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The Agile Brand with Greg Kihlström®: Expert Mode Marketing Technology, AI, & CX
#688: Building a Customer Brain and how it benefits customers and the business with Christian Nelissen, National Australia Bank
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- [00:00]
Greg Kilstrom
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Christian Nellison
The Agile Brand.
- [01:15]
Greg Kilstrom
Welcome to Season seven of the Agile Brand where we discuss the trends and topics marketing leaders need to know. Stay curious, stay agile and join the top enterprise brands and Martech platforms as we explore marketing technology, AI, E commerce and whatever's next for the Omnichannel customer experience. Together we'll discover what it takes to create an agile brand built for today and tomorrow and built for customers, employees and continued business growth. I'm your host Greg Kilstrom, advising Fortune 1000 brands on martech, AI and marketing operations. The Agile Brand Podcast is brought to you by Tech Systems, an industry leader in full stack technology services, talent services and real world application. For more information, go to teksystems.com to make sure you always get the latest episodes, please hit subscribe on the app you listen to podcasts on and leave us a rating so others can find us as well. Now onto the show.
- [02:14]
Christian Nellison
Agility at enterprise scale means building systems that not only make millions of micro decisions but also keeps humans at the center, creating experiences that are timely, relevant and respectful of the customer. What if your data knew your customers better than your frontline employees and use that insight to serve, not sell. Today we're here at Pegaworld 2025 at the MGM grand in Las Vegas and we're going to talk about how National Australia bank has built what they call a customer Brain, a centralized intelligent decisioning layer that unifies engagement across service, sales and relationship building at scale. Tell me Explore this I'd like to welcome Christian Nellison, Chief data and Analytics Officer at nab. Christian, welcome to the show.
- [02:57]
Thanks very much. Thanks for having me.
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Yeah, looking forward to talking about this with you. Before we dive in though, why don't you give us a little background on your role at nab.
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So well, as you said, I'm the chief data and analytics officer and what that means in, in reality is I always say I do four things, so I have two bosses, which means that's because I need a lot of supervision. But for the bank's cio, I run all the tech for all our data estate and also I'm responsible for all the risk related activities around data. So I have the chief privacy officer, the head of the data risk management function, we have records retention risk. So I do all of that for the bank and then for the chief operating officer, I run all the analytics for the bank. So I have all the analysts that work in the bank and that includes the work we do account and that all the work we do on the decisioning pega decisioning engine which we call the brain. And then I also am responsible for the bank's gen AI agentic AI. I think we're going to have to rename it soon given where the industry is going. I run the. That run that program for the bank as well.
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So a few things then.
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Yeah, it keeps me busy and somewhat out of trouble.
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Nice. Nice. Well, yeah, so let's dive in here. And you know I mentioned National Australia Bank's customer brain in the intro. I was wondering, you know, can you talk a little bit about that, you know, break it down for us and what exactly is it and what's it designed to do?
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Yeah, so the brain is a pega. It's based on a pega decision engine. And the idea is that we take everything we know about our customer and feed it into the brain. And then the brain is connected to all of our channels that we see customers in. So whether it's inbound or outbound, human or digital, we're in every channel where we see customers. And so what it means is when we see something that we want to talk to a customer about, we can make that decision centrally. And then in every part of the bank where we see that customer, we can have that one conversation with them. So if we think you're interested in a home loan, we can send you a text message, send you a push notification, we can put that in a queue just for the teller to talk to you about when you go into a branch. We can make it appear when you open your mobile app. And we can also, when you log in online, we can also make it appear there. So once we see. And we can also push it out to an agent to call you. So if we think that you're interested in something and we see something in the data that tells us that's important to you, it all comes through the brain. And the idea behind the brain is every human just has one brain. The bank just has one brain that makes a decision about what's most important to talk to a customer about.
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Yeah, and so, I mean, that sounds, a lot of people say omnichannel, but that actually sounds omnichannel. Right. And just to give some scale here, so you're managing about 8 million customers, 16 channels, 300 actions, 2,000 adaptive models. So you know how from a complexity standpoint, how do you even begin to orchestrate something at that scale?
