Academic publishers, along with all those involved in scholarly communications, are experiencing unprecedented change. This critical moment will have long-lasting effects, with the disruption creating threats and opportunities. This post proposes three directions publishers should consider, if they are to thrive in the new reality…
A revolutionary moment
Anyone claiming they can accurately predict the future is either a time traveller or a liar.
However, there is ample evidence emerging that the Covid-19 pandemic will cause a severe economic downturn, if not a global Depression, placing traditional business models under unparalleled pressure. Certainties now appear uncertain. It seems reasonable to predict that things will not return to the way they were before.
Often referred to as an ‘ecosystem’, the scholarly communications industry includes authors, publishers, Higher Education Institutions (HEIs), libraries, businesses, laboratories, funding bodies, governments, and more. The system is a complex, interconnected web. Budget cuts in Higher Education due to enrolment deferrals will reduce University Press subsidies. Academics will lose jobs – most likely, departments will close, if not entire universities. Library budgets will be cut, which will in turn hit publishers’ bottom lines. What affects one, affects all.
In these extraordinary circumstances, the question for leaders in academic publishing becomes: how best to respond?
Immersive Virtual Spaces
When shaping transformation journeys with companies of all sizes and sectors, the best approach we’ve found is to anchor the discussion with our Business Evolution Map.
Academic Publishing sits on the northwestern region of ‘Media Publishing’, bordering ‘Tertiary Education’, due to the overlap in capabilities between the workforces in those two sectors
Take a look at the map below. The most relevant border for our purposes is between Academic Publishing and the Tertiary Education sector. Universities have had their own publishing arms since 1534, and publishers have recently been opening up their own Universities. Indeed, in 2019 Pearson moved even further ‘west’ into the Secondary Education sector by co-launching an online Sixth Form College with Harrow School.
With the HE Sector being seriously disrupted, a publisher attempting a move into Tertiary Education would be like jumping from the frying pan into the fire. So what other directions does the map tell us Publishing should explore? Over the past few years, there has been a breed of CTO in Publishing who have insisted that the path to diversification is to transition into Software Development. According to the map, this is as unlikely as a University Press opening and running a successful retirement home. However, according to the map there is an intriguing journey open to publishers towards ‘Interactive & Immersive Media’.
Even before the Covid-19 pandemic, the idea of thousands of academics flying to briefly attend a conference in person was being challenged by the urgent needs of climate change. Due to budget cuts at Universities, coupled with ongoing public health concerns, all conference travel has been suspended. In this context, a question emerges:
How can Academic Publishing contribute to the effective dissemination of scholarly information in the absence of physical participation?
And more specifically:
What role can publishers play in creating immersive community events, occupying virtual spaces?
In a remote-first Higher Education model, what can publishers do to optimise teaching and learning outcomes?
With regards to ‘immersive virtual spaces’, those in Academic Publishing are well placed to claim this emerging problem, and open up a lucrative new revenue stream. At first glance, this may feel like a stretch, until you consider that within the cluster of companies that sit alongside Taylor & Francis under the Informa umbrella, there are several Events & Exhibitions businesses. And in the pre-Covid world, universities used their facilities to host a variety of events throughout the year. Virtual conferences present a valuable opportunity to those willing and able to pursue it.
The same applies to helping ensure pedagogically sound online teaching and learning in the Covid-19 era. For centuries, Higher Education has involved the student and teacher in the same room, referring to physical reading material, making notes with ink on paper. In recent decades, some of this changed, with the introduction of digital equivalents of their analogue predecessors (an eBook instead of a weighty tome in the student’s bag; a laptop instead of a notepad). With the rapid transition to online learning in Higher Education (it even has its own abbreviation now), publishers can and should rethink their role entirely. This line of thought quickly becomes radical: what if the very best way to help students achieve their learning goals is not another book, but a series of podcasts? A suite of interactive masterclasses? A virtual tour of an engineering plant?
In the thorny (if lucrative) textbook market, you see great inventiveness from publishers, exemplified by the publication of new editions of best-selling textbooks in shorter and shorter cycles. This strategy intentionally renders previous versions obsolete, and is intended to combat morbid symptoms such as piracy, leakage and the rampant second-hand market. Such tactics are decreasingly effective, exposing the shortcomings of the textbook business model, and leading to acts of desperation, such as failed mergers between large textbook houses. A rethink is required if better outcomes are to be achieved.
