The Future of Competitive Strategy and the Evolving Role of Data, Customers and Digital Ecosystems with Mohan Subramaniam
Mohan Subramaniam is a Professor of Strategy and Digital Transformation at the IMD Business School in Lausanne, Switzerland. He is a recognized thought leader in digital strategy, focused on the digital transformation of incumbent industrial firms and helping them locate new sources of competitive advantage in the digital age.
His 2022 book, The Future of Competitive Strategy: Unleashing the Power of Data and Digital Ecosystems, is one of the most clear and compelling I have the topic. On the Outthinker podcast, he laid out specific ways in which the paradigm for strategy needs to evolve for business leaders in incumbent organizations to take advantage the data and digital ecosystems that will form the foundation of strategy in the digital era.
He discusses:
Kaihan Krippendorff: Mohan, thank you so much for being here with us. It's great to have you on the podcast finally. ?
Mohan Subramaniam: Thank you so much for inviting me. ?
Kaihan: So I want to open up with the same two questions I ask all of our guests. The first is, if you could complete this sentence for me, if you really know me, you know that...?
Mohan: I'm a huge connoisseur of Indian classical music, and I sing Bollywood songs and have given a lot of shows in Boston.?
Kaihan: No way, wow. When do you find time for that? ?
Mohan: I don't find time now, but there was a time in Boston I did find time, yeah.?
Kaihan: Oh, that's lovely. The second question I'd like to ask is if you could share with us what your definition of strategy is. ?
Mohan: For me it is about how to drive competitive advantage. ?
Kaihan: Interesting. Great. I think that leads directly to the first question I wanted to ask, which was competitive advantage as a term was not introduced until ‘79-’80 when Michael Porter coined and popularized the term. And yet much of your recent work and earlier work is sort of looking at some assumptions that the Porter model introduces. So if you don't mind just opening up with your thesis, what I found really fascinating, because here at Outthinker, we believe that strategy is really language that's introduced that helps us think differently and solve new problems. And you talk about certain assumptions that are maybe less relevant in this new basic competition.?
Mohan: Absolutely. And by the way, my hats off to Michael Porter. I mean, he gave a tremendous amount of clarity. Oh yeah. But, you know, times evolve and change like everything, you know, and I'm still saying that there's a tremendous amount of value in his frameworks and his thoughts.?
But this is how I see it. His thinking evolved in very clearly a different era, which was what we call the industrial era. And not surprisingly, we borrowed a lot of his understanding from what we call the industrial organization economics, which is essentially the fundamental assumption that competitive advantage is a function of the nature of the industry you are in. So I've got great regard to Michael Porter. His concepts and frameworks are still relevant, but one has to understand that they were created, conceived and developed in a very different era, which we now understand as an industrial era as compared to what we may call the modern era as a more digital era.?
I've got great regard to Michael Porter. His concepts and frameworks are still relevant, but one has to understand that they were created, conceived and developed in a very different era, which we now understand as an industrial era as compared to what we may call the modern era as a more digital era.?
But what do we mean by the industrial era? The nature of the industry image of firm competition had a huge part to play in terms of how it created competition. So in certain cases, the nature of the industry would be so bad that even the greatest of firms would not be able to do well, but in most other cases you could manipulate the forces in the industry and create an advantage and then protect that advantage. Now this thinking came from industrial organizational economics. You know, the broad understanding that some industries are monopolies and some industries are perfect competition.??
And for the economist, monopolies were bad, perfect competition was good. But Porter's genius was how he flipped that around and said, well, we are not talking about liberal monopolies, we are talking about monopoly power. And if you do certain things with the industry characteristics, you can actually create monopoly. Now connected to that understanding are two other very important assumptions, which was that all of our revenues come from products. And for us to drive both revenues and products, we need value chains, and then of course products and value chains together along with the nature of the industry and how you manipulate the forces of industry with how you drive competitive advantage.??
That is, in a sense, Porter's theory. I put it that way. Now, what has changed? A sense of Porter's thinking was drawn from industrial organization economics, where economists figure out what are the factors in an industry that were likely to create a monopoly and were likely to create perfect competition. That was the spectrum.
So Porter's genius and brilliance was to turn that around. And of course, the economists would say that monopolies are bad, perfect competition is bad from the consumer's point of view. But Porter's genius was to flip it around and say it would be home. You know, it doesn't make sense to be in a perfectly competitive industry. And it doesn't mean that we want to have a monopoly, the one from 100% revenue.?