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Yeah, it's an interesting challenge. I think there's a couple of dimensions to it. Firstly, I think it's really important that you give people a way of understanding the framing for what it can do, because it's a very common, useful use case in the bank. You want to talk to customer about something, but the ability to have that capability means you really have to help the whole bank understand what the capability is. And I'm routinely in conversations with people that say, well, you could do that. Yeah, of course, once you, once you have all that, once you have the data plugged in, you have this decision layer and you're connected to the channels, what you want to do with the customers is relatively straightforward. The second thing then I think is, you know, how do you keep it simple? Is we deal with, we have a very good team and we deal with the complexity. So we try not to bring the complexity into the organization. So we've learned over. This isn't my first go at this and some of the people that work with me have worked with me for a while. We've learned over the years how to position it with customers, with our customers, without the people that we support in a way that's easy for them to consume. And really what we're talking to, we want to talk to them about what they're trying to achieve. So what are you trying to do with your business, where you want to take your business? And we then help them understand, we bring the thing to them and say, hey, look, if that's what you're trying to do, here's what we'd suggest would be a great set of, of things to do. And then we deal with a lot of the complexity in the background. But also, you know, I think one of the reasons, one of the things we, reasons we like PEGA is because it helps us simplify the complexity. Like it's, we've been at this for a long time. We've been great partners with them over a number of different institutions and we can see how that their tools have evolved that make it easy for us to manage some of that complexity.
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Yeah, yeah. And so another, you know, there's channel complexity and there's decision complexity. There's also competing priorities. Right. You know, there's, you know, a customer calls for a service thing, but there might be a sales opportunity, but it's not always the right decision to make. Try to make that sale when, you know, when a customer is calling about a service thing. And then there's, you know, other things like just customer engagement and things. How do you approach that trade off and you know, with more intelligent orchestration.
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Yeah, we start with the principle. And I've been at this for a while and so I think it's really important that you start with the idea that you have to talk to a customer about what's most important to a customer. And I used to have a. I don't do this as much anymore, but I used to, in the early days of doing this, I used to talk about taking banking back to the 70s. And the idea was in the 70s, your bank knew you as an individual and when you walked in the branch manager and trying to sell you a credit card, every time they saw you, they, they talked about your family and what's happening to you and oh, well, if your kids are going to college, maybe one of them needs an account. You're like, oh, you know, I've got lots of interesting stories off the back of that over the years of telling those stories. But, but in the 70s we knew you as an individual and, and so I used to use that conversation as a way of framing what we're trying to do both externally and internally with the organization. The fundamental principle is, should be about what's the most important thing to the customer, not what's most important thing to the bank. And so that's where you start that conversation. And so, you know, within the bank we have targets for how many, how many service interactions we have and how many engagement interactions we have. And then what's left over is the sales. So we're constantly making sure that we're trying to constrain how much stuff we do on sales I think that's one dimension. The other dimension is you start to get into an interesting place once you have the capability because you can go to a much smaller cohort and get much better response rates. So I've got a live example, we were just talking about it before we flew out where we went to 20,000 customers and we got a 15% response rate. And so you're talking about 3,000 customers. And if I had a, and that's a trigger targeted, very small set of activities in an old school way, I'd go to 100,000 customers to get to that same population. But now I have the capability to run ads and at scale, but also economically run much smaller, much more tightly focused campaigns. In fact, I'm trying to stop the organization from calling them campaigns, so let's call them actions. And I can run multiples of them side by side. And so in that coming, eventually coming back to the point, in that world, I can get the same volume of sales by going to a lot smaller groups, many smaller number of customers and use the rest of that space to have a different conversation with our customers. To have a service or an engagement conversation.
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Yeah, I mean, because the, I mean the economics of that, I mean one, you're having higher conversion rates with the audience. But also there's a, there's frustration like there's, there's loss there too. Right. If you get it wrong with the wrong, you know, if you just keep trying to sell everything to everybody 100%.