One potential solution is to ‘zig when others zag’, by borrowing an idea from Gaming (another part of the ‘Media Publishing’ sector on the map, above) – namely, the concept of ‘Unfolding’. In essence, this allows the publisher to make post-publication updates and enhancements, extending (rather than deliberately shortening) the life of the work. Small updates would be fed through automatically and for free (including errata, and other small changes to help keep the experience fresh and interesting). More significant changes (e.g. substantial new scholarship, new online supporting materials) would be available for purchase at a marginal cost.
Nintendo’s Animal Crossing is the world’s biggest selling game of 2020 – despite only releasing two games in the last 12 years. The secret to how they built up such a huge following is ‘unfolding’
With the perpetual, fixed digital equivalent of an analogue artefact showing signs of strain – for example, the growing need for asynchronous updates of different chapters within a collected work – ‘Unfolding’ is an intriguing concept. As elsewhere, it should prove effective in dislocating content updates from the traditional, print-based new edition cycle, thereby reducing costs and opening up new revenue streams.
In this scenario, the frequency of new editions, which has been ramping up over the past decade, would drop, and purchasers would instead buy a dynamic digital copy of the book with an extended lifespan. The risks inherent in producing expensive new textbooks every two-three years would be radically reduced, margins would increase whilst previously escalating prices would fall. The strain on authors, reviewers and publishing staff would ease, and most importantly, the learning experience would improve.
Starting with first principles
Before proceeding any further, let’s agree on the following:
All those who belong to the scholarly communication ecosystem are actively working to ensure that as many different people as possible can access the right content, at the right time, in the right way.
From there, we can agree on further principles – such as, any behaviour that deliberately frustrates the dissemination of knowledge is bad. Everyone involved in the industry would agree that a library cancelling a subscription for budget reasons means we are failing in our collective mission.
A castle besieged
In his classic novel/memoir Austerlitz, W.G. Sebald examines (amongst many things) the history of fortification. Sebald elegantly analyses how the architectural principles of castles were developed in response to the technology and tactics of those trying to destroy them. Ultimately, the most heavily fortified castles became vulnerable the moment attackers could fly overhead and drop bombs from above. “Somehow we know by instinct,” he writes, “that outsize buildings cast the shadow of their own destruction before them.”
Returning to the matter in hand, proprietary publisher platforms have begun to resemble besieged castles. Outside the walls are camped such forces as SciHub, the Open Access (OA) movement, and international library consortia embarking on increasingly vexatious negotiations with content providers. The question is – could publishers hunkered down within their castles be spending their time and money better by doing something other than building, maintaining and defending ever higher and thicker (pay)walls?
Breaking down old boundaries
In the same way that we saw how the differences between the physical and virtual worlds are being dissolved by such concepts as immersive digital spaces, so other boundaries are being challenged. Three of the most important are as follows:
- Academic disciplines: In his recent article for the Scholarly Kitchen, Simon Holt spoke persuasively of ‘interdisciplinarity [becoming] the new norm’, with HSS and STEM disciplines mixing more easily than ever
- Publishing imprints: During the typical discovery use case, readers rarely care about which publisher publishes what
- In front of or behind the paywall: The OA movement is disrupting the traditional business model of valuable scholarly content existing behind a paywall, with legitimate access impossible without the individual or institution paying for it
On the last point, it was encouraging to see publishers unilaterally drop paywalls to allow access to Covid-19 related content. Yet as a rule only STEM-related content was opened up, indicating a failure to comprehend the opportunity at hand. As Bernice Housman, from the Department of Humanities at Penn State College of Medicine, recently said:
Science-based medicine is a tremendous advantage that we have in the modern world. The fact that we could sequence the genome of the coronavirus so quickly; the fact that we’re talking about a vaccine within 18 months or two years is phenomenal – all that is based on advancements in medicine. But the experience of the pandemic, the social disruption it has caused, the difficulties of discrimination and unequal treatment that the pandemic has uncovered – all that is the realm of the social world, and all of that is much more difficult to handle.