You're talking about monopoly power, where get a town normal return. That is possible if you take some of those insights from industrial organization economics and see how you could marshal the forces in the industry to create competitive advantage. Right, So that was his primary thinking. But connected to that understanding were also two related assumptions which were kind of intuitively you understand them to be right now, but 100% of revenues came from products or services. And to deliver those products and services, you need value chains.
So the value chains became the engine for a firm's capabilities and products and markets were means by which you could manipulate industry forces. And that combination of all that was what we understood to be competitive strategy and competitive advantage. Now why is it less relevant today than what it was 40 years back? One of the prime reasons is that if you look at the most valuable companies in the world, 100% of their revenue comes from data, not from product.?]
Now why is [Porter's framework] less relevant today than what it was 40 years back? One of the prime reasons is that if you look at the most valuable companies in the world, 100% of their revenue comes from data, not from product.?
Kaihan: Like who, who are the most valuable companies that we can visualize?
Mohan: Facebook, Google,?Amazon. If you think of Facebook and Google, think about the value that they are creating. Google's value or Facebook's value comes from its digital advertising and not which is largely a play of data. It's not necessarily products. The platform is a vehicle by which you can use data for massive, massive advantage.
Now they are clearly able to do this because their business models are anchored at our digital platforms, which intensively allow them to use data as a revenue driver and breeder. Now for the vast majority of companies in our world today, they are the same companies that Porter talked about in the industrial age. They're still around, but they have lost that whole sheen. They're not as valuable as the Amazons and Facebook because their business models, the value chain product-driven business models are designed primarily to drive revenues from product, not from data. So the way we use data, by the way I call them legacy firms, these are firms that are based on legacy from way before the arrival of the internet.?
Now for these firms, their DNA is about value chains and products and data becomes a means to support products. If you really want to do something differently, I'm thinking why Porter's understanding gives us the foundation, but now we are thinking beyond that. It's to see how a legacy firm can use its strengths in its products and value chains to become vehicles for new kinds of data that can drive greater value. So you don't have to be restricted by your products. There is far more value possible with data.?
If you really want to do something differently, I'm thinking why Porter's understanding gives us the foundation, but now we are thinking beyond that. It's to see how a legacy firm can use its strengths in its products and value chains to become vehicles for new kinds of data that can drive greater value. So you don't have to be restricted by your products. There is far more value possible with data.?
However, then you want to understand competitive strategy differently. Which is, it's not just about industries, because industries can amplify the value of your product. Okay, if you are in a scale intensive industry, your product become more valuable. To amplify the value of data, you need digital ecosystems, which we understand from the Ubers and Amazons and Googles of the world. But what does it mean for a legacy firm??
My thesis in the book is essentially about that, which is to say I'm focused only on legacy firms. There were enough books that have written lots of good stuff about platforms. And for me, I said, okay, there's enough about platforms. What I'm interested in is what does a bicycle company do? What is a water pump company?
You know, what do these companies do to tap into that explosive power of data that these digital platforms have shown as possible? And how can they do it on the foundations of their existing business models of value chains and products. What should they do differently? That's what my book is about.
Kaihan: Love it. So looking at that challenge, one concept that really jumped out at me for your work was this idea of a digital customer, moving from customers to digital customers. And that seemed to be one of the first steps for a legacy company. Can you?explain that for us?
Mohan: So to understand that I need to explain one important concept of the book, which is: what is new about data. All the focus in 1980s is not that the firms did not have data. Data has always been around. What's new about data??
And I see the differences when I call it with sort of data and interactive data. A digital customer is one who gives you interactive data. So to explain digital customers, I need to explain episodic versus interactive. A good way to see the differences with this example. Say I go to Barnes and Noble, the old Barnes and Noble, you know, the legacy Barnes and Noble.
I spent two hours in Barnes and Noble and I buy one book. What is the data there? You compare that with I spent two hours on Amazon and buy nothing. What's the data they have? That is the essence of the difference between episodic and interactive data because the power of interactive data and the insights they give are fundamentally different from what the insights episodic data can give you.
If I go back to the episodic data, it meant to support that. For Barnes & Noble has a lot of data, okay, these books are selling, or this is the contribution margin, or these outlets are selling well, these outlets are not selling well. You note that you aggregate this data and then use, analyze it for after the fact. Amazon, that interactive data, which comes scaling in, that interactive data can be used in both ways. It can be used after the fact for aggregate analysis of profiles.?
But the difference is that that data comes from pinpointed sources. It comes from you. It comes from me. It's not an app. Even if it is after the fact, the insights are very different.
