marketplace, and it came from our best ideas. I wish I could say there were Segment ideas, we're data scientists, we're data librarians, data scientists. The great brains are the Brians, and the institutions that we have on our platform that are saying, what if you could do this, I'm feeling this, this is a competitive risk.

Greg Gruning:
Listening to those really brought forth the model of attrition as a great example. So, Brian, I'll let you speak on how the Busey Bank is leveraging the attrition model, but that's the backdrop in the future, and why we leverage our technology to be able to deploy that at scale.

Brian Lindenmann:
Yeah. So we, with Segment, we actually were the very first people to help build out this artificial intelligence model for customer attrition. I'm very excited about it, so near and dear to my heart. The reason for that idea was really focusing on artificial model. That was the idea that I was pitching to them and they picked it up real fast, was the ability to use artificial intelligence to change on a dime, because you're looking at the customer's transactions, and the customer's behaviors change.

Brian Lindenmann:
We ran up against leveraging a single set of criteria to understand our customer's behavior, and transactions that led to them leaving the bank. We found that set of transactions was very hard to keep, so we were always missing the mark. We thought somebody was leaving, but our understanding was that criteria was always not right using artificial intelligence to be able to scale across all of our customers, and all of the transactions of our customers.

Brian Lindenmann:
We're able to build a better criteria that always changes, and always with that changing criteria, we can better hit the mark of understanding what's driving our customers to leave. Is the behavior really stemming from Busey's environments, Busey's behavior, or is it the market behavior? We're going through inflation right now, and there's going to be trends associated with the economic inflation that is causing customers to leave. We're able to spot those trends and be able to react faster and better with that insight.

Jim Marous:
You know, that's interesting you should bring that up, Brian, because there's a couple examples I've seen in the last two years. Number one, with stimulus checks, we have an institution that we're familiar, that said, we are all getting fat and happy around the fact that our deposit volume was way up. We were getting a lot of new customers depositing a lot more money and we got really excited about it. One institution that we know said, yeah, but what else happened with that money? They started looking at transfers, and they realized that organizations like Robin Hood and a couple others were getting a significant amount of transfers out of their bank to these organizations. Now, on an attrition basis it didn't look all that bad, because people weren't actually leaving, but they were moving their funds someplace else. It's that kind of AI modeling that can identify that.

Jim Marous:
In much the same way, when the government and where finances gave a break from mortgage payments, Navy Federal Credit Union said, what's interesting is there was a big difference between those that stopped their payments because they wanted to build up savings, and those that stopped their payments because they couldn't make the payments.

Jim Marous:
I think this is a great example that you just brought up about attrition, is that this is the same logic that says we have to avoid false positives, or false negatives, depends on which way you're looking at it. It identifies new models over time because, as you said, the marketplace is changing. People's reactions are changing, and the more you get in front of that, the more you're able to use data and AI to help consumers with their journey, without asking them what their journey is, the better off your loyalty's going to be, isn't it?

Greg Gruning:
Yeah, absolutely.

Brian Lindenmann:
Absolutely.

Greg Gruning:
Yes. To make those connections, it's an art form. It's not one transaction. Jim, I'm only smiling because you nailed just a couple of them. I'd add other data trends that you mentioned on attrition. It's those, and then some. The scoring of the KLIs. How about entering into the gig economy? How about subscriptions? I mean there are all ebbs and flows that you can see and where money movement is going, or not going, and the amounts, and the frequency, that are all indicators that gives Brian and the attrition model time to be able to see that well in advance, not with one foot out the door.

Greg Gruning:
Real easy to be able to say, well, I can look at some basic information and say this person's likely to attrite. It's a bit of a science and a methodology around being able to do that 4, 5, 6 months out, when you actually have a chance to be able to change that behavior, to retain that particular customer.

Jim Marous:
It's interesting too, because the dynamics of the marketplace change. We're not going to see complete closing of accounts attrition. We're seeing this distribution of relationships. Again, using AI and doing the things that Brian's doing at Busey Bank to be able to uncover those opportunities of people that are just plain changing their behaviors, because that also gives you an indication as to what's on their mind, what they need help with.

Jim Marous:
Brian, as we wrap up this podcast, what is on your to-do list as it relates to using data to deliver better experiences and a better share of wallet. What are you looking at the next 6-12 months that you'd really like to solve for?

Brian Lindenmann:
Busey Bank is a bank that's focused on commercial and wealth. That's the corner that we live on. We also cater to our retail customers. We have a much larger retail base, but with that mindset of commercial and wealth. Taking the data that we have on our retail customers and applying what products to upsell, cross-sell to those retail customers that are related to wealth. How do we better suit them for investment services? If that retail customer is starting their own small business, how do we leverage Segment data to identify those, and then move them to that commercial side, to that wealth side, and have those teams better suit our customer.