Set your content free
Pandemic or not, there is no bigger priority for publishers in the digital age than the optimisation of discovery. Therefore, instead of trying to ensure content is protected from all angles by a high and thick paywall, this issue can and should be turned on its head.
The single most transformative action that academic publishers could take would be to destroy paywalls forever, now. If publishers are to remain sustainable businesses, this may well be too radical a move. But there is now, and there is the future. In software, the ‘open source’ movement was once considered a fundamental attack on the bottom line, aimed at destroying the big software producers. Instead, open source software (and the technology companies that use it) has risen to rule the business world.
So what are the most constructive and effective options available to publishers when it comes to improving the discovery journey? One would be a more ‘porous’ paywall, which would grant non-subscribing individuals and institutions time-limited access to the content they need for a marginal cost. This would generate positive PR; broaden participation (from scholars in the developing world, from independent researchers, from smaller schools and businesses, etc); and create new revenue streams.
Another idea would be to explore how access could be indicated earlier in the research journey. In other words, the researcher should always be able to see, on the search results page, the content to which they have access.
It is a truth universally acknowledged that researchers neither start searching on, nor limit their subsequent search to, a single publisher’s platform. Instead, they cast their net wide, engaging with intermediaries such as Google Scholar, who help them navigate the entire research landscape. The problem is, the researcher still only knows if they have access to a piece of content when they hit a paywall, and are either let in or turned away.
As Olly Cooper, CEO of Researcher says: “Connecting scientists and researchers with relevant, impactful content to which they have legitimate access is still harder than it should be. The cost is an enormous number of missed opportunities for research outcomes to be inspired and progressed. At a time when the world is looking to the academic world to help solve our healthcare, economic and societal challenges, this feels like a high price to pay. Initiatives like GetFTR are a great start – but publishers need to achieve universal coverage if they are to properly untangle this problem.”
Attempts have been made in the past. Google Scholar were, a few years ago, exploring the concept of “Search Once, Read Always”, or “SORA”. Due to a lack of appetite from the major publishers, the idea never came to fruition. With hindsight, this was a missed opportunity, and should be revisited. Google and other aggregators and intermediaries are an increasingly important part of the scholarly communications ecosystem. Publishers should be investing in closer working relationships with them, to improve the discovery journey. There is nothing they could do that would have a more positive and lasting impact.
What business are you in?
In the digital age, many companies, across a variety of business sectors, have made it a priority to become ‘data-driven’. This is easy enough to say, and extremely difficult to achieve.
The costs of hiring and keeping highly skilled data scientists would make most publishers blanch, let alone the myriad complexities and high costs of creating and maintaining a data lake in the cloud. That said, those publishers who are able to demonstrate the highest levels of transparency and accountability with regards to open data have a significant, positive role to play in the dissemination of knowledge – and an abiding source of competitive advantage.
Buy, Build, Borrow
It should come as a relief to publishers to admit that their traditional, core skills (and wage structures) do not lend themselves to building a comprehensive data capability. The problem of hiring and keeping the required skills, especially during these uncertain times, isn’t one for publishers to claim. Instead, they should conserve their energy (and budgets) and instead consider a simple truth: good data provides quick and reliable answers to the most important questions. In other words, hire and keep those people who have the right mindset, and buy or borrow the rest.
This post started by posing the question: how should publishers best respond to this unprecedented moment?
The current economic, social and cultural crisis is accelerating conversations about how businesses can survive and thrive in this new reality. Faced with a determined OA movement, transformative agreements, library budget cuts, significant student enrolment deferrals, increasingly sophisticated piracy, and a highly organised second-hand market, publishers should resist the conclusion that the answer is to build higher and thicker paywalls.
Instead, the answers lie in:
- Sustaining growth through diversification into adjacent spaces, such as virtual and immersive community events;
- Making bold decisions to ensure the discovery of, and access to, academic content is as simple as possible;
- Being evidence-led, and using data to inform decisions large and small, without overinvesting in expensive data science capabilities that could easily be bought or borrowed.
