Rich Carlton:
Let's bring all of our data together. We want to bring together everything from our core and our wealth management and our online banking and all this, and we're going to spend a year or two building out that. And to me, that's almost like saying, "Hey, we're going to have Thanksgiving dinner, so what I'm going to do is I'm going to go buy everything that exists at the grocery store."
James Robert Lay:
Greetings and hello. I am James Robert Lay, and welcome to episode 282 of the Banking On Digital Growth Podcast. Today's episode is part of the Exponential Insight series and I'm excited to welcome Rich Carlton to the show. Rich is the president of Aunalytics, a data platform company that delivers managed IT analytic services empowering financial brands and their leaders to get answers and insights to maximize their future growth potential, which is exactly what we're going to be discussing today to guide you, dear listeners, so that you can level up your future growth at your bank, at your credit union, or at your fintech. Welcome to the show, Rich. It is good to share time with you today, buddy.
Rich Carlton:
You as well. Thank you for having me.
James Robert Lay:
Before we get into demystifying data and analytics for financial brands so that they can in fact maximize their future growth potential in the age of AI, what is good in your world right now, personally or professionally? It is always your pick to get started on a positive note.
Rich Carlton:
Personally, I always feel like I'm more blessed than I deserve, so that area, no complaints there. Professionally, we're excited about momentum I feel like we have. In the data space in particular, we've been in it for about 10 years, very early, still feel like we're very, very early in that game. But what we're seeing is a couple things. The market is evolving. More and more financial institutions, we're gaining momentum with this year as they are catching up to the opportunities that data and technology can bring to their offerings. And at the same time, what we have done is made sure that we've adapted some of our offerings to make sure that we're solving not just the technology side of it, but the people side of it that is necessary to help them capitalize on their data.
James Robert Lay:
I appreciate that perspective framed around people. And if we look back say over the past couple of years, what would you share as a perspective, the greatest lessons that you've learned along the way about how financial brand leaders are thinking about this whole world of data and analytics?
Rich Carlton:
Couple things that we've learned, one, just as we've been involved in the last few years, one of the biggest positive transformations I've seen is that for quite a long time, both our customers and us thought of it as a technological opportunity. Oh, we're talking about data. That means it's our IT departments. Oh, Aunalytics, let's develop some technology to give and solve this problem. The convergence of the two things is number one, what we realized was to actually capitalize on your data, to build a data pipeline every day in these early stages, particularly if you're a midsize institution or even a large institution. We have some to those, but you happen to be in secondary markets. Data scientists, data engineers, platform people, they're not just laying around.
And at the same time, a bank's IT department isn't really looking for things to do. They're pretty busy. They're building data warehouses. They're doing things that help capitalize on the data, but some of the advanced architecture and other things. And so how do you best partner with the bank technology group to be able to solve problems and deliver and make that more valuable? But at the same time, what the realization has been is that while this is a data opportunity, it is a business problem challenge. And the more involvement that we have seen over the time here with marketing leaders, with CLOs, with COOs, in really setting strategy for their organizations of how we want to utilize data within our financial institution, and not just say, "Here, IT, go figure it out," have been one of the biggest game changers.
James Robert Lay:
I appreciate that perspective that it's not just a technology opportunity, but it is a business opportunity. It is a growth opportunity. And I want to pause just for a bit to maybe demystify or distinguish the differences between these two words that always go together it sounds like when I hear them, data and analytics. It's almost like peanut butter and jelly. You can't say peanut butter without saying jelly. So what are the differences between data and analytics? And I would say even more deeply, why is it important to think through the differences here?
Rich Carlton:
Well, every bank has data. They have tons of it. They have tons of historical data. They have tons of information. And to be honest with you, we see on average about three and a half transactions per customer per day from some of our customers across when we look at it, whether it's an online bill pay, or a debit card swipe, or something. You start to do the math on that of-
James Robert Lay:
That's over 1000 a year per individual.
