Rule the World with Customer Experience
WILSON RAJ: Today, extreme consolidation and accelerated digital transformation is changing the global business landscape. To win, businesses must embrace agility and use automation to address customer needs. It's all about adopting a customer experience strategy that speeds decisions, reduces risk, and enables real time customer engagements to stay relevant, valued, and in demand. Hi, I'm Wilson Raj and welcome to this episode of Reimagine Marketing Podcast, Rule the World with Customer Experience.
I'm super excited to have my guests, Ray Wang, founder, chairman, and principal analyst of the Silicon Valley based Constellation Research. Ray is an expert in his field. He is well known. He hosts a weekly enterprise and business leadership webcast called Disrupt TV that averages 50,000 views per episode.
He's also a prolific writer, authoring a business strategy blog that has received millions of page views per month. Ray also serves as the nonresident senior fellow at the Atlantic Council's Geotech Center and he has been on countless stages and keynotes all over the planet. So, welcome Ray.
RAY WANG: Hey, thanks a lot, Wilson, and finally I'm now on your stage. So thank you for having me, really appreciate it, and glad to be on your Reimagine Marketing Podcast.
WILSON RAJ: Hey, it's fantastic because I know we do a lot of things together and it's always nice to see you in action and it's nice to actually, for selfish reasons, have you on this podcast to really have a great conversation on a lot of things you have done. But suddenly I think your latest book, Everybody Wants to Rule the World. And I think I'm going to start there first, Ray. This book certainly is a timely read, the backdrop is certainly COVID-19 scenarios, it has digital transformation, but your thesis there is that that itself is not enough.
And as I kind of read through some of the reviews and the summaries, you have been really aligning around some historical narratives. Bringing in Alexander the Great, quoting the British military strategist, and he says time was his constant ally. He capitalized every moment, never pondered on it, and thereby achieves his ends before the others had settled on their means. That is JFC Fuller on Alexander the Great.
So let's start there. What was the impetus and the main thesis for this book?
RAY WANG: Yeah, taking a step back we started Constellation in 2010 focused on digital transformation. I wrote a book that kind of summarized the theses of the company, which was Disrupting Digital Business that was published by Harvard Business Review Press. That book laid out the groundwork for where digital transformation was heading. White data was such an important asset how people were building new business models.
So in 2018 I started the process. They tell you to write the book after the book comes out for your next book. I forgot to do that. So five years later, I'm like, OK, let's see what happened. We started looking at companies in 2018 to understand what was going on, 2019 we started to get some weird trends, and we realized that wait digital transformation, as you were putting, is not enough. Something is going on here.
We got a different class of companies that were exceeding expectations. And it wasn't just like, oh it was like one X over 2X. Let's put this in context. In 2017 Facebook, Amazon, Apple, Netflix, and Google, their market cap was about $2 trillion. And Microsoft. There were about $2 trillion. The Fangs plus Microsoft today their market cap is over 10 and 1/2 trillion. Never before in the history of capitalism, in the history of organizations, have companies of that size and scale quintupled.
WILSON RAJ: Yeah.
RAY WANG: And so usually they get big, they get sloppy, they get lazy, they forget what sight of their mission is, right? They have a bad management team in the back end, their products no longer scale up, but in a digital world these companies completely changed the game. And so the book starts out by talking about what are the elements of these digital giants, what's required to partner and compete with digital giants, what that lifecycle is going to look like, and of course how do we keep fair and free markets as we regulate digital giants.
WILSON RAJ: That's such a fantastic statement because you're right. Digital transformation in and of itself is not enough. Right? There are other factors. And I think there's a great concept that you have coined decision velocity. I love that, because you and I have been talking quite a bit, right, in our engagements around, and I quote you, "data to decisions," right? And now this term decision velocity really bridges and spans that gap. Can you talk a little bit about that and some examples of what you have seen in your really expansive research, not just for this book but I think really over your career?
RAY WANG: I appreciate that. Decision velocity kind of works like this. You and I make a decision per second, but it takes us what a day to get out of management committee? A week? A quarter? Maybe a year? Right? It takes us a long time, right? And most people have that challenge. They're constrained by the decision making capacity of an organization. Machines make 100 decisions per second, even thousands of decisions per second.
That asymmetry is what we're competing against. Companies that can make faster, more precise decisions on a consistent basis are going to win. They're going to create an exponential advantage by being able to do that. And to deliver on decision velocity there are a couple of things you have to do, and three of them are important. This is analytics, automation, and AI. We have to ask the right business questions.