Never has it been more urgent and important for rigorous, well-evidenced, peer-reviewed scholarly thinking to be disseminated globally, in order to inform critical decisions about tomorrow. Making the right calls today will define how we best achieve those crucial aims.
Interested in discussing these ideas further? Do you have something to add, something with which you strongly agree, and/or a contrary position you wish to argue? In any and all cases, it would be great to hear from you. Start the conversation by commenting on the article in LinkedIn, or alternatively drop me a line at firstname.lastname@example.org
With the global pandemic of 2020 and the depression that followed, came the realisation that our economic system was hugely vulnerable in the face of disruptive events. Companies inevitably rushed to automation more than ever before, and the emerging AI & Robotics business sector played a pivotal role in this transition.
Why the past tense?
Because, regardless of whether it’s right or wrong, this is what will happen. This blog post does not look at whether Robotics is the future, but who is best placed to succeed. As a new economy emerges from the other side of this pandemic, businesses will be forced to question the previously unquestioned: are global supply chains optimal? Is ‘Just in Time’ manufacturing robust enough to survive future shocks? Do we need offices any more?
As we adapt to lockdown, industry is struggling to cope with the sudden absence of people from essential processes, and automation is forefront in their minds. Previously, many saw automation as a way to remove human fallibility from well defined, repetitive processes in order to improve quality and productivity. Now it will focus much more on removing people completely from the process in order to remove a point of failure.
Automation of production lines has long been a thing, and although people play an important part in these lines, they are treated more as a cost/benefit equation than as the actual human beings they are. If (and it is a big if) we see a new economy emerging over the coming months – one that incorporates elements such as a more than minimum living wage or a universal basic income – then that cost/benefit equation will swing even more towards automation, adding to the sense of vulnerability businesses are now feeling.
Given the extent of the damage caused to businesses by the lockdown, companies will now look for solutions throughout the supply chain, not just in the factories. They will accelerate the development and introduction of self-driving vehicles; they will roll-out Amazon Go – style self-service retail stores; they will copy Ocado and Amazon and replace people with robots in their warehouses.
This is a dangerous strategy, as it represents a swing of the pendulum to another extreme, and as discussed in Part 3, specialisation leads to fragility in the face of disruption. Imagine, if you will, what happens to this automated world in the face of a virus of the electronic variety.
Back to the Map
Rightly, or wrongly, it will happen, and so the more important question relates to which industries are best placed to capitalise on this trend. And so we return to the Map, and another “sea” waiting to become a landmass (business sector) in its own right. We’ve labelled it AI & Robotics.
As you can see, the Map shows that the neighbouring territories are Computer Consultants, Software Development, Electronics, Electrical & Mechanical Manufacturing, and Motor Vehicle Manufacturing. Companies in each of these sectors are well placed to enter the field of robotics, and some are already doing so. In each case, the entry point is different and so it’s worth looking at a couple of them in more detail by way of explanation.
Step forward Dr. Susan Calvin
In Isaac Asimov’s body of work, there are a significant number of stories that centre around intelligent machines. Many of these stories feature Dr. Susan Calvin, who Asimov refers to as a robopsychologist working at US Robots and Mechanical Men, Inc. He postulates that this profession would be a combination of advanced mathematics and traditional psychology, but in reality the need is more likely to revolve around the training, utilisation and integration of Robots into the business world.
This is a reasonable role for companies operating in the Computer Consultants business sector to take on and thus start the migration into the AI & Robotics business sector. Following the disruption caused by the COVID-19 outbreak, and the resulting demand for greater automation, there is a clear opportunity for these businesses to promote their skills in this area and start the journey.
The first areas of greatest demand are likely to be in the production line, and in the warehouse element of the supply chain, where “dumb” robots already play a major role. There will now be a push to further automate the more complex activities currently undertaken by people, and this will lead to a demand for consultants with experience in introducing technology to organisations. Computer Consultants are ideally placed to benefit from this demand, especially if they include software development capability in their offering or partner with companies that do.
Management consultants are less well equipped to help as the level of technical expertise required to understand the art of the possible, and design solutions is far outside their skill set. They will, of course, have a go, but the Map confirms that they are not well placed to enter this sector.