Rich Carlton:
Per individual. You've got 50,000 or 100,000, you start to do the math on that, you're starting to see. And within that data, there's all kinds of analysis and information that could be done.
James Robert Lay:
Yes.
Rich Carlton:
Where people sometimes make the mistake on that is they start to say, "Hey, we want to get into the data business, so the first thing we need to do is we need to build out a big data warehouse, so we're going to build this." Or let's bring all of our data together, we want to bring together everything from our core and our wealth management and our online banking and all of this, and we're going to spend a year or two building out that. And to me, that's almost like saying, "Hey, we're going to have Thanksgiving dinner, so what I'm going to do is I'm going to go buy everything that exists at the grocery store." You don't do that. You say, "What's the meal I want to cook and why? Okay. And then what are the things that I need?”
And so when we talk about analytics or analysis compared to data, it starts with: What's the business problem I'm trying to solve? Why am I trying to solve it? Then it becomes: What are the data points that I need to address that? Where do they exist? Let me be hyper-focused on that. But that's not the end of it. That's the beginning of it. It is now that I've got data together and I've done analysis, we talk about getting it to the edge. It's only meaningful if somebody takes some action on it. Can I get it to the edge to where in time for somebody to make a decision for it to impact?
And so sometimes that is, I get it to the edge in a dashboard, or at an insight, or however, that's all analysis. But once I take that analysis, how do I measure it to come all the way back? How do I know if it's successful? That's when you start to say, "Great. Now I want to double down, triple down, scale this, take it across my organization." But it's being ... Data's huge. Analysis to me is: What's the business problem? What data do I need to bring to it? And how do I measure success?
James Robert Lay:
I like that idea of almost starting with the end in mind, tying it back to business strategy or growth strategy. And there's a framework that I'm writing about in Banking On Change that I introduced slightly in Banking On Digital Growth right at the very end, but we're going to expand upon that further in Banking On Change. And the question is probably one of the most important questions that it can be used for any area of the bank, or the credit union, or the fintech. And that question is: How do you want to grow? And you could use this too when it comes to your data strategy? What are your goals? What are the roadblocks that stand in the way of the goals? And then what are the opportunities to use data, analytics, analysis, insight, to provide a path forward to overcome those roadblocks and to make progress towards those goals for growth?
I want to get your take on this. When it comes to data, analytics, analysis, a common belief that financial brand leaders might have, that is limiting their future growth potential, something that's holding them back that you would disagree with them on, perhaps even provide them with an alternative perspective to open up a whole new horizon to guide them forward beyond their roadblocks that are holding them back just within their own mind.
Rich Carlton:
Sometimes I think the perception is data is cold, data is impersonal, data is ... That's not how we've grown. And you know what, the answer is you're correct. You're correct in your financial institutions. Part of it is when you start to say, with the end in mind, you start with: What's my why? Why would I even invest in this? And you do such a great job in the Banking On Digital Growth of more or less taking the mindset of, you have a competitive mode that you've had in your region or your markets that you've served forever. Many times, if you ask customers, if we ask our banking customers or our prospect beforehand, "Hey, why are customers, customers at your bank?" Overwhelmingly what you will get when you talk to them, oh, service, we take care of them. It's the way we treat our customers. It's our culture. It's our [inaudible 00:09:06], all those things.
Rarely does somebody say, "Our core technology is better than anybody else's." They don't say that. And yet, we know you make this case, the consumers are demanding a digital experience. They're going to demand a digital experience. So step one is: How do I create that personal experience that I've had and extend it to my digital experiences? Part of what we talk about with the data is then saying, "Hey, you are giving a great digital experience on your website, and in your online banking, and in this," all disparate data points that when you start to connect them together, tell a story of what your customers are actually looking for. So your ability to personalize then, take your data and harness it, and know who your most likely customers are for product X, Y, or Z, to know every day who your customers that you already have, that literally minding the transaction data, here's your customers and how much money they're sending out of your bank to your competitors every day so you could act on it.