They are like individual profiles. But the other big difference is that they use it in real time to share it. So the time when I'm browsing, the time when I'm interacting, at that time, that data is being used for lots of different kinds of value. So stuck with episodic data, you miss out on a huge?amount of value that data can give you.
Kaihan: Can you give us an example of a physical product that is able to create continuous data and not episodic?
Mohan: Absolutely. Okay, so that is where the digital customer comes. So the digital customer is one who gives you interactive data. And how does a product do that or a legacy product do that? Through sensors. This is the simple answer.?So if you have a mattress, for example, the mattress is streaming in data on your heart rate and tossing and turning, and?it is much closer to the Amazon model of interactive data than the Barnes & Noble model of episodic data.
So the digital customer is one who gives you interactive data. And how does a product do that or a legacy product do that? Through sensors.
Remember, in the old world, it's not that the mattress company did not have data, but it was all episodic. I sold a mattress, or a component was shipped, or this is my inventory, or whatever. Here now I'm saying, okay, this is the quality of sleep at every second microsecond, right? That data is streaming in.?
Now that data is on top of the episodic data that you have. It's not that that data has disappeared or your value chains have disappeared or your products have disappeared. But now on that foundation you have the potential to create new kinds of data which?be used in different ways and that's where the digital ecosystems come in. To give you a quick example of how the mattress can create value from that. After a while when it has enough data from you and millions of other potential digital customers, its analytics can start predicting your health issues. It can tell you you're likely to get sleep apnea. It can tell you that you are having restless leg syndrome.
And based on that, it can tell you, you're likely to get a heart attack. Okay. Now, it can also shape your life by connecting that data in real time with external entities. You've gone to sleep. It knows that you are in deep sleep.
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It can shut off a television, which is on that you left on. It can shut off the lights that you left on. So there are two types of ecosystems that I get into, which look at value in very different ways, But at the core of it is your product, your digital customer who gives you interactive data, which you can create value in different ways.
Kaihan: So we start by turning customers into digital customers, which involves moving from episodic data to interactive data. And then we start playing in this ecosystem. But I also think it's interesting is that you lay out these two different types of ecosystems. Could you explain the distinction between those two??
Mohan: For me, you know, because my focus was on legacy forms, I start with our existing infrastructure. For most forms, they have a value chain, which is what Porter beautifully articulated. Now that value chain can be seen as a network. And by the way, as to digital, what are digital ecosystem? Digital ecosystems are networks of data generators and recipients.
Digital ecosystems are networks of data generators and recipients.
The most obvious way to understand that is, okay, Uber has a digital ecosystem of what? Drivers and riders and a whole bunch of app developers. They all generate data, they share data, all their data sharing is done in real time, but in platforms because of the intrinsic nature of the business model. Right. But now when you go to a legacy form, what does a digital ecosystem mean?
So you start with the value chain and see that as a network and say that, okay, how do you see this value chain as a data generating and receiving network? Now, if you create that network within your value chain, you're creating what I call a production ecosystem, which is the digital ecosystem in which goods are produced and sold. So this ecosystem started way back when IT services first came. But those moves were initially very clunky and, you know, there are lots of silos. So today with modern digital technologies with sensors and IOT?and AI, that production ecosystem can be made very rich and very powerful.
Kaihan: So just to help, I'm visualizing, for example, the automobile supply chain, which is very complex with thousands and thousands of parts developers and things, right? We're digitizing that.?
Mohan: Absolutely. And this is where the interactive data plays in. Now, we talked about interactive data from customers. Firms can also get interactive data from their assets. So with Ford, which you mentioned, I'm an auto company, it may have augmented reality and virtual reality in its paint shop.?So it is getting streaming data which replaces visual inspection with this and AI kicks in and so it dramatically improves its quality control efficiencies and so on so forth.
So interactive data can also come from assets but its main benefit becomes increased operational efficiency. But when interactive data comes from customers, and it is still used within the value chain in the production ecosystem, it can also drive new revenue generating services. The most common one is predictive services. So a Caterpillar is getting streaming data from a very complex mining equipment and it can, through analytics, tell you ahead of time that maybe this component will fail, maybe we can replace it at a time that reduces your downtime, and I'm now charging revenue, so I'm making some money out of it.
When interactive data comes from customers, and it is still used within the value chain in the production ecosystem, it can also drive new revenue generating services.
Coming back to the mattress, the ability to predict a heart attack is coming from data centers that are inside the value chain of the organization and it's part of the production ecosystem. The other part of the digital ecosystem, which is new, to me, it's the more exciting part, begins with understanding that every product typically has what we call complements.
The other part of the digital ecosystem, which is new, to me, it's the more exciting part, begins with understanding that every product typically has what we call complements.