Here in my car
The other area of the supply chain that will, no doubt see renewed demand for automation is that of transportation. This will accelerate development of self-driving technologies coupled with increased pressure from business to make changes to the road transport system to make introduction of such technologies less challenging. We can expect to see proposals for “freight only” lanes, and dedicated telemetry systems to lower some of the barriers to entry.
The companies best placed to occupy this part of the robotics landscape are the Motor Vehicle Manufacturers. Much work has already been done into self-driving vehicles, but most of the focus has been on cars. It is likely that attention will now move onto the larger freight vehicles. Despite their size, these vehicles actually present an easier route into this sector as they generally follow more predictable routes, and travel between a smaller set of end points.
Transportation companies such as Uber have also tried to make inroads into this area, but the Map predicts a less successful outcome for them, as they are a significant distance from the new area of AI & Robotics. Remember, on the Map proximity indicates similarity of skills and mindsets – companies located in other areas take much longer to develop the required attributes than organisations on the immediate borders. Uber are making the classic mistake of assuming that being a consumer or seller of a product somehow positions you to become a producer in your own right.
Stuck in the middle
So, that covers the types of business that will benefit from the inevitable demand for automation, but what about the demand itself? At the start of this post, (and in previous posts) we’ve discussed the dangers of specialisation and the increased resilience that comes with diversification. It is for this reason that a headlong rush to “automate all the things” could create as many problems as it might solve. It would also lead to an unmanageable portfolio of change that could cripple an organisation during what will inevitably be an extended recession.
One of the more difficult decisions for most companies is where technologies such as AI and machine learning can and should be effectively deployed. There is much talk of AI as the answer to everything, but there are places where it is most appropriate and places where it is less useful. There is also the confusing matter of machine learning algorithms versus “true” AI in the form of neural networks. The same question arises – which to use and where.
The problem is complex, but as a starting point here is a simple 2×2 grid (because we all love a 2×2 grid):
The horizontal axis represents the sophistication of the problem being solved ranging from highly complex (multiple variables and multiple outcomes), and the vertical axis represents the nature of the decision to be made ranging from fully objective (where there is little or no doubt) to highly subjective (where the outcome is open to interpretation and opinion).
For highly complex problems involving a significant amount of subjective judgement, people are by far the best suited to this type of activity. At the other extreme, simple problems with highly objective outcomes can easily be automated using traditional and well understood hard coded solutions.
As we remove subjectivity from a problem best suited to people, or add complexity to a problem currently solved using traditional code, machine learning algorithms come into their own. These are complex, knowledge based solutions that take broad sets of inputs to make a decision in a predictable and traceable way. The automation of the NHS 111 service is a good example of a problem well suited to machine learning.
Heading in the other direction, if we can take some complexity out of the decisions currently made by people, or there are simple problems that were previously not automatable using traditional coding techniques due to the desired level of subjectivity, we now have AI as a solution. Familiar examples involve identifying the subject matter of documents, interpreting medical scans or identifying people or behaviours in CCTV footage.
The same grid can be applied to physical robotics. In the bottom left square we have the type of machines we’re all familiar with on car assembly lines. In the bottom right, (Complex/Objective) space we have the potential for automating surgical procedures. In the top left AI opens the door for semi-autonomous machines such as exploration vehicles. Self-driving cars sit on the boundary between the top left and right squares, and this is why the problem has proved so difficult to crack. Deliberate simplification of the problem by altering the highway environment (or reducing the scope as described for freight vehicles) could accelerate the introduction of such vehicles faster than advancements in the current level of AI might achieve.
And let’s not forget that automation does not have to mean less people; far from it. History has shown that as machines take over in one area of human endeavour, this opens up areas previously ignored. If social distancing has taught us anything, it has told us that personal contact is essential to our wellbeing and to the success of our businesses. Instead of replacing people with robots, think instead of using technology to do the mechanical things, and free up people to be more human.
And so that brings to a close our quick visit to the new landmass that is the AI and Robotics business sector. In part 5, we’ll look more broadly at the Map and how things might unfold as we move out of lockdown and into a time of financial uncertainty. We’ll look at the challenges, but more importantly we’ll seek out potential green shoots and identify where they could emerge.
