Our guest this episode is Rob Windle, CEO and founder of Dynamic Customer Solutions The core of his business revolves around implementation and supporting businesses to lower the cost of call centre operations while improving the quality of outcomes &, supporting learning and improvements through data-driven metrics using state of the art feedback analytics software to fast track improvements.
Become Data Driven and Reimagine The Customer Experiences With Nick Young
The pandemic has acted as a powerful catalyst for market dynamics already in place. Most notably the switch from in-person to digital banking, channels, Apps collect vast amounts of unstructured customer feedback in the form of the comments that accompany app ratings. These comments used to be mainly complaints about table stakes technical stuff like security usability and reliability, but these days there are as likely to be about complex banking functions or customer journeys, such as managing payments or locking and unlocking card features. If data is fast becoming the most reliable single source of customer feedback for the whole bank brand. And because of its universal availability across all brands, it's unique in the power of its market benchmarking capabilities.
In this episode, we talk to the latest touchpoint team member. Nick Young, Nick has more than 20 years experience in customer analytics, pricing strategy and revenue optimization for financial services around the globe. He is an expert at leveraging deep learning techniques and predictive analytics to transform business processes. And re-imagine customers' experiences. During his career, Nick has pioneered the use of predictive analytics and big data for customer behavioral, forecasting and optimisation. He's also led companies in the U.K. and in North America.
01:58 Glenn: Welcome Mr. Nick Young, the irrepressible Mr. Nick Young. How are you today? My friend?
02:04 Nick: Extremely well, this sunny fine morning in deepest, middle England. How are you, Glenn? I see it's pretty dark there.
02:15 Glenn: Pretty, pretty damn marvelous. This is deepest, darkest. Middle of England. It's actually, it looks, it looks like a stunning day out there, but anyway, welcome to Touchpoints. Welcome to Touchpoint the company. We are so excited to have you on board with the wealth of knowledge that you bring to us. And let's kick off by. How I always start with these, give us the two to five minute life story of how you ended up where you are today.
02:43 Nick: So I suppose I've spent about, you know, it seems like an awfully long time now about the past 20 years doing, customer data analytics and the critical thing I've been interested in, is how to operationalize the insights to create business value and what I've learned oh well I've made a lot of mistakes, but, I've learned a lot. So what I've learned is that the best way to operationalize insights from data analytics, customer data analytics, is to somehow bring the customer to life for the people I'm closest to the customer, but half to make decisions based on the content of the insights.
And what that means is that the can't really be a black box. You have to be able to somehow transform. The segmentation in particular into real customer personas. And I've been doing this in some fairly, you know, important from a business point of view, but, but not necessarily the most glamorous areas like I probably, at some point in my life became the world expert in, somewhat obscure subject to predicting mortgage churn and yeah know, nobody's going to win an Oscar for being the churn world's expert thing. That's interesting to me about, Touchpoint and Ipiphany is, is that they are actually at the leading edge of this brand new area called. AI based natural language understanding, but most excitingly it's being applied to, an area of business business problem,right in the middle at the heart of the leading edge of, Where businesses are today and in particular where a financial services businesses are today, which is trying to, realign themselves around the digital experience of their customers because things now move.
You know, like, speed of light to be able to, respond both, with the operation and strategically to how customers are, both, feeling and, thinking as well as how they're behaving. So again, my experience now, so is that in order to be able to understand behavior, you need to understand customers thoughts and feelings.
05:52 Glenn: So why are customer feedback analytics so hot right now?
05:57 Nick: Well, I mean, that's the point really. It'sbecause, companies, in general banks in particular, I need to get a grasp of the way customers are interacting with them. Not anymore, mainly through physical retail channel but now through digital but in particular now the mobile app. So we've been tracking in my kind of consultancy work over the past couple of years, we issued a monthly tracking report, which did something really interesting. It tracked, first of all, consumers. Customers movements, using some very, very complex mobile phone data. And then it started to look at their channel usage.
So the first thing that we observed, soon as COVID happened, And this is a very large database in North America clearly in terms of customer movements, they were moving away from branch centers. So they're moving away from town centers, city centers. And then we could observe there was a corresponding rise in the use of digital to not just to you know, interact with your kind of current account behavior, but to buy new products. So, and then, if you start to look at the stats, which are really interesting, for example, and this is just a single use case, in the UK of net new current banking account.
So we're opened on an app, not just on digital, generally online generally, but all of that has now become. Effectively for many, many people who, you know, my son, who's never set foot inside a bank branch. So to him, the app is the bank. And that means two things. Really one is if something's wrong, goes wrong with the app, then effectively that's bringing in his view, that's bringing the whole bank down, you know, the performance technical.