That's where the analytics comes in. And sometimes the answers don't exist. We have to find sources. There are external sources, internal sources. And automation, because that's how we actually collect the data and the information and insights and how we actually scale up by bringing that information and collecting at a much faster rate. And then the last piece is the AI. That's the basis of the business graph. That's the basis of these new companies, which are these data driven digital networks.
These are the platforms, the 100 year platforms with multi-sided data networks that they're powering. And AI provides that capability to have the institutional memory to build the business graph, the intersection of a customer, an employee, a supplier, a partner, with an object like an invoice or an ad or an order or a request. And the context. Weather, location, time.
Thinking about your sentiment. Wait, what was your heart rate, Wilson? Were you smiling when you were at that engagement? Who were you with? All that data is being captured for future use so that they can anticipate what's next.
WILSON RAJ: Right. I think those three As, right, analytics, automation, AI, is really the fundamental crucible in which that decision velocity happens. And I really love frameworks like that because it really helps I think business leaders and marketing leaders to really hang their technology, stack on their organizational structure, even measurement. So we're going to talk a little bit about some of those things further down. And you mentioned business graph and I do have a specific question on that a bit later, too.
But as you see in your research and putting this book together, I mean, you came up with very surprising examples. And these are not the digital natives, per se. These were some old older, traditional brands that have really adopted those three As, analytics, automation, and AI. Can you speak to some of those? And maybe some of the surprising things you found in terms of customer experience and meeting consumer needs?
RAY WANG: Yeah we saw that. I mean some great examples were I think we use Phillips as an example. How they're using data to compete, how that data is actually changing the way we look at health care. Every device has become a sensor. We looked at companies like Honeywell in terms of their connected buildings and their ability to actually build digital twins of buildings and kind of deliver on that.
And we started to see other examples where people have built businesses that are on data. And I'll give you an example in terms of industries that are collapsing against data value chains. Communications, right? Telecom. Media, entertainment, software, and tech. They're really the same business today. We have a digital asset. That digital asset gets put out through distribution networks. Those distribution networks run on a technology platform.
And we basically try to build the biggest customer networks that we can. And you see that, right? Whether you're selling a game, music, a video, a live stream audio, enterprise software, it's really the same set of business models and monetization models. And that's why we see comms, media, entertainment, and telco collapse and come together.
WILSON RAJ: Right.
RAY WANG: Retail, manufacturing, distribution, same thing. We're going to see that. Hospitality, health care, that sounds an interesting one. Hospitality, health care, and insurance? Well those are really experiences. Right? And they're data driven experiences. So we'll see more of these types of collapsing around data value chains and I think that's where people should spend some time understanding where upstream data, downstream data come together in the Cloud and more importantly, how that data is being used, consumed, or being able to be created as inputs.
WILSON RAJ: I think that's a really fascinating idea, Ray, around not just CX innovation within a vertical, right, or a sector. It's these multi industries coming together as you mentioned in different combinations and probably there are other combinations that we have not even seen yet. And that is now the totality of the experience, right? And therefore that data becomes that connective glue.
So it's really I think an important point for our audience to listen to. There's one thing I want to press on and you mentioned it so many times, and this notion in your book. You call it data supremacy, right? With analytics and automation as sort of accelerating and you talked about, I think you tweeted this and it's certainly in your book, that data is a foundation of every customer experience powered moment. Every company will have to be competing for data supremacy.
So can you unpack that? There's a lot in that tweet, in that sentence. What are some of those attributes that can be gleaned and be practical for our listeners?
RAY WANG: Yeah, it's important to think about data. It's really about having that analytical mindset, asking the right business questions. Part of the challenge in most organizations is they only ask questions to answer as they have. And that works, right? I mean you want to know what your performance is, you want to know where you are in terms of profit margin and how many employees you have. That's great, right? Those are our answers you know. But let me ask you this question. Should we add 10 more people to the marketing team, or do we add one million to the budget? Which ones are going to have better left and which ones is going to drive more revenue?
WILSON RAJ: Right.
RAY WANG: Now you and I know we can't answer that question right now because we really don't know. And part of that is because we don't have all the data sources we should be putting together. And many times we actually don't even know where those data sources are supposed to be.
But when we start asking those types of questions, we start getting closer to business alignment. And by getting closer to business alignment, we can start figuring out what techniques to apply and more importantly as well, what tools to apply. And so that's an example of where this is headed.
The other way to look at this as well is once you have that in place, we want to deliver on what we call precision decisions. And precision decisions are hard, right? We make exceptions all the time. We break rules all the time. And automation doesn't know that that's happening.
