INTERVIEW
Deep Thought from HBS Deep Thinker
With Das Narayandas – Professor, Harvard Business School
The Edsel Bryant Ford Professor of Business Administration at the Harvard Business School, reflects on their early connection and dives into decades of B2B client relationship expertise.
Das Narayandas naturally covers decades of B2B client relationship expertise, but also shares insights from his groundbreaking marketing dissertation where he emphasizes the nuances of long-term supplier customer dynamics. And from there, we transition to critical aspects of customer selection, service, and indeed the evolving landscape of customer lifetime value.
Das offers a unique perspective on strategy where he advocates for the dynamic nature and continual alignment with the most attractive customer profiles. Of course, we naturally also talk about AI and Das recounts his transformative journey through the pandemic, translating complex data science into some quite practical business applications. Particularly interesting views on the intersection of human machine interaction and predictive analytics, navigating biases with what he describes as a healthy skepticism approach, and the concept of augmented intelligence.
Richard Owen
I’d like to start, if I could, with a recollection from not long after, I think, the first time you and I met. And we had our very first Net Promoter Conference. And I want to say it was like 2008. It was in New York. It was at the Essex house right on Central Park. And you were, I believe, our first opening speaker for the whole conference series. And you said something at the time that has stuck with me ever since.
You had the 2×2, which I’m sure you have like 1,000 different 2x2s because it’s a professional obligation in your business. And I think you’d characterize customers who were neither financially attractive nor particularly loyal to a business as strategic because that was the excuse people constantly gave for why they continue to do business with.
Das Narayandas
You remember too well, and you remember too much Richard.
Richard Owen
That’s well, it just goes to show it is something I quote frequently because it’s had a profound impact. But perhaps you could start by talking a little bit about the arc to your research work. Go way back in time perhaps and start how did you get into business-to-business marketing and what’s been the sort of direction your research has taken over the years?
Das Narayandas
Sure. So, when I came to the US to do a PhD in management science with a major in marketing, the in thing then was using logistic regression and doing brand choice models using scanner panel data. And there was one data set with coffee purchases and another data set with some cola. And for some time, I tried my hand at doing that. And, you know, at best I could replicate what had already been done. But it was hard for me to come up with something interesting, something new.
And, you know, a PhD is about pushing the frontiers. So, you know, not being successful in trying to figure out consumer marketing, I kind of thought about what I should do next and realized that the one place where I had an advantage was my own background. I had worked in B2B sales and marketing for six years before I went to do my PhD, came to the US to do my PhD. And so, I said, let me go and go back to my roots and focus on what I understand and what I do well. And so that was where it got started, and the right thing to do because ever since then, I’ve been focused in this area. So, my dissertation was actually essays on managing long-term buyer-seller relationships in B2B markets.
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One of my dissertation papers, which is frequently quoted because it was one of the earliest papers in the academic journals, in the journal of marketing, was a paper using empirical data to validate if being in a long-term relationship made sense to the supplier. Till then there was data on how customers benefited from long-term relationships. So, Toyota, I mean, just picking one company, I mean, gets benefits from having long-term relationships with its suppliers, but for anybody who’s done business, B2B, I mean, working with large customers isn’t an easy thing to do. I mean, the power is on the other side, and they know how to kind of get the most out of you. And the point that you made strategic accounts, that’s the classic example. You’re typically your largest customer. Happens to be those where your cost to serving them is very high and you don’t get much in return. So, while it was very well established, customers benefit both in B2B and B2C in having long-term relationships with suppliers.
There was no empirical evidence that whether the suppliers did well. It was evident in B2C where brands benefited from loyal customers. But if you’re a professional service firm, if you’re an accounting firm, or you’re a hard-hat traditional industrial marketer, I mean supplying to these large customers, does it make sense? So that’s really what I looked at in that paper. And what I found there, which is interesting, is that yes, the large customers squeeze you out. Your margins, your gross margins are lower when you deal with large customers as compared to being in a transaction orientation with your customers. But where you benefit from are your sales and marketing. costs. So that was the first time we had empirical evidence that big customers squeeze you out but dealing with big customers also makes you more efficient in your sales and marketing activities. And so, my journey started there. That was what I did in my dissertation. It was very well received. It’s even today one of the most frequently quoted papers because I was one of the first.
So, there is an advantage to proving something before anybody else does, because others have but to talk about what you did.