Performance of the app and the customer experience is actually impairing. If it doesn't work, it's enhancing, repairing the brand perception of the entire bank. And in fact, yes, very recently. You know, in Touchpoint we've been looking at a couple of examples of bank apps, bank, mobile apps, which have launched a brand new or singing all dancing, redesigned app.
And both of these have completely bombed, for one reason or another. I mean, it's not just the conduit in the innate conservatism of customers who, who hate and you, you are, it's really fundamental things that are going wrong around the authentication process or, you know, the most obvious core customer journeys like in the US and Canada check deposit thing.
So to answer it, it's long winded way to answer your question. The reason that the natural language understanding applied to business problems, operational example, is that the apps are also collecting, this incredibly large and rich and fast moving repository of customer feedback about how users are feeling.
About different aspects of their app experience banking app experience. In this case, both positive and negative. And the business problem up to now has been held to actually harness and make sense. So all that fairly random feedback in such a way that you can package it up, you know, create, a kind of conceptualized on apology after that.
And most importantly, to be able to apply that to both strategic and operational business decision by the, the issue loans within the organization that need to make those decisions. So that's why, you know, I just find this an incredibly exciting area. You know, the reason I joined Touchpoint is that, you know, my view that they are the leading exponents of using natural language understanding, in order to make sense of customer feedback from verbatim app reviews.
11:09 Glenn: We've talked a bit in the past in and around Clarabridge and Medallia, and, and I think the terminology that you used, is around different ways of calibrating the heat in the kitchen. but to expand on that a little bit. And the whole Clarabridge Medallia, enormous phenomenon around the, out the out the collecting data and really where the differences is and getting those insights in driving the, the action. So what does that, what is the difference between something like that then and what you're doing today?
11:47 Nick: Yeah. Good. Good question. So, Medallia you know, is a broad kind of customer experience platform, which we've probably incorporates aspects of natural language understanding. Clarabridges is more a natural language understanding platform is steady. Both of these companies kind of got huge valuation. So you know, Clarabridge was acquired by Qualtrics last year for over a billion dollars and the thing Medallias latest valuation 6 billion. So yeah, you know, potentially, market capitalization, point of view. This is a really hot space. I think the difference with, Ipiphany which is Touchpoints own natural language and understanding solution is it goes a lot further, towards, you know, creating a year and a workflow to the verbatim data that is attuned specifically to the needs of the business user and business decision-making and I mean, Yeah, it's quite interesting in terms of, again my experience in terms of product software, product development says that you really need to sort of focus it in on the needs of a particular subset of users.
So for the mortgage thing we did was guess targeted at a handful of people in the world who are responsible for managing mortgage tension. So the way the Ipiphany product roadmap is going, is to specifically tune it to, financial services and, people in financial services, who, whose job it is to either maintain the apps, you know, the DevOps team or to, orchestrate customer journeys within the app, for particular customer tasks by buying new products or to keep adding or maintaining or enhancing, feature functionality around. You know, essential business tasks like payments or, current account statements. And then last year I think this is most interesting.
The customer experience and brand teams that are really responsible for the whole integrity of the customer's relationship with, the bank is a brand and that's to me, the most interesting thing, it's almost like exhaust fumes come out of the app reviews, but we've now got this whole category of feedback called customer trendily called customer love and it's really hard to shift the needle in customer love. If something goes wrong, that needs moved down quickly and that's the most damaging, because as I was trying to say earlier, that's where the experience of the app actually impacts their entire experience of the bank. And all the banks got really think about it is it's brand and you know, that's something that goes way beyond, you know, the people, the sharp end of the bank to rise up to the C-suite and the CEO.
15:26 Glenn: So Nick, for the uninitiated that don't know a huge amount in regards to how we look at engage customer writings and banking app feedback. Overall, give us a simple explanation of engage, engage customer ratings and how it differs from your overarching app score rating and the impact that they have. There's a lot to unpack on that.
15:52 Nick: No, that's all right. Right. So the concept effectively drives the Ipiphany customer feedback analytics in the case of app stores is engaged customer rating and engaged customer is one that leaves both the rating and a verbatim comment explaining that or adding context to that rating in their feedback. So we filter out, you know, the great unwashed who just leave a rating in a kind of, you know, fairly slapdash kind of way. which is left with the people that have left the rating thing and really thought about it. And the effects of that is really interesting because if you just look at the vanilla rating you know, most banks get like four or five or something like that, or potentially one or two, but, but you know, it's really hard to kind of prize apart the banks in the competitive set in relation to the ranking, because if by rating, because it effectively, you know, everybody gets more or less the same.
As soon as you filter out the, people who only leave a rating, you suddenly get everything gets shown in kind of vivid, sharp relief. And if I can just try and share my screen
17:26 Glenn: And for those of you that are going to be listening to this, we'll try and give you, a good colorful description of what's on screen.