So a car needs roads, gas stations, it needs independent service providers, a light bulb needs sockets and wiring and electricity. But typically a legacy firm in its traditional business model, especially in the porter’s world of business models, The value chain ended at the point at which you sold the product. You did some after-sales service, but you really...?No, auto companies are not concerned about whether you had roads or not. A light bulb company is not going to get into the business of giving you electricity and wiring and sockets. Those were left for you as a customer to manage. What has changed today is that because of sensors and IoT and because of interactive data, a company can connect a product user with external entities in real time.
What has changed today is that because of sensors and IoT and because of interactive data, a company can connect a product user with external entities in real time.
I'm going back to the mattress example. You are connected to a light bulb. You're connected to a television, which shuts off when it knows you're asleep, is an example of how what were originally unconnected complements, which you did not even think were complements. When you sold a mattress, you never thought about a television or a light bulb being connected with a mattress. Today they are getting connected. In the book, one of the examples is about an asthma inhaler.?
So if you are AstraZeneca and you have sold an inhaler, you are concerned with the medication and the patent and the brand and how the person actually used the inhaler in what context was not part of their business model. Today, the location data of a person being picked up by a sensor can be connected with external entities that tell you in this location, maybe you have some irritants that can create an asthmatic attack for you. That's the conceptual ecosystem. So when you look at this in totality, a legacy firm has a whole bunch of choices. E-state can say I'm going to use digital technologies just to improve my efficiencies on my interactive data as assets.
Or it can say, no, I want to get interactive data from my customers, but I still want to use it only internally. I'm not going to go outside in a consumption ecosystem. Or you can say, I want to go into the consumption ecosystem. By doing that, you're extending your value chain into a digital platform. Mattress is connecting you to a smart TV or a smart light bulb. It is behaving just like how Uber does, by connecting you to a driver or connecting you to an app. In?real time, only because of interactive data. You cannot do it with episodic data.
Kaihan: One comment, one connection I'm making, and then a question. We also had a woman named Stephanie Woerner from MIT. She's with CISR, the Center for Information Systems Research. She has a framework, it's a two by two. X-axis is operational advantages. Y-axis is like customer experience or something like that. I'm almost thinking that the X-axis in her framework is the production ecosystem, activating that. And then the y-axis is the consumption ecosystem.?And then she kind of says you could go one way or another way, you could take steps, kind of go back and forth between them.?
Mohan: I have great respect for her and her work, so I'm going to get me wrong, but where I differ here is this. The two by two, we call them the deadly two by two, which is that it's very seductive to explain a concept in simple ways, but many times it misses the intricacies and the nuances. In this case, your production ecosystems can do both. Like I said, it can also improve efficiencies and it can drive new revenue streams. Now the revenue streams come because of new customer experiences, obviously.?
If you are selling me a smart mattress and telling me, please pay me $20 a month for these services. I will pay those $20 only if I see some added experience. So for me, the focus I take is that a legacy firm should be looking at what new experiences it can give for new revenues. But these new revenues can come either from your production ecosystems or consumption ecosystems. And it starts giving you a more granular, shall I say, or a more refined understanding of your choices. And also tells you in some cases, maybe you cannot go into a consumption ecosystem. Every product cannot become a platform. Every product cannot drive new revenue streams through data. But at the same time you want to be conscious that either you're not leaving money on the table because you're not noticing these opportunities or you're not noticing new competitors who are coming in and who are doing things with data that you are not.
Kaihan: How do you know who's a competitor in this real world??
Mohan: In the Porterian, the industrial world, it was very simple. Who's a competitor? Those who offer similar products. Because you see, the entire focus was on products. Now, if you say I'm going to drive revenues from data, the competitors are also those who have access to similar data.?
So if I'm a light bulb, for instance, and I have a sensor that detects motion, it's an old technology, but now I'm using that data of saying that nobody's supposed to be at home, there is motion, I'll connect it to a smartphone or I'll connect it to some security system. Now, that is a consumption ecosystem play for the light bulb, right? It is connecting data to external entities. But the point here is that who else has that data? You know, it could be Nest, it could be Alexa, it could be furniture in the house, it could be any number of cameras, it could be photo frames, it could be anybody with a sensor who has access to the same data you have. So you have to be careful to see where are your new competitors going to come from.?
Kaihan: Yeah. So we're already taking more time than we agreed with you. And I have so many questions, but there are a couple I think are really important. When you think of your product outside of the value that it delivers, like a light bulb delivers light, but you also talk about a light bulb on a street with a sound sensor. Can you tell us that story??