Richard Owen
Well, in some ways also, B2B marketing has never achieved the level of focus that the consumer side has, right? And we’re all taught early on that the problem with doing business with consumers is they don’t respect your right to make a profit. So, there’s this notion that consumer is a big problem because at the end of the day, even if they have brand loyalty, they don’t care whether you make money. Whereas in theory, you’ve got this, it may be asymmetrical, but you never have this relationship with your customer in B2B, which says they want you to be around. Yes, they may push you, but they have a stake in your success to a degree. And so that nature of the relationship is so fundamentally different, but not well examined. People haven’t spent time and effort thinking about how these B2B relationships play out.
Das Narayandas
Absolutely. So, I’ve spent the last 30 odd years focused in this area on client relationships. Initially, it was in product marketing firms, spread then to professional service firms as well, but my primary focus has been B2B. But nowadays, I kind of look at B2C too. But if you ask me, my DNA is B2B and kind of look at how do you select customers you want to serve.
We all know that the best time to say no is right at the beginning, whether it’s interpersonal relationships or inter-firm relationships. But how often do we do that? Do we have the discipline to say yes, or do we say no? And you and I, Richard, have talked about this at length, that the very point that you just made, we land up not getting into good relationships. But once you select, you need to then be very clear how you want to serve your customers. There are some customers who want hand holding, some who don’t. There are some customers, so different customers play different roles. So, you need to serve them in different ways. So, selection is important. How you serve them is important. And then, you know, you’ve got to constantly monitor whether they are getting, you know, they’re getting value from you, you know, whether you measure it through satisfaction or loyalty. There has to be some way for you to understand whether you are satisfying them.
And then at the end of the day, I mean, you need to survive to play another day. So, select, serve, satisfy, and survive. And as I tell my, you know, I teach entrepreneurs and businesspeople now, senior people who run businesses. And I tell them, you select, you serve, you satisfy, you survive, but then thrive if possible.
Richard Owen
And one of the points you’ve made in the past, which again, I constantly steal and use and only slightly represent as my own, is that the nature of that selection means that to some extent, it’s inevitable that you become the business based on the customers you do select, right? And the idea that you have any control of selection, first of all, is something a lot of marketers don’t fundamentally think of. The pressure is there to essentially drive revenue. The idea of being selective is almost a former concept. Then the second concept that says, well, if you then organize your operations to serve those customers, then you’re building your company in a way that suits them. I think there’s also a great quote from Peter Fader, who I know you know over at Wharton, and he talks about account-based marketing.
It’s all about customer selectivity and building your operations to serve the profitable customers at the end of the day. I think we’re more sophisticated companies now thinking about this. The shift, surely, towards thinking of customers through the lens of lifetime value makes it even more important because you can no longer look at that initial transaction and say, oh, this is great. We can make money on the deal. You have to think about the next 10 years.
Das Narayandas
Absolutely. I mean, like the point that you just, when you started off, just when you what you mentioned is important. I mean, as I keep saying, who you serve affects who you become, who you become impacts who you can serve. And every time you decide to serve a customer, especially in B2B markets, it’s an endogenous decision. I mean, there’s an endogenous effect. Who you decide to serve impacts your resources, impacts your capabilities?
It’s like, you know, the very act of picking a customer changes your trajectory. This is that old Heisenberg uncertainty principle. The act of locating an object moves that object. I mean, similarly with the act of serving a customer doesn’t move the customer, but it moves you. And you know, I am appalled by the amount of time, you know, businesspeople I work with, they spend an inordinate amount of time talking about strategy. But what they don’t recognize is that when it comes to execution, every time you make a customer selection decision, you’re affecting yourself. So, strategy has to be dynamic. I mean because execution is constantly changing you. And if you’re not careful and thoughtful in what you do, I mean, you’ll end up with two problems. One, you’re serving customers you should never have served, but worse still, you don’t have the capacity to serve customers who come down the road, who might be fabulous opportunities for you to serve.
Richard Owen
Or you’re bending your whole operation in a direction that is going to thwart good opportunities going forward. In so few companies, you can’t be good at a lot of things for a lot of customer segments. It’s very, very challenging.
Das Narayandas
Absolutely.
Impossible, impossible. Trying to be everything to everybody, not going to work. Because strategy at the end of the day, like Porter says, is about making choices. And choices need to be made because our resources are limited. And recognizing that cycle makes it virtuous. Not understanding that cycle will make it vicious on you.
But what they don’t recognize is that when it comes to execution, every time you make a customer selection decision, you’re affecting yourself.