Those types of things are personal experiences that the banks have won at forever in their market. This is just enabling it to do it faster, more effectively, more efficiently in a digital world. It's very personalized if you do it right.
James Robert Lay:
So there's this whole human element, and I think that's one of the things that I'm always encouraging financial brand leaders to think about, is behind every piece of data that you have, which is essentially a one or a zero, is DNA.
Rich Carlton:
Oh, absolutely.
James Robert Lay:
It's flesh and blood. It's human being. It's pain. It's problem. It's also hope. It's opportunities. It's dreams. And I think if we come back to that central truth that behind ones and zeros, behind data is DNA, it's a different way about thinking of the potential opportunities to guide the people in our communities beyond their questions and concerns towards a bigger, better, brighter future. And I think that's where a potential roadblock does come into play here because, back to your point, about three and a half transactions per day, you multiply that over the course of a year, that's over 1000 potential data points just in transaction data alone, per account holder. And then you exponentially multiply that. So there's an abundance of opportunity for financial brands to establish and expand a practice of data analytics that turns into insights, and most importantly, actions and recommendations that they can make for account holders. But we know that when there's an abundance of opportunity, an abundance of choice, there is the paradox of choice, almost no choice is made. So let's distill this down into practicality here, thinking about all of this opportunity. What are some very practical paths that the dear listener could take to begin to move forward and make progress once again? Instead of going and buying all the ingredients at the grocery store for Thanksgiving, what are the few ingredients that we should start with in the first place?
Rich Carlton:
There's a multitude, oh, my gosh. Do we want to go into risk? Do we want to do this? Do we want to ... All those areas that you can look at with data and analytics, this is our path of recommendation. It may not be for everybody. We start with the customer because here's what we know is that every financial institution wants to grow the customers they have and attract more in the most efficient, effective ways possible. That's kind of the linchpin for everything. And we don't start with commercial customers. We start with retail customers. And so what we try and do is number one, and everybody, sometimes the people that do get excited about it want to jump right to the far end predictive. And we want to get there very much as well. But what you really start with is, we build a customer intelligence data model that says, "How can I take the most relevant things out of all that transaction data, out of core, out of wealth management, that can start to paint a picture of who are our most profitable customers?" Who are those that are close that maybe should be, that we could be taking better care of? Who are those customers that are leaving us? And what are the signs that start to come around from those? From there, you're getting into some just good, healthy business intelligence about my customer base. And there's a lot of health that you can get right there just from looking at branch performance. Where are home branches, family? Taking things down to not just an account level, but to a household level. So which households are most profitable? Which parts of my regions, which branches, transaction branch versus actual sign up branch? We see that in the data a lot of times, where people particularly post pandemic, have everything assigned to where somebody opened up their accounts.
Well, we actually map it to, they might've opened there, they haven't done one transaction since they've been there. And the assumption is this branch is really way more profitable than the other. So a lot of that is just organizing and structuring data when you start to look at the customer side. It's then that you build off of, and you can start to do some of these predictive models. They get really exciting around what is the next best product for a certain group of customers. So now once I've narrowed to be random, so to speak, here's a group, this is where it gets fun, like where you were talking about, it's the human action side. So now that I've got a group of customers that say, "Hey, these folks are most likely to need a HELOC, the data is saying this," now it's up to us for choices and that marketing group. Who do I send this piece to? What kind of offers do I do? How do I track it? But now you get the data flow coming back in that starts to be able to inform and say, "Hey, campaign A to males worked way better than campaign B did, vice versa to female." So you get really personalized in that when you get through. I love to think big, start small.