And so if we can actually figure out how to augment that, reduce the number of false positives, reduce the number of false negatives, we can improve precision in that process. And we want to automate precision decisions because we want to bring this to a point where when we see a gap we can actually improve the false positive, false negatives rate and improve the precision. And then we get to that concept we talked earlier about, which is decision velocity.
WILSON RAJ: Right.
RAY WANG: How do we use that to drive decision velocity? And so this whole notion of what you asked about, competing on data supremacy in that chapter and in that lesson learned is the fact that organizations that understand that data is their most critical asset are the ones that are going to win on data supremacy. And you apply, you get information to the Cloud, you think about the impact of data, and then you apply the three As of decision velocity, analytics, automation, and AI, and that's how you come to it.
But there is one more piece around autonomous enterprises. Is their time to talk about that?
WILSON RAJ: Absolutely, Ray.
RAY WANG: OK. So the autonomous enterprises are important because in every single business process we're going to ask four questions. When do we fully, intelligently automate something?
WILSON RAJ: Yeah.
RAY WANG: Now I'll take you back to senior year in college. There was a class I wanted to take on a Thursday night so I could avoid a Friday class to make my major and graduate. And it was a class on the history of something like train accidents. Something like that. One of those kind of classes you're just trying to fulfill a requirement.
WILSON RAJ: It sounds rather esoteric there.
RAY WANG: Yeah it was very esoteric along with the history of the American automobile. But so this class was interesting because in the history of train accidents it was the conductor was asleep, the conductor was drunk, the conductor was on a mobile phone, right? So it was always human error that drove that.
Now when you go to an airport that has a self-service, automated monorail or connector between different terminals, do you see anybody driving that thing? No. It's fully automated. And in fact, not only is it fully automated, there might have been maybe a handful of recorded accidents ever with these kind of devices. And it's automation of a train. So we have full intelligent automation today.
The second question that we ask is when do we augment the machine with a human? And that's where we start to train and pair these machines and that's where the false positives, false negatives also get looked at, because what we're trying to do is figure out where are the errors? Right? Why is something not perfect? So 99% accuracy in manufacturing is not bad. We want a few more nines, we get that, right? You know, but 99% accuracy in healthcare would you take, Wilson?
WILSON RAJ: Yeah, I think not in this day and age, probably.
RAY WANG: But your doctor is only 87% accurate. Right? So we believe that the human is more accurate when the machine is actually more accurate. And we have higher expectations for machines than we do on humans. That's what we've just proven over and over again. But that's what happens when we augment the machine with a human.
But then we're going to augment the human with the machine, and that's different. That's where we democratize data. We give people the access. They make the insights. They jump in and they use that information to make faster decisions. And then at some point you and I going to trust human judgment.
So let me give an example. It's 2:00 AM, I'm checking into a lower budget hotel in the RDU area. I don't want to see the clerk. I want to choose my room. I want to tell him my preferences. I'm allergic to feather. And I want a late checkout and an invoice sent to me and a mobile key. I don't really want to see anybody, right? That's fully automated self-service in the back end.
But imagine if I'm checking in at the Umstead. I want to pull up to the driveway. I want the door person to welcome me. I want to smell the scent airburst. Oh, it smells like the Umstead. I want to see the flowers, right? I want someone to hand me a flute of something to drink and someone to greet me and say hello.
I'm the same person. Nothing changed. Right? And our ability to offer choices and friction when there's value or automate when it's more necessary, that's what's driving the future customer experience. That's what's driving how decision velocity plays a role. And that's where analytics automation and AI come together.
WILSON RAJ: Right. I think this is really I think what you're building up to. With the decision velocity, right, with data as a foundation activated by those three As, is coming down to this notion of immersive, right, and what you call ambient customer experiences that's really delivered for mass personalization and at scale.
So we did some research, we found that brands are really stepping up. In a separate survey we did recently, a pulse survey to experience trend data, which is around the future of experience, around how they are accelerating their deployment of customer facing technologies such as AI, AR, VR, holographics, customer journey analytics. And we found that the cohort that was just-- This was mid-pandemic that we surveyed, that 33% of brands are accelerating them and shortening those timelines by literally 24 to 36 months.
So that's not trivial. That's really huge. So the question is this notion around ambient experiences. You know, there's mass personalization and then at scale. You know. Great to hear, but sometimes it may seem at odds with each other. So as you did your research and from your findings and just your experience, how these two elements you know reconciled from an immersive and ambient experiences like the example that you talked about, what's the underlying principles or enablers to be able to do mass but then at scale on top of it?