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So I, I used to say to people like, you know, we, we'd send out, when I first started doing this years ago at the Royal bank of Scotland, we, we'd send out 100,000 credit card mailers and we'd get. Cause we've got 3,000 responses to it. And I say you've got to remember 97,000 people opened that letter and was somewhere between somewhat and very disappointed in what we'd sent them. Right. So we managed to annoy 97% of the people. And the interesting thing for me is always, and we do it, we've done various bits of tracking on this in the past. When you go to a cohort with a more targeted, more where you've got a better reason for why you're having the conversation. People, even if they don't, even if they don't do whatever you're asking them to do, they still understand why you've done that. And so their response to that, it actually makes sense to them while you're having that conversation. Whereas if I'm just getting a credit card mailer every second month because that's what our targeting rules are like. You're wasting my time and you're not listening to me. You don't understand me. And so I think the whole we're in this sort of fundamental shift from campaign driven to really and people have been talking about this forever but really truly always on and very focused with what's around the customer.
- [11:55]
Greg Kilstrom
Want to learn more and join the discussion About Marketing and AI? Attend a premier conference dedicated to marketing and AI. That's Meacon, the Marketing Artificial Intelligence Conference. From October 14 through 16 in Cleveland, Ohio. Meicon brings together the brightest minds and leading voices in AI. Don't miss this opportunity to connect with a dynamic community of experts, visionaries and enthusiasts. The Agile brand is proud to be the lead media sponsor of this important event. Register today@marketingai institute.com that's marketing AI institute.com and use the code Agile150 for $150 off your registration fee. I can't wait to see you there.
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Christian Nellison
And so part of the understanding the customer and I guess with the best action. Right. I was going to say campaigns but.
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I'm trying to use your we want to be fridge. So it's important that you.
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Greg Kilstrom
Right.
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Christian Nellison
So for a human to understand what the, what the best action or potential actions are, they need insights. Right. So how do you look at, you know, everybody's talking about bringing customer insights to the forefront and certainly there are many ways to do that, but it's still hard to do that at scale. So how do you look at operationalizing insights so that your teams and the other teams can bring those opportunities and those actions to the customers?
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We always start with triggers because actually customers do and share with us a whole bunch of information that you could either push into a propensity model and have it sort of blended away or you can choose to respond to the actual trigger and the thing that, you know, the thing that the customers doing that is, is relevant. And so, and this, it turns out that when you really put your mind to it, there's all these really interesting things that you can respond to and that are valuable to the customer. And so in Australia, one of the things that you see customers do when they're ready to, when they're starting to think about borrowing money is they'll, they'll often download all their credit, all their statements because they're, whoever they're dealing with has asked for all their statements and that's a signal to us that they're a looking for credit and they're probably not talking to us about it. That's a really good trigger. If I see we can see customers online using our mortgage calculator, that's a really good signal. Now it's not a perfect signal and if you're, you know, if you're under 18, you're probably not the sort of person want to talk to. So we do some filtering and stuff like that and you know, but, but it's a really good trigger for what we want to talk to you about. Those sorts of, those sort of triggers. Actually there's a lot more of them when you start to really think about what happens in the data that you can start to respond to. And our primary focus is always let's figure out, I always say let's hoover up all the triggers we can and then we'll fill the rest with propensity models. And you keep, once you start on the trigger journey, once you really start getting people thinking, okay, if I see that I should do that, there's a lot more triggers than you ever think you're gonna have, than you ever you can imagine to start with. And it's a great place to start. And then the rest of it sorts itself well.
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And those are, those are leading indicators too as well as opposed to, you know, trying to figure out things after the fact, you know, so there's all kinds of win wins there and it.
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Extends, if you'll indulge me, you know, it extends to all sorts of other. This trigger based idea extends to all sorts of other things. So at rbs we found that at half a million times a year, customers would do everything they needed to do to take cash out of an atm, except they'd forget to take the cash. Oh. And I've told this story quite a few times and I would say the audience neatly divides into two completely separate groups. People can't believe that anyone could ever do that. And pit for people who've done it. Yep, yep.