James Robert Lay:
Exactly. And I think about a conversation that I had with a chief marketing officer at about a $10 billion organization. It was on a panel discussion I was facilitating. And through this practice that they had established very similar to one that you're talking about right here, they identified that retention for accounts was directly correlated with a HELOC. And so what they did is they went in, they increased the number of HELOCs for those that did not have one with checking accounts. And as a result, increased the average lifetime value for account holders exponentially going forward. And I think what that did is it gave leadership a level of confidence that they never had before to then go out and then acquire new checking accounts because the big concern for them was they were going out and acquiring checking accounts, but then there was this whole attrition thing that was going on behind the scenes that they didn't really have clarity around. But it was through this practice that they were able to gain clarity, and then as a result, increase their confidence going forward into the future. Now on the flip side of this equation, dangers, roadblocks, when it comes to establishing and expanding a practice around data, analytics, and insights that lead to actions. What are the things that the dear listener needs to be thinking about that could be a potential impediment to growth?
Rich Carlton:
Two things that I'll say, one goes back to where I started with the technology, the thought that to start this, I need to build all my technology. You're going to spend a year, year and a half and invest a ton of money to try and get all the data technology right. And you haven't given your user, your business person, any taste of where they're going. It's almost like you're telling them you're going to take them on this great vacation, and you're spending all the time building the plane instead of taking them on a little trip, so to speak. Part of that is the starting narrow with a use case. What is something we're trying to solve that I can work on in a more narrow, nimble, get a taste of success through data? The other part to that, and it builds off of the action side of it, and one of the pitfalls is you can run into things with this is the way we've always done it. This is the way we always think about it. This is how we've tried this. And so once you get the data together, and you're going to say, "Hey, we're going to work on this opportunity," you've got to have the right mindset of people that are willing to think a little bit differently. And that's where I'm saying a smaller group as well. It may be okay, the data set, here's some customers that are likely for this. You're going to do an experiment. You can do an AB test. It may show that a couple of the things didn't work, or it may show that, hey, the way we've been doing it for years, this is maybe a better way. And you've got to be okay that says, "Hey, that's not a bad thing that we've been doing it this way for 10 years." It's, "Oh, my gosh. We learned something new and now we can do it better." Like you said, the insight that institution got that a HELOC was tied to checking, you could take a look at that and say, "Oh, my gosh. For 20 years, we haven't been marketing for HELOC, what a failure, so I don't want to talk about this." As opposed to, wow, what a great new insight. How do we act on this? So it's the right mindset of action too.
James Robert Lay:
One of the things you're tapping into is the dangers of retreating back into the cave of complacency. The cave of complacency has a siren's call, luring leadership in. It's safe, it's secure in here. The world is chaotic. Why don't you come in and seek solace in the cave of complacency? But in reality, it's a very dangerous place to be because the world is continuing to transform at an exponential rate. I want to pivot the conversation just a bit and maybe even get personal to gain your personal perspective. How do you as a leader continuously challenge your preconceived notions so that you're not getting trapped in the cave of complacency or falling victim to, well, that's the way that we've always done it? What have you done on this front here?
Rich Carlton:
Some of it is, you try and let the data do some of the talking. You can come into it with a theory, absolutely should come into it with a theory.
James Robert Lay:
Hypothesis.
Rich Carlton:
Yeah. I believe there's more of our customers that we are attracting when we market to the COO as opposed to the CIO. We spent lots of times talking over the last year of: Who can say yes and who can say no? Data, when we're having these conversations with institutions, lots of people are involved in it. Lots of those people can say, "No, I don't think we should do this." There's only certain ones that can say, "Yes, I think we can." So you have a hypothesis going into it, but now let's check that hypothesis with some data. Let's see how it actually holds up. And in the same way that culture of complacency, there was an institution a few years ago that we did a project for, and there was a particular mindset of the CEO on who their target was, their risk, and they'd done it this way forever. And we challenged, and their team behind the scenes, challenged and said, "I really think we could do X, Y, and Z." The analysis showed X, Y, and Z was absolutely true. And it was interesting, the CEO's own team laughed at us and said, "Hey, could you guys present this because this goes against their theory, and I'm not sure how it's going to be received." Now to that CEO's credit, they loved it and said, "Wow, now we've got a new way of ... " But I made a decision at that point and time based on the data that I had. And so that willingness, that ability to say, "Hey, I'm going to have a hypothesis," and we should have hypothesis because I've got industry experience. There's things that I've done. There's ways that we've done it. But I need to be open to the fact that there might be some more data points that I couldn't have before. And let's take a look at it and see what they say.