RAY WANG: Great point. What's happening in the world of ambient experiences, these are things that are happening in the background. They're being observatory, they're understanding what you're doing, they're buying relevance and contacts to make things happen. And the idea is that in a digital world every choice you make has a demand signal. It informs of what's going on.
And so we actually see that the context plus the choices plus the ability to deliver an anticipatory analytics give people the capability to actually personalize over scale. That's the fundamental thing. But I'll take something even step back. Let's use a work example. Your return to work.
You come into the office, it's 2:00 in the afternoon, 27 point facial scan, gait analysis says, hey, that looks like Wilson. Not like you have a lot of skyscrapers where you are. But imagine you're working in a 50-story building and you walk in into the lobby. And it's 2:00 in the morning, and you've been standing there for 20 seconds. They should actually send an elevator down. What are they waiting for? You don't need to push a button. There's someone sitting there, and they've come in through for 20 seconds.
OK, great. You swipe your badge. The digital exhaust and the digital footprint starts kicking in it says, hey, we think it was the guy on the 14th floor. Now it's the guy on the 14th floor because he swiped the badge. You get into the elevator and the console says, hey, would you like to go to the 14th floor? You know, your glasses a little VR chip in there and says, hey, would you like to the 14th floor? Your phone says, hey, would you like to go the 14th floor?
I mean, these are immersive experiences. It doesn't matter what channel you're in, it suddenly says, hey, would you like to go to the 14th floor? But wait, your boss. She's on the 48th floor and you've got a 15 minute window. And you could actually make it and set up a meeting with her. Would you like to meet up with your boss?
That's very interesting. Now there's a 90% probability you would have gone to the 14th floor, but now we've entertained another idea and there's a 70% possibility you might catch up with your boss who you've been trying to catch up with for the last three weeks. So choice number two pops up.
But wait, there's more! There are free donuts on the 10th floor.
WILSON RAJ: Oh that would be-- I'd be rushing right there.
RAY WANG: See, I'm learning. Your decision of choosing the donuts on the 10th floor was smart, but did you take those donuts back to the office, did you eat it yourself?
WILSON RAJ: Share it with the boss.
RAY WANG: Or did you give it to your boss on the way up?
WILSON RAJ: There you go.
RAY WANG: There you go. Share it with the boss, right? And that's the learning, right? And so what do we do? We just walked through this. We talked about the fact that we digital exhaust, right? Then we figured out how we were going to deliver on a set of immersive experiences. Those immersive experiences didn't care what channel you were in, right? They basically took the context, they understood what was going on, delivered different channels.
Then we actually started delivering the personalization at scale. We had anticipatory analytics, I gave you a bunch of catalysts that inspired you to make a set of choices. If you took the choices, we had value exchange. Sometimes it's consensus, we had a meeting request. Sometimes it's an action, you forwarded something that's non-monetary. And sometimes you paid for something, it was a transaction.
And what we start studying is the cadence of those interactions, which we learn over time, we apply to our ML models, and we start bringing that process back in again. And we know what you did last time when you go back into the lobby at 2:00 in the afternoon, and maybe there's not a meeting request that's open, but maybe you'll take the free bundt cake on floor three.
WILSON RAJ: Right. You know, I think you've really introduced I think just a very interesting model or a framework. When we talk about ambient and immersive experiences, most of the time it's more around at least from what I have seen really, connecting digital and physical. Which is definitely a part of it, and your example are on this elevator experience shows that, but behind that there's a lot more. I think I love the context and choice and action and then predicated by learning underneath that where each moment this system is learning.
RAY WANG: Yes.
WILSON RAJ: And then providing better options. More predictive, forward looking kinds of choices for the consumer or the person to take.
RAY WANG: Yes.
WILSON RAJ: So I think that's really a new, fresh idea. So appreciate that. There is one term, Ray, that you mentioned earlier on that I think part of this dynamic. You talked about brands or organizations building a business graph, right? Very much of how social networks have a social graph.
RAY WANG: Yes.
WILSON RAJ: So what does that mean as a brand? What are the aspects or attributes practically of building a business graph for differentiated CX? What the componentry there?
RAY WANG: Yeah, the business graph is anything from the CDP on the back end, your ability to figure out relevance, your marketing intelligence, your customer intelligence, the analytics on the back end. But let's take a step back. Like social graphs and social networks, it's that interface, right, between the customer and a object and the relevancy on the back end that we talked about.
And when you build out the business graph, what happens is you're using the volume of information and the levels of connectedness that you have to other networks. What you're starting to do is identify patterns. Because what we're trying to do is go from data to decisions. Lots of data, lots of insight in the business graph, let's align them to something like a business process, incident to resolution, order to cash, campaign to lead, and what we want to do is start mining for patterns. And so you need a lot of data in the back end to make this happen.