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Richard Owen
So how does that bring you now to your recent work, which has been around and talked about the impact of artificial intelligence on this? And that’s, I think, really current thinking. Obviously, AI is a very hot topic, means a lot of different things to a lot of different people. So particularly interested in your perspective on this.
Das Narayandas
So, I, you know, look, I mean, years ago, really long time ago, I mean, in a galaxy far away when I was a PhD student, I mean, if you’re going to do a PhD in management science, you’re going to do multivariate statistics. I mean, you know, heavy duty stuff. I mean, and so analytics has always been part of me, I grew up with it. And then I saw all these things happen over the decades and to be honest, I was a bit cynical saying that this is just old stuff being wrapped in new wrappers or, you know, poured into new bottles kind of a thing. And so, when COVID hit and I had to start teaching from my basement and, you know, it actually opened up the world for me because I realized that, especially in spring of 2020, it became very clear to me that if I did not reinvent myself, I would be part of the has-been, not the is-now thing.
So, I decided with a student of mine who runs a software firm (they develop AI-based analytics) I put together a course, it was less about data science because I was not interested in, again, like as I was in my PhD days, I mean, kind of being a follower in a world where many are already kind of talking advanced data science capabilities. My interest was more on the gap between practice and theory. You know, the gap between data scientists and businesspeople. And so, I wanted to take a perspective of, you know, not data science, but business science. I mean, how is business using data? And the reason I picked that up is because over the last decade, even before COVID, as I was talking to, you know, students, people who are teaching, most of them had no understanding of data. I mean what it meant, how you handle it. For example, yesterday I taught a group of senior executives. And I was telling them that you talk about analytics. But to do that, you’re talking about the 20th floor of a building whose foundation you haven’t put in place. And they said, what do you mean? I said, in order to get to analytics, you need to first understand your data strategy.
How do you think about it? I’m not one who’s going to tell you. Which format it should be in, and what code you need to use, and all that. But I have one question for you. Where is your data sitting? And if you’re telling me it is sitting in five different places, in five different corners of the world, you know, you’re building your house on shifting sands. So, it begins there. So, I got interested in understanding how to take the language of data scientists and bring it into a language that businesspeople understood. So that’s where we began. And you know, simple mistakes that get made because data scientists don’t understand the business world. If you’re going to use a variable that comes much after the variable that you’re trying to predict, and if you’re trying to predict whether someone is going to travel to Boston, and one of the variables you’re looking at is did they book a flight to Boston in the last 10 days, you’re going to get an amazingly accurate model that doesn’t make that a good model.
I mean, causality is important to understand. So, I taught a course to the MBA students in the fall of 2020, which was all around understanding the business aspects of data analytics and how important it was to understand that. And then as we did that, I mean, the focus in that course for me was understanding that humans will make decisions, machines will provide. Information, sometimes predictions. And more often than not in the lives that we’re going to live in the coming years, a lot of the decisions actually will be a human plus machine interface where humans will make decisions based on information or predictions provided by machines. That’s where I see it. And that’s what the data scientists call as predictive analytics. And I’ve written an HBR on this thing to help people.
Richard Owen
Right, it’s a great piece. I totally think it’s really interesting how you came by this observation. You know, and it begs a couple of questions, right? I think first of all, as you said, this gap here between management’s understanding of data and analytics versus potential, or let’s just say the current firm that can be used.
Of course, some of that’s because you’re teaching a course at Harvard. If you’d been down at MIT, you’d have had a whole group of people who fully understood data and analytics, but just couldn’t spell correctly. So basically, there’s a little bit of bias there. But I think this issue of how we make decisions in a complementary fashion using machines, as we’ve introduced predictive analytics into frontline employees, one of the biggest surprises has been we were much more nervous about it when we started. We thought people’s reaction to what a computer says is a decision that’s going to be very negative. I’m not sure what your experience has been, but ours has been one where it’s not as simple as people saying, oh, I don’t trust the data or the machine. People are quite reflective. It’s to some extent the difference in perspective that comes from a data versus a human view. People just intrinsically process information differently than a machine does. And maybe it’s just biases at the end of the day. We just have so many intrinsic biases we bring to that we end up in different places.
Das Narayandas
You’re right. I mean, the way I think about it is you, you know, everybody wants to drive at a hundred miles per hour. But if you’ve noticed the needle needs to go from zero to a hundred before you are driving at a hundred miles per hour, there is that time, there is that acceleration you need to go through. And what happens is very often businesspeople kind of get thrown in into the stream where everything is moving at 100 miles per hour and they’re told, hey, everything is okay. No, I mean, what you need to do is bring them, I mean, from a stationary start to that speed, whatever that is. And that is a, you know, the immersive experience is critical. And when that is not done right, there is a total rejection because at that point, someone’s very identity gets challenged.