James Robert Lay:
Yes. And that comes back to the point of transforming where one might be wrong, or what could be perceived as failure. That becomes the classroom for continued future growth. And I want to talk about talent here for a bit when it comes to maximizing future growth through establishing and expanding a practice of data and analytics because when we look at our primary research, when we look at our secondary research, there is a talent gap in this area. I'd even say more deeply though, it is a knowledge gap. It is a mindset gap that's limiting the future growth potential for many financial brands that we've already been touching on this. What are the opportunities to overcome the talent gap? Because yes, back to your point, we can have all of the technology, but if we don't have the talent, the skillset, the mindset to capitalize, to take action from the insights that are coming from the technology, then we have a Rolls Royce or a Ferrari that's just kind of sitting in the garage.
Rich Carlton:
The awakening for us, like I mentioned, we spent a lot of time developing our technology platform and thought, "Hey, we're going to sell this platform, subscription as a service," focus on mid-sized businesses, and they'll take it and away they go. [inaudible 00:22:29], candidly, to learn we could give it away for free, and at least the way the talent gap is right now, if you don't have data scientists and engineers, it's a struggle to capitalize on that. There's a reason that many midsize institutions have their own IT department and don't work with AWS because they're just going to give you technology and hope you figure it out. So for us, and what I would recommend on that, our business shift was saying, "We need to deliver more [inaudible 00:23:00] service, as a service up through, hey, we're probably trying to solve, let us get you the data, let us deliver the data set every day, let us organize it. Where our customers, at least in our model, how are doing best is they've either with us or on their own, sometimes figured out a way to, I'm going to let that group get me the data. Where I need to have my investment is, okay, I've got these insights. How am I going to act on it? Who are my analysis that can say, "Okay, I get it that my majority of my customers are doing X, Y, and Z"? Okay, how now can I analyze that to say, "What does that mean for what markets should I expand into? How should we change our messaging on our website? Who of our customers should we market to? What's the message to them?” So investing more in analysis seems to be an area that the financial institutions can find that talent and grow, and finding somebody to partner with on more of the advanced analytics because it's not just the data science or the algorithms. When you're talking about the volume of transaction level data, you're not going to put that onto a traditional data warehouse in SQL and have it work. So there's other things that us or companies like us design and do so that you can handle the millions and billions of rows of transactions to be able to pull insights out of daily.
James Robert Lay:
And I think that is another key perspective as I think about the banking on change methodology going forward. Growth will come through collaboration. I want to come back to my how do you want to grow model that I introduced earlier, goals, roadblocks, and opportunities. Why I left off the W with that, to really frame all of this, and the W is putting the who before the how to capitalize on these opportunities, to overcome these roadblocks, and move towards these stated goals for future growth. And the who could be an internal team member. It could be an external collaboration. It could be even an AI model at this point, to where AI, I'm trying to get leadership to start thinking about AI being a collaborative teammate with all of this. So when you look at the collaboration lens, what are you seeing transforming on this front? Because this I think is one of the most exciting advancements within financial services that historically was pretty closed off. I'm even exploring the possibility of industry-wide cohorts to connect and learn together because there is this idea of knowledge share and transfer because we're all seeing and experiencing things differently. I get that's a bit of a higher level theoretical model. But I want to come back to collaboration. What are you seeing on this front in regards to true value creation through collaboration?
Rich Carlton:
I think it's a decision for any institution, whether it's data or anything. What do I want to own? What do I want to rent? And I think the things that I want to own as far as within my talent base and my insight, are things that can give me a competitive advantage within my marketplace. So some of it is, I don't know that owning how the data's sourced, how it's pulled together, how the data model is build, if that's the creative advantage, as much as: Okay, now that I have this, how does it apply to my market? How does it make my institution better, faster, smarter, cheaper? How does it allow me to serve my customers better? And so making that distinction, and once somebody gets comfortable that you're partnering. And that's where I talked a little bit about our role and what we do is not at all to replace the IT department.