WILSON RAJ: Right so that becomes sort of a template, almost like akin to a customer profile.
RAY WANG: Yes.
WILSON RAJ: But enterprise-wise for CX and then to be able to activate on the merging, the technology, and the intelligence, and certainly the outcomes in terms of journeys and in moments. Have you seen, just on that path alone, examples of companies, big or small traditional or digitally native, that are doing those kinds of things particularly well?
RAY WANG: Yeah let's take a great example. I mean let's take Tesla, right? People like, oh, they're doing EVs and that's great. But at the heart of it, Tesla is a data company. Those cameras that are driving around or capturing location information, mapping data, the analytics behind your car behaviors, powering the future of Tesla insurance. Right?
You could get underwrited by Tesla because they've got better data than the insurance companies on your performance, on your safety records. Right? They're building electric grid management capabilities with not just your car, which is a source, and not just the charger or solar panels which is also sourced.
But also what happens when you interact between the choices, right? What draws peak demand? What do ISOs have to do? Can you provide additional demand by rerouting what you put into the batteries? Right? And so you can see all across the board how much Tesla is a data driven digital network. It's a data driven company and we're seeing them compete for data supremacy.
WILSON RAJ: Right. I appreciate it. Thank you. That's a great example. I'm sure there are others. I think what you said within that, there's a selling point. Tesla is a data company. And I think by definition, every company is a data company.
RAY WANG: They just don't it yet.
WILSON RAJ: Right, to be able to succeed I think in this new world. Yeah, there's no getting way around it. Wow. For the audience, just to summarize, I think a couple of points, at the end of day, capturing data is really what allows long term success for customer experience in this very, very complex changing digital world.
To survive, really, brands need to rethink everything. You know, journeys, experiences in the way that you talked about, right? Anticipatory, immersive, but yet having that business graph to be able to optimize those linkages between the data, the technology, and certainly the whatever, the experience interface.
And of course, the automation and the AI that helps empower that, that's a great summary. So just before we wrap up, you are a person. You're just prolific. You're thinking about these things. So I'm just curious, and I'm sure the audience is also curious as to what other topics you researching or pondering about that are taking brands from status quo to become a market leader or market disruptor? What are the other concepts that's buzzing around your brain that you think you want to maybe write another book on?
RAY WANG: So if I were to write a book today there are two book ideas I would do. One is more tech related. The other one's a little bit different. The tech related one would be about the metaverse economy, or DeFi, decentralization, where new digital worlds are going to play.
The gap between the analog physical world and the digital world, how do those interfaces and interactions occur? And of course how these transactions are occurring with blockchain DeFi. With what's happening with cryptocurrencies and new types of coins with NFTs and what's happening with the representation of digital assets. That's where we see this future on the metaverse economy.
WILSON RAJ: Cool. Well we can't wait for that. And in the meantime, I would encourage our audience to pick up your book. Ray, any tips there?
RAY WANG: Yeah.
WILSON RAJ: Everybody Wants to Rule the World. And it's not the Tears for Fears song. And by the way, I had a quick question on that one, Ray. Did you have to get permission from those guys or?
RAY WANG: You actually don't for book titles, but we do--
WILSON RAJ: OK. I was wondering about that.
RAY WANG: If Curt Smith is listening around, happy to sit down with you, catch up, maybe we'll bring you in to actually do a cover live or something fun. But yeah.
WILSON RAJ: That would be something.
RAY WANG: That would be awesome. So. But, hey, tips on the book. Really simple, if you're building a digital giant there's five lessons learned. Here's the cliff notes. Disintermediate the customer account control, build the biggest network, compete for data supremacy, figure out digital monetization models, and play the long game. That's how digital giants work.
WILSON RAJ: And there you have it, folks. Great spot to wrap up this discussion. Pearls of wisdom there, but I would encourage everyone to get the book at your favorite bookstore. That's it for this week's episode of the Reimagine Marketing Podcast. Thank you, Ray, for joining in.
RAY WANG: Thanks a lot, Wilson. Always a pleasure.
WILSON RAJ: Same here. Now if you enjoyed today's show, be sure to head on over to SAS.com/reimaginemarketingpodcast-- all one word-- to join in this conversation and discover more fantastic bonus content, you can obviously also subscribe to the series on your favorite podcast platforms. Just search for Reimagine Marketing. So stay in touch and share your topic or guest ideas by also emailing us at reimaginemarketingpodcast@sas.com-- all one word. So till then, don't forget to join us for another episode. And thank you for listening. Have a good day.