17:35 Nick: What we did was, Compared banks, engaged customer rating with, a kind of common business KPIs, which is return on risk weighted assets, which is broadly how efficiently they managed to extract value from their customers and you know, we were quite surprised when we saw this, because it shows that there are remarkably, there is a distinct relationship. So the banks that have a higher engaged customer rating score who just did not produce actually achieve in general. A better return on risk weighted assets that statistically pretty impressive because it means that the hypothesis that the app now is driving the performance of the back is becoming true and the other thing is that the concept of the engaged customer rating, is also itself very predictive of a bank performance. So for those two reasons, you know, this is an interesting demonstration, obviously the correlation could and will be stronger, but I mean, there lots and lots of different, different aspects of performance driving. We can respect that. But clearly, the customer experience of the app, is a significant contributor.
19:17 Glenn: So you know, again, There's a lot of people out there are looking at banks and the industry and think it's old and silted and stuck in the past. But is it the fast really approaching the leading edge of customer feedback analytics? Well, why is that?
19:38 Nick: Well it's because the banking is now moving, you know, as I said earlier, you know, channel migration, has taken place and you know, all the investments in the bricks and mortar of the branch, you know, has become slightly, different in relation to the need to invest in apps. And you know, this is, you know, the app is now the new battle ground. So, you know, we run these ranking benchmarking reports every month now.
And you know, it's clear that there are obvious winners and losers and, you know, if you wanted to kind of. You know, make some money on the stock market. You could do a lot worse than to follow those benchmarking reports because they, you know, you see them, they are highly predictive of actual bank business, financial performance.
So that I think would be very long before. You know, these, these types of, customer experience metrics start finding their way into financial reports, annual and quarterly report. Because, you know, the main style of the ultimately the value of the brand and, you know, this, this can't be a more important very important.
21:06 Glenn: Yeah. And they also talk about those processes outside of the App itself, like customer service, product features. And what does it, what does it all mean? As far as output goes for customer feedback? And for people like chief digital officers and things like that.
21:28 Nick: Yeah. That's a good question. So, you know, obviously the chief digital officer is one of the main consumers of, of these insights because he has to run the channel, and he's also responsible for its ongoing successful failure. And obviously he's got, you know, people responsible for, DevOps features more after that. It inputs into that roadmap. But as we've seen it's increasingly kind of spilling beyond the cheek digital office into you know, the custodians of the whole bank, brand and, you know, I won't be very long if it's happening now, actually we are being asked to help, digital teams prepare, C level reports. In other words, all this stuff is going to go straight up to operating board because it's now recognized to be, you know, the main signal in terms of customer and their customer.
22:44 Glenn: Yeah. It's mission, critical feedback right? Yeah. So there's all sorts of other reports and things out there. Your JD Powers, your Ipsos. You know, these NPS there is a plethora of options that are out there for the people and they all have their, they all have their benefits. What is it that, that gets you excited? Not just Ipiphany but what are the things that are out there that you look at and you think. These are, these are, these are helping businesses progress and why?
23:19 Nick: Yeah. I mean, the JD Powers and the Ipsos are fairly old school in that, you know, they are very kind of, you know, highly reputable and published widely. So people take notice of. And they're fairly old school, so they're kind of, you know, you can research we've, we've kind of closed questions, you know, that they sample, but the sample, isn't necessarily representative of people that are engaged with that at that point in time. So I think, you know, they have, you know, important, but they have limited, utility from the point of view. Practitioners, people have to kind of operate within the bank curiosity very much about what to do if you are some way down that link so they're not my diagnostic tool. Then I mean the one, then there's one that comes closest to the top of benchmarking reports, a Touchpoint through issuing from Ipiphany to the banks is Brain PRISM and you know, obviously I know the owners of NPS and invent, you know, they've got that brand name, the customer, that is good.
It's pretty detailed. It bothers to actually drill down to specific customer journeys and benchmark banks against those journeys, but it doesn't really utilize, customer feedback, verbatim feedback to create those journey on politics. So those are, those are kind of preconceived. So if a new type of customer journey comes along, you know, to do with crypto or something to do with buy now pay later, Bain has to catch up, whereas Ipiphany immediately sees that phenomenon those, those new trends, customers talking about those things within the live app store data and get an immediately creates a concept around it and so forth. So the difference, I think really is that at the end of the day, Ipiphany is based on what customers are saying not, what customers are being asked.
And I think that's the important thing is the question to some extent the answer, whereas with the Ipiphany you're actually based basing the ontology on customers, talking themselves
25:59 Glenn: The the scary version of that as you're getting the answer before you potentially needing to ask the question.
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