James Robert Lay:
No.
Rich Carlton:
In fact, it is to partner alongside of them and say, "Here's how we can get the data organized, structured, better, faster, and to you and your partners to where now you can take action on it and actually build into a multitude of things that drive value to the organization."
James Robert Lay:
That's a great point because if we think historically here, internally IT teams could look at this as a quote, unquote competitive threat. But I look at this as a collaboration opportunity, and through collaboration, you get capability upgrades. And through capability upgrades, you get exponential multipliers to move further, farther, faster with economical scale as well. And I think that right there is the communication opportunity to transform the mindset beyond looking at competition because competition looks at things through a lens of scarcity, where collaboration looks at things through a lens of just ever-growing abundance. And back to your example on just transaction data alone, that is an ever-growing abundance of opportunity.
Rich Carlton:
Absolutely. And it's not just an ever-growing abundance of opportunity. You've got to remember on this is that there's a whole cycle. You're going to get data. You're going to get insights. You're going to take action. But who you're going to take action on are human beings, and human beings are going to react to that and they're going to change how they react. That's where you get into the machine learning, the model. What you're doing today may not work tomorrow. We have a next best product algorithm that it is very different to a financial institution in a major market on the East Coast versus a midsize city in the Midwest because they're humans that are taking action. So you can't just say, "Hey, customers who have this product, this product, and this product, their next one is this one." I don't know. There's a lot of human factors that go into that, that the data helps tell that story.
James Robert Lay:
That right there, the human factor, as a digital anthropologist that have been practicing now for the past 20 plus years, I've studied the lens, really the intersection of marketing, sales, technology, and human behavior. But I'm growing more and more fascinated with the variability of the human behavior component, the human factors component, because it is so unpredictable. However, through the predictive patterns at a higher level crunch by machine learning, crunch by automation, it now provides financial brands an opportunity to move from a reactive relationship to a proactive relationship within the lives of people. Rich, this has been such a great discussion today. As we start to wrap up, I always like to send the dear listener off with something practical that they can apply based upon everything that we have discussed here because once again, an abundance of opportunity. But let's really simplify and distill this down to one small thing that they can commit to do today to at least feel like they're moving forward and making progress along their own journey of growth. What would that one small thing be, your recommendation?
Rich Carlton:
My one small thing would be a couple little sayings that we talk about here. Some of it is, we start to say, because people talk about, I want data to do this, this, this, this, and this. Always ask the question. If I knew this, then I would do that. So start with that. So I'm wanting to [inaudible 00:29:50]. What's your business problem I'm trying to solve? Oh, man, we need to grow deposits. Okay. If I said to you, "If you do this, first off, what would that?" Oh, I wish I knew this. Okay. Great. If you knew that, what would you do? Oh, I'd take this action. That begins to set the framework for, oh, here's a data use case. Can we get this data, this information, to be able to help you [inaudible 00:30:11] and know that? Can I take this action? And then I measure it to see: Is it actually driving results? Am I being random? Am I creating better situations? So that, along with it's great to think big, start small. Start narrow. Start small. Build something. Build wins. Then you expand your data program.
James Robert Lay:
And that comes back to how we started this conversation because when you get those wins, you get wind in the sail, and that creates momentum to continue to push the ship further, farther, faster, forward, so I really like that as a way to wrap up this conversation, Rich. What is the best way for someone to connect with you to continue the discussion we started here today?
Rich Carlton:
Absolutely. I appreciate that opportunity. Aunalytics, our website, is A-U-N-A-L-Y-T-I-C-S.com.
James Robert Lay:
Connect with Rich. Learn with Rich. Grow with Rich. Rich, thank you so much for joining me for another episode of Banking On Digital Growth. This has been a lot of fun today.
Rich Carlton:
I appreciate it. Thank you.
James Robert Lay:
As always, and until next time, be well, do good, and make your bed.