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I'm not going to say maybe I'm in the latter group, but yeah, okay.
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But the cool idea is with this sort of setup and with what you have in this sort of environment is that's a trigger, right? Like if we see that data you've. Because what happens is the machine eventually swallows the cash back up and it deposits back into your account. Or the joke is always because we're in a UK bank, maybe not so much in London that someone would come along and take the money, but everywhere else the money would be. And so the ability to take that piece of data and then, you know, in an ideal world, as you're walking away from the atm, you're getting a text from the, and you look at your phone and it's your bank and your bank says, hey, you've got to take your cash, but don't worry, it's back in your account. You don't need to call us, but here's the number that you need. It's just one of those moments and it's a trigger and it's super relevant. And the customer, and the customers immediately understand why you've, why you've done it. And even if they're not worried about it or they've already forgotten about it, it's just a really good service moment. So those sorts of things, I think the ability to take those signals and do something with it is so much more useful than, you know, at some point you start to have to think about propensity models and those sorts of things. But that's way down the track.
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Yeah, well, and with so many, I mean, we talked about several different scenarios here just now. I'm sure there's countless others. You know, with so many data points, you know, propensity models, adaptive models, all of these things at play, how do you think about transparency in terms of, you know, internally understanding what's going on so you can do, do a better job and optimize. But also, you know, the trust factor with your, with your customers. You know, how do you, how do you ensure that.
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So we have a rule which I think I stole from somebody who now no longer uses it, which is don't be creepy. Right? So, you know, we want it, we want it. Because if you. Again, the, the interesting thing about a mantra like taking bank to the 70s as a baseline is you wouldn't do things that were creepy if you were a branch manager or a staff member in the 70s. So that's a test, right? Like, does this feel normal, natural to us? Can we explain why, you know, we're not, we're not chasing you around the net with stuff, but we're being, we're relevant to you. And when we understand stuff, it's obvious that we understand stuff and we're trying to be connected to you. I say to the guys that work for me, the ability to use our customers data to help them based is based entirely on the trust that they have that they, they trust us with the data in a way that they, in most, they don't necessarily trust Facebook or any of those other things. And we would never want to do anything to breach that trust. That's much more valuable than any given sale or any given opportunity. We're somewhat unique, and I've done this job in banks, this sort of working in my way at this level four times in different banks. The one thing I'm really super proud of where we are now is we have a data ethics policy. And that isn't just a policy that says blah, blah, blah, it's actually operationalized. So before, you know, before you do some, there's a set of decision criteria when you want to do something new, there's a data ethics assessment. And the thing has to pass the ethics assessment. And we have stopped a few things, including some things that have really adored my CEO, which I won't talk about, but we did them for the right reasons, and they just weren't the right thing to do for the customer. And I mean, to be fair, he understood it. In the end, I'm obliged to say he's a good guy. And for my own career sake, though, he is genuinely a good guy. And when you explain to him why we did it, he wasn't. It just took him a little while to come to the same conclusion as we did. But that ethics assessment is real and it's embedded in the way the organization work and it makes decisions. It actually stops things from happening.
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Yeah. And even beyond the ethics thing, obviously very important on a number of. I mean, from a customer trust perspective as well. You know, any regulated industry, there's. There's a lot of needs there. What about just the pure data volume? You know, how do you look at. You know, is more data always a good thing? You know, how do you distinguish between useful insights and maybe just noise?