And that hurts. So, bring people along gradually with care, rather than taking them from one state to another one, which is dramatically different. Because then you are going to run into the world of cynicism. What you want are, as I keep telling people, you can win a skeptic. You can win over a skeptic. You’re not going to be able to have a conversation with a cynic because a cynic is not going to have a conversation with you. They’ve already made up their minds, and they’re irrational about it from your perspective, very rational in their minds. But if someone is a skeptic, then they’re actually open to suggestion and open to learning. And so, the key here, as we want the world of data science to be more pervasive in the world of business, is to walk into a world of skepticism, healthy skepticism, rather than a world of unhealthy cynicism. And I find that to be a very simple issue that is not tackled. People kind of come and force, brute force things. Brute forcing things doesn’t work.
Richard Owen
Well, and we don’t have enough experience, right? So, we’ve got evolution. We spent all our history as a species making decisions based on anecdotes or people telling stories to each other. And then all of a sudden data gets introduced very, very late in our experience. And so now we’re trying to train everyone to make decisions using data instead of body language.
And instead of storytelling, it’s not going to be a painless overnight transition. And I think that the mathematics of whether or not a prediction is correct in some ways is less interesting than can you persuade someone is correct. And if you have a big black box AI machine that spits out an answer, it might be correct, but it’s going to have very low value and persuasion.
Das Narayandas
Yes, absolutely. I mean, you kind of nailed the whole issue. I mean, let’s not get obsessed with accuracy. Let’s get obsessed with performance enhancement.
Richard Owen
Also, we’re trying to nudge people, aren’t we? I think that we’ve changed even our language. We used to say, next best action, and we say next best nudges, because at the end of the day, we recognize that we’re trying to get people to improve their performance marginally. I’m not just talking about online people, I’m talking about boardroom, because you could argue, and I’m sure your experience is, Hannes, that the group that’s often least developed in their use of data can be in the boardroom, surely.
People are quite reflective. It’s to some extent the difference in perspective that comes from a data versus a human view.
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Das Narayandas
Absolutely. I’ve written an HBR piece on how actually predictive analytics will solve the perennial problem of sales and marketing integration not happening. But I also kind of finished that article by saying that much as there is so much opportunity, it’s not going to happen because the people who need to bring these functions that are siloed together, i.e., the board, are the least knowledgeable about the value of doing this. And so, you know, ignorance is not bliss here. Ignorance is dangerous. And that is the issue. This will not be an issue 20 years from now, because people will be sitting in the boardrooms, those that have grown up in the digital world, those that have grown up with analytics. But, you know, that’s the beauty of evolution. There is always a generation that is caught in transition.
Richard Owen
Generationally.
Das Narayandas
And we are in that phase now.
Richard Owen
And the group that can get in front of that generally are the ones that profit the most, right? If you’re one step ahead, talk a bit more about this idea of the sales and marketing integration. Because I read that in your last piece, and I thought that it was interesting, but I have to admit it kind of left me hungry for a little bit more. It seemed like it was a very big topic, because historically sales and marketing functions have struggled to integrate. They tend to think of themselves as upstream and downstream. Certainly, in the software industry, I’m most familiar with the marketing guys kind of want to top spin lob leads, or what they call leads over the wall. And the salespeople basically want to take them and complain about them and claim there’s not enough. And that barrier has been really a concrete wall between the two functions.
Das Narayandas
That has lived forever. I mean, and that’s why the two functions are always at each other’s throats, even when they seemingly are supposed to be having a cordial dinner. I mean, it is, you know, I’m going to have you for a meal, kind of a thing. And the only person who can solve that, Richard, I mean, as I think you were kind of pointing to, is someone who’s sitting above them, the general manager.
And the way the general manager has to solve it is to figure out where and how resources get allocated. And if at the end of the day, the general manager says, I’m gonna allocate resources to stages in the whole pipeline that enhance the probability of a favorable outcome, and I don’t care whether it’s a marketing function or a sales function, these functions will stop thinking about themselves in a territorial mode and start thinking of themselves more in a firm perspective. What has been lacking is then it made a lot of sense for firms to organize by functions because organizing by functions allowed for specialization that allowed for a better way of doing things. But in doing so, local optimization was achieved at the expense of a global. And it was very hard for the general manager to say, okay, I’m looking at the revenues of the firm and I, you know, because there was causality missing. I mean, in most of marketing and sales, the biggest problem we’ve had is there is no feedback loop traditionally. What this world brings, what the world of humans plus machines brings is the finally the presence of a feedback loop. And when you have a feedback loop, you can actually assess the efficacy of your investments.
And if the feedback loop is covering the entire value chain, you then start looking at where the biggest constraint is and resolve it. You’re no longer saying, should I optimize marketing, or should I optimize sales? You’re just saying, I need to kind of fix the pipe wherever the plug is and once you know that, it’s a win-win for everyone.
Richard Owen
Well, and also the downstream.
And if I may also say the downstream operations have the same problem. So, the barriers between organizations continue past customer acquisition, the sales and marketing, and persist through all the downstream operations. And we know that customers don’t care. So, this issue of global optimization is a problem for the entire cycle of value chain. But that’s where the money is, surely. I mean, if you’re looking at this as, as you said, a general manager and your role is asset allocation, resource allocation. The classic textbook in the software industry is trying to solve problems downstream with support functions because you sold to the wrong customer in the first place, or you sold to a decent customer and then you messed up the onboarding so terribly that now they’re unrecoverable. Then your solution is to employ huge teams of people, call them success managers or support people, running around trying to put fingers in the dirt, complete suboptimization.
Das Narayandas
Band-aiding their way back to normalcy, which, yeah, have you ever tried that?
Richard Owen
very expensive, right? It’s one of the reasons that, you know, I think software companies’ margins have historically been good, but SaaS businesses, I think have lost the plot a little bit on how they could be smarter in acquisition, again, selective about customers, and also how they can mitigate long-term downstream costs in an LTV model. We now have the data to tell people this, but as you say, it runs across boundaries and organizations. And we’re still dealing with those hard drawn boundaries.
Das Narayandas
Yeah, you know, that point that you made, I’m kind of, with a colleague of mine, we are kind of writing a couple of cases on firms that are looking at the support function, the success factor functions you’re talking about. And it’s no longer an afterthought. I mean, it is in many, in many cases, that is the key success factor for the firm in the long run because they are the ones who hold the relationship. You know, years ago.
The first case that I wrote actually at HBS was a case on Dell Computer Corporation when the case was when Dell was at that point $20 million from bankruptcy. That’s where the case begins. It had disastrous results with a bunch of loopholes that they had lost.
Richard Owen
Yes, I remember. I remember it. I remember it very well.
Das Narayandas
Yeah. So, in the Dell case, I mean, it’s all about the direct marketing and the direct model. I mean, it was kind of novel at that point. But a sidebar there in that case that I had written, which I had not fully understood but wrote about it but took me a while to understand is there was a field sales organization in Dell that would serve the corporate customers, and there was a tele-sales organization.
This is pre-mobile phones. I mean, we still used landlines then. Now, who do you think was the relationship manager? Most people are “It’s always the field salesperson”. No, it was the telesales person. Why? Because that person was available 24 by seven. If a customer had a problem, they knew there was someone they could call, and they would get them on the line. I mean, support is omnipresent in the delivery of services. To not recognize that and to see that as a necessary cost rather than a delight factor and asking the question, what can they bring to the table in terms of bringing in the right people to begin with, is a wasted opportunity. So, I fully agree with you. I mean, the article that I wrote in HBR was more just on sales and service because, I mean, it’s marketing and sales, but I’m writing another one on.
Richard Owen
Very interesting.
Das Narayandas
One part of the picture. Support, the service function is critical in most of us today.
Richard Owen
Well, we’ve wrestled a lot over the years also with the issue of digital versus human factors and support. And again, there’s no single answer because there are tasks that are really well suited to digital, and customers prefer digital for. And then there are tasks which are ambiguous or complicated and customers want to speak to humans. But software has always been interesting to me because the product is complicated, not well understood, often not well understood by the vendor, let alone the customer. And so, by its very nature, getting value out of that product is challenging. Customer success sort of emerged as an effort to say, well, how do we help customers get value? But being smart about it, understanding how you can efficiently use those resources has suddenly become very popular because all these companies are under much tighter cash constraints. So, we’re in a fascinating time for what you’re talking about because the capital availability environment is finally forcing efficiency. And you can make a pretty compelling case that you’re making an argument for global optimization, which is an efficiency argument. Been unfashionable for the last 10 years because who cares about efficiency?