Mohan: Yes, so this is what is key, which is when you think of value chains and products, you kind of define your business scope around that. What can my product do and what can my value chain do in terms of what products I can offer? That defies your business scope. But now when your products have sensors and you can innovate on the type of data that you can capture, then your business scope is not restricted to the product features and the value chains that deliver those product features. So the light bulb example is great.?
This was a startup in Boston that I used to know the CEO for, and he was working on this idea and had kind of actually started operationalizing it also that the sensor on the light bulb was based on sound. So on street lights, these light bulbs with sound sensors could detect a gunshot. The idea was that if it senses that within say a certain radius the lights would come on, cameras would come on, AI would kick in to know whether it is a serious thing and if it is then calls go to 911 ambulances and police and so on so forth. Now that's a consumption ecosystem player. Now what you notice that the scope of the light bulb is very different from the original idea about the product.
There's another example. So if you have an iRobot, you know Roomba for example, it always had a sensor. But the sensor was originally designed for the actual product functionality, which is that it is meant to clean, and if it bumps into a piece of furniture, it will move, it's all through sensors. But imagine that if they have sensors that detect mold or detect mouse droppings, it opens up a completely different consumption ecosystem to have a play. And you see like the app world, the risks of moving into those kinds of businesses are not the same as what it used to be in terms of mergers and acquisitions and all.?
So here opening up your APIs and saying, hey guys, are you interested in participating in this platform? I'll give you the data. It's up to you. This is theirs, not yours. So it allows you to grow and expand your business scope in very different ways.?
And it all boils down to saying that there was a product world at one time, and now we are using the product as an infrastructure and underpinning and a base to elevate ourselves into the data. And how to do that requires a lot of thinking. And that's what the book is about.?
Kaihan: Wow. Yeah, that just blows up the whole idea of I am a light bulb company. I am a vacuum company. Peter Drucker said strategy is the answer to the question of what business are you in? And this completely expands what business you can say that you're in.?
Peter Drucker said strategy is the answer to the question of what business are you in? And this completely expands what business you can say that you're in.?
I know we're over time, but there's just one important question that I really must ask, which is data. I want to know how you think companies should be thinking about data. We had the guest on that said data, people say it's the new oil, but it could be the new water. You talk about episodic data versus interactive data. Interactive data has a very short shelf life, so is it even about capturing and controlling data??How should companies be thinking about data?
Mohan: So strategically, I would say this. The big shift in thinking is that we always thought of data as something that supports our product. What I'm saying is that think of products as something that can support your data. If you make that shift, it opens up a lot of other things. The issue about whether data is oil or water is this, that data is oil was not because of what legacy forms did. Data is the new oil is because of what Facebook and Amazon did.
The big shift in thinking is that we always thought of data as something that supports our product. What I'm saying is that think of products as something that can support your data.
Then we have to be conscious that they could do it because their digital platforms are designed for that. They were designed to drive new revenues from data. Now we can't be an Amazon or a Google, but at the same time, we don't need to be restricted to what we were doing in our product world. And for that, we need to have frameworks to have the options of saying, you know, can I move into this new space? Can I expand my scope?
And I'll just add one more point to this, that is, what does data mean today? Is that it is changing of a concept of commoditization. In the product world, what was commoditization? A competitor is coming with a product of lesser quality and at a lesser cost, and is squeezing me because I am a product of higher quality and I can't match it. I'm getting commoditized.??
In today's world, it's somebody with data that is adding new data-driven features to products that will make a pure product play commoditized. It's not about an inferior product quality. It may be, but it is able to use that product base to drive new experiences and features. And in that world, your pure product play becomes commoditized. That is what I would say is different about data and how you should think about data.
In today's world, [a competitor] somebody with data that is adding new data-driven features to products that will make a pure product play commoditized. It's not about an inferior product quality. It may be, but it is able to use that product base to drive new experiences and features.
Kaihan: Wow, okay. There's so?many implications to this and you've been generous to spend more time than we booked with you. I think it's because your work is so profound and the implications are so significant. I highly recommend that people read your book because you go meticulously through the phases of transformation.
Thank you for being here. Thanks for the work that you do.?Thanks for sharing with us. Thank you.
Mohan: Thank?you so much for inviting, and I really enjoyed the conversation.?
Kaihan: Thank you to our guests. Thank you to our producers, Karina Reyes and Zach Ness, our editor and the rest of the team. If you like what you heard, please follow, download and subscribe. I'm your host, Kaihan Krippendorff. Thank you for listening. We'll catch you next with another episode of Outthinkers.?
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