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There's a couple of things I think at play there, like in the background. One of the other things we're doing is building out a data lakehouse. We've got a couple of data estates and we've turned off a couple of legacy environments, so we're progressively moving to a single estate. So the first question is we're trying to get to a place where the data's available, and we call that getting data, like electricity. So if you need data, you can get data. But to your specific point about how do you pick things out, there's an interesting. Again, because the dynamics have changed so much in the way that we think about these things, you can now have things that run at a much lower unit cost. It doesn't take a lot of overhead for us to keep running them. And so the cost of experimentation, the cost of failure and the cost, some of that conversation has changed significantly in that sense of. So the example I would use is we have this thing that runs continuously and it's about encouraging people to tell us when they're traveling overseas so that we don't block their credit cards when they're overseas. Right. So relatively useful service. And it doesn't do very well when there's not school holidays and it does super well when school holidays are coming up because it turns out a lot of people travel during school holidays. Right. So. And, and I would say if we'd been really overly rigorous about it under the older regime, we would have said, look, you know, we, look, we would have, if we had put it in the wrong time, we would have said it's not doing very well, we'll kill it, we don't want to, whatever, but we just let it run. And it turns out it just, it does a really good job for peak cycles, you know, three or four times. I don't have any kids, but I'm guessing they still have school holidays. I vaguely remember every school holidays when I was a kid, but you know, four times a year. It does, it does really well. And well enough for us to leave it in because it's helpful to our customers. And so I'm never too worried about, to your question. I'm not that worried about managing the proliferation because the proliferation will somewhat sort itself out. I'm not, it's not an unreasonable, I don't have to pre filter a lot of the ideas at the moment. We're in a place where every time you add something to the system, it does better. We'll get to a point where it starts to, you need to start thinning down. But the cost of failure is dramatically different than where it was before. I haven't done the math, but, you know, it's an order of magnitude cheaper to put something in the system now than it used to be.
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Yeah. Yeah, makes sense. Well, Christian, thanks so much for joining. Two questions for you here. First, we're here at pegaworld. You know, we're, you know, we're about a day in, you know, what's either been, you know, something you've enjoyed most so far or something you're looking forward to.
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Look, the interesting thing for me, because I run the bank's Genai program is actually seeing how much how Genai is transforming not just my direct interest space, but it's really making me continue to think about where we're going with genai or agentic AI now that is coming in. So every conversation, I mean, I'm making more and more connections with what I need to do when I go back. And I think that's been super interesting. I'm in a sort of fortunate position of PEG has asked me to meet with a few customers and stuff like that. And I just love, I love those conversations and playing backwards and forwards with where they're at and what they've learned and sharing some of the stuff that we've, we've gained. Love it.
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Love it. Well, last question for you. I like to ask everybody on the show what do you do to stay agile in your role and how do you find a way to do it consistently?
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I, I'm, I say to my team, I'm a, I'm someone who's got a person with very fixed convictions that's open to learning. So I think that it's important once you decide to do something to really, to get it done. And I think there's, you know, in a large organization that makes a huge difference is if you can execute. But I read a lot. I read an enormous amount. The lucky thing is I don't have any kids, so I have an enormous amount of spare time, as it turns out. But I read a lot and I'm constantly making connections in terms of thinking about how this changes with that. And so and because I'm lucky enough in my job to be able to take that back and say, okay, we were doing this, we need to build on that and do that now and keep moving. Yeah, love it.
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Greg Kilstrom
Well, again I'd like to thank Christian.
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Christian Nellison
Nelson, Chief Data and Analytics Officer at National Australia bank, for joining the show. You can learn more about Christian and NAB by following the links in the show notes.
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Greg Kilstrom
Thanks again for listening to the Agile brand brought to you by Tech Systems. If you enjoyed the show, please take a minute to subscribe and leave us a rating so that others can find the show as well. You can access more episodes of the show@theagilebrand.com that's theagile brand.com and contact me if you're interested in consulting or advisory services or are looking for a speaker for your next event. Go to www.gregkilstrom.com that's G R E G K I H L S T r o m.com the Agile brand is produced by MissingLink, a Latina owned, strategy driven, creatively fueled production co op. From ideation to creation, they craft human connections through intelligent, engaging and informative content. Until next time stay curious and stay agile.
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Christian Nellison
The Agile Brand.
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Greg Kilstrom
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