Now all of a sudden, time might have come when everybody wants analytics to get smarter. They want to make more efficient decisions. And so, it’s the perfect time for people to get smart with data. And perhaps, you know, we’re sort of running up against time. So, I’d love your closing thoughts on that. What do you see as being next? Where is the where are people going to go with these analytics solutions?
Das Narayandas
I think I have my own pitch. Might not be a popular one, might not be something that people want to hear. But before we get to handing over the world to artificial intelligence, I think the next 10 years is about augmented intelligence. And I think we need to kind of, senior leaders especially, those that did not grow up in the digital world, those whose roots are still pretty much in the analog world, need to understand the power of analytics augmenting decision making. And a few weeks back, I was talking to a bunch of entrepreneurs, and I asked them, what do you care about? They said, oh, I care about losing customers. So how do you want to think about it? I said, I constantly keep asking my people, how many customers did we lose? I said, that is great. I mean, do you see a problem with what you’re asking? And they said, no. I mean, I’m obsessed about who we lose.
Can you do a little better than that? And they just couldn’t think of it. I said, wouldn’t it be interesting if you knew who you might be losing? They said, well, if I knew that I could do a lot. I said, well, why don’t you ask your team to use analytics to help you figure out customers at risk? And it was like, whoa. And I’m saying, oh my god. That’s really where just getting to the basics of shifting the mindset from the world that came, customer retention is important; therefore, customer attrition is bad. And so, it’s a binary state to actually saying that you can predict who might iterate and therefore actually do something about it is an aha. Now, Richard, if that’s an aha, you know where the world is. And so, we’re gonna deal with a lot of such ignorance that you have to overcome one small step at a time.
To not recognize that and to see that as a necessary cost rather than a delight factor and asking the question, what can they bring to the table in terms of bringing in the right people to begin with, is a wasted opportunity.
Richard Owen
Yeah, well.
Well, I mean, that’s a perfect place for us to end because you just gave an incredibly eloquent sales pitch for our business, perhaps inadvertently, but I’ll take it. Our reason for existence. I think that at the end of the day, I would start our conversations with any customer with asking the question, what would you do differently if you could predict the future of your customer?
And I argue that it would change just about every part of your operation. If you could look around corners, you would do radically different things. So that’s the question we should all be asking about the future. But as you said, it’s this fascinating generational shift. So, a lot more we could talk about. But we’ll provide in the show notes some links to your most recent publications as well because there’s a treasure trust that we could definitely get into.
Das Narayandas
Which is a shift. Exactly.
Richard Owen
That’s, as ever, unbelievably fun to talk about this. We could turn this into a one-hour conversation but want to be respectful of your time. Thank you so much. And keep pushing on this idea of getting Harvard students to start to use data. I think it might actually really happen one of these days. And it might catch on over there on the other side of the river.
Das Narayandas
Alright.
Hey, a lot of my colleagues are doing that, Richard. I mean, so I know there is a cross the river issue that you and I have to figure out in some time, somewhere we will meet halfway, but on a group and we will be there.
Richard Owen
We’ll settle it on the bridge. We’ll meet on the bridge. Thanks, Das. We really appreciate it..
ABOUT THE CX ICONOCLASTS
Das Narayandas is the Edsel Bryant Ford Professor of Business Administration at Harvard Business School. His academic credentials include a Bachelor of Technology degree in Engineering from the Indian Institute of Technology, Bombay (IITB), a Post-Graduate Diploma in Management from the Indian Institute of Management, Bangalore (IIMB), and a Ph.D. in Management from Purdue University. His research spans topics such as customer data analytics, leadership development, and professional service firms. Narayandas has authored influential works and continues to contribute to the field of business administration.
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Richard Owen is celebrated as a leading figure in the Customer Experience industry, primarily known for his contribution as CEO at Satmetrix, where he and his team, along with Fred Reichheld, developed the Net Promoter Score methodology, now the globally dominant approach to customer experience measurement. His efforts further extended to co-authoring “Answering the ultimate question” with Dr. Laura Brooks, establishing netpromoter.com, and initiating both the NPS Certification program and a successful conference series. Owen’s diverse 30-year career has seen him drive technology-led business transformations at Dell, lead software companies like AvantGo to a Nasdaq listing, and Satmetrix to acquisition by NICE Systems, while also engaging in venture investment and board roles. Today, he spearheads OCX Cognition, leveraging machine learning for real-time NPS and customer health analytics.
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