The real answer to unpredictable change, if you have the scale to achieve it, is to maintain diversity of offering, and build flexibility into your processes. Diversity means that when change comes, if you lose in one area, you gain in another. Flexibility allows you to adapt quickly to change to capitalise on the gains and minimise the losses.
Survival of the fittest
In part 1 of this series we introduced the Business Evolution Map and explained its origins, and in part 2 we used the map to chart the Nintendo journey, showing how the Map can be used to explain (and therefore predict) success and failure. But that is the past, and it’s the future we’re really interested in when shaping business strategy.
Specialisation can be powerful during times of stability. The hummingbird, for example, has evolved to feed on nectar from plants that only a few species can access. In return, certain plants have evolved to rely specifically on hummingbirds for pollination. This codependency works brilliantly, as long as the environment remains stable, but if you remove one partner from this equation, the other will struggle to survive.
The future is difficult to predict, and the only reliable way to survive the unknown is to remain diverse and flexible. Nature achieves this through evolution for slow change (the hummingbird) and variety for rapid change. The hummingbird may not survive the loss of one food source, but the diverse population of birds as a whole will barely be affected.
Before the COVID-19 outbreak, Primark was dominating on the high street, but without an online offering they are like the hummingbird without the flower. Overnight, specialisation has turned from a strength to a weakness. Similarly, companies with a predominantly online presence and high degree of automation will have suffered less, and may even have benefitted from the impacts of lockdown. Common Thread Collective provide some interesting raw data on their COVID-19 eCommerce update page.
No doubt, we will see companies rushing even more to be “digital first” and “fully automated”. (In part 4 we will discuss how firms able to exploit the emerging Robotics business sector will benefit from this change). COVID-19 has taught many companies a lesson here, but the lesson is NOT digitalise and automate everything. The lesson is not about specialisation.
There but for the grace of God
Those who embraced digital as an integral part of their offering were certainly right to do so. In fact, if supermarkets had invested more in online and home delivery capability the impacts of lockdown on our daily lives might have been significantly reduced. Instead, we are seeing the system failing to meet the new and unexpected level of demand.
However, if fate’s dice had rolled the other way, things would have been very different. We can expect to see a lot of survivor bias exhibited in articles over the next few months, coupled with a renewed effort to get rid of “bricks and mortar” channels. Those who didn’t go “digital first” will be referred to as dinosaurs.
However, the next change is unlikely to be another once-in-a-century pandemic. The clue is, after all, in the name. Imagine if, instead of a physical virus, the next disruptive change came in the form of a virus of the electronic kind. This time the heavily automated, all-online businesses that would be the ones most affected. High street companies with large human workforces and manual processes would be the survivors in this scenario. Primark might have become even more dominant, and the survivor bias would look very different. The narrative would be less about dinosaurs and more about “look before you leap”.
The real answer to unpredictable change, if you have the scale to achieve it, is to maintain diversity of offering, and build flexibility into your processes. Diversity means that when change comes, if you lose in one area, you gain in another. Flexibility allows you to adapt quickly to change to capitalise on the gains and minimise the losses.
We’ve already covered flexibility in a previous series of blog posts that describe our Continuous Evolution framework. Diversification is what we’re going to cover next.
Luckily, identifying successful diversification is the core purpose of the Map. In Part 2, we used Nintendo as an example, and described a successful early partnership with Disney. If we compare their performances in the face of the current disruption, we see an interesting trend.
Looking first at Disney, we see a not surprising drop in share value as COVID-19 (commonly referred to as coronavirus) impacts economies across the globe. When we look at Nintendo, we see the same drop, but this is followed swiftly by a recovery back to its original position. Disney’s predicament is understandable; even when we take its broad range of assets into account, its business model has a strong (one could say pivotal) dependency on physical presence. Nintendo is not similarly encumbered, but more importantly the rise coincides with the release of the game “Animal Crossing, New Horizons”.
If you haven’t seen it, suffice to say the game allows multiple players to interact and socialise as cute animals on an idyllic island. It is immersive, social, and stress free – the antithesis of present circumstances. In his review in The Telegraph, Jack Rear described New Horizons as “the perfect DIY recipe for the most chilled out, relaxing, and engaging life simulator