Jim Marous:
It's interesting Brian, because I look at my own situation. I have a business bank, and I have my consumer bank, and every month because the two banks don't talk very well together when it talks to transferring funds, I write a check twice a month off my business bank, and do a remote deposit capture to my personal bank. This has been going on now for 10 years. Not one time has either bank reached out to me and said, oh, by the way, how about if we try to consolidate these relationships and bring you better value? It's astounding to me, but what you just said is exactly what you're looking at, and all these internal transfers, especially now that people have so many different relationships, become so key to understanding the customer, but also most importantly in helping them. What you're doing at Busey Bank is a great example also.

Jim Marous:
I want to make sure my audience takes this away, that Brian doesn't work for the biggest organization on the block. He has a lot of bigger organizations that are competitors, but that doesn't mean he can't do things that even the bigger organizations haven't solved for yet. That's where coming in, no matter who you use as a partner, will become so important. To partner with an organization that can bring you to these opportunities, and can keep you going at scale, and the speed that's necessary for digital transformation.

Jim Marous:
Greg, as we wrap things up, we have often asked on this podcast, as it relates to digital transformation, what do you see that most often stands in the way of a financial institution making the decision to go forward? Is it a lack of pain, because organizations are making money as they have throughout the last decade and more? Is it a lack of perceived opportunity? Or, is it something else?

Greg Gruning:
I think it's a combination of everything. Inherently, in the financial vertical, there's some risk averse-ness to it. Being out and leading. To which, quickly the discussion is, look at some of those particular institutions that are leveraging this like Busey Bank. I always find there isn't a sense of urgency, there is, I'm going to get to that.

Greg Gruning:
There's recognition that yeah, I'm sitting on data, but part of the issue is, and depending upon who you talk to, there can be an ego there where I've got data scientists that can go in and they can cleanse, and tag it. Then we start talking about particular brands and, if it's a Wells Fargo where last week, we're up to 55,000 different pre-mapped iterations of transactions, and a bunch of them don't even have a W and F in it.

Greg Gruning:
Just being able to get, and cleanse, tag five or 10% of that, it's like building a house on sand; it's not going to work. It's that kind of recognition of, I can't do it. I can't deploy very expensive assets, which are data scientists or data librarians. I need to take a step out, and partner with somebody.

Greg Gruning:
That's where I commend a Busey Bank, to be able to say, listen, I recognize that there is an opportunity, and I recognize also I don't have the resources, the time, or the expertise to be able to do that. That's where we come in and talk about, again, we're not a marketing company, we're that data company; data that drives marketing. In the financial world, banking specifically, a lot of people go and say, ah, it's marketing. It's soft. It's not ROI based. That's not at all what we're talking about here.

Greg Gruning:
It's changing and shifting that mind shift to say, first, it's your data. You've got to understand that, and understand and recognize you need to be relevant. You need to have that emotional connection with your brand, your bank, because if you don't, somebody else is doing it. It's that sense of urgency. Then, it's the recognition of, I don't have the internal resources to do it. I can't buy it. I can't build it. It's too costly to maintain. I need a partner.

Jim Marous:
Well, it's interesting. I'm going to give a big plug to Brian here, too. One other major theme that we see on an ongoing basis is the lack of leadership that allows an organization to move out from beyond what other competitors are doing, and do more. Change is tough. Taking your data and partnering with an outside organization to make that data really work for you, is difficult.

Jim Marous:
I'm going to give Brian and his team at Busey Bank a lot of credit here, because it takes leadership that says we're willing to embrace change. It takes some risk, and deliver on a brand new way of doing business that wasn't the way we did business in the past. Even more credit to Brian for leading the charge on this, because again, he has to commit to having the trust and organization, and the willingness to let them go run down the field on his behalf.

Jim Marous:
Then continually, what that does was brought up by Brian in his conversation. It allows him time to think about the future, to be more future-ready and to create new ways to make the data work for him.

Jim Marous:
Thank you both for being in the show today, I really appreciate your time. As we take something away here, I think the most important thing is it doesn't matter the size of the organization. Any organization can take data and insights and make them really work for a grander share of wallet, and to be more future-ready. Thank you very much.

Greg Gruning:
Agree. Thank you guys.

Jim Marous:
Thanks for listening to Banking Transformed, the winner of three international awards for podcast excellence. If you enjoyed today's show, please take some time to give our show a five-star rating on your favorite podcast platform, and be sure to catch my recent articles on The Financial Brand, and check out the research you're doing on the Digital Bank Report.

Jim Marous:
This has been a production of Evergreen Podcast, a special thanking to our Producer Leah Haslage, Audio Engineer Sean Rule Hoffman, and Video Producer Will Pritts. I'm your host, Jim Marous. Until next time, remember the data your organization collects every day only has value if deployed for the benefit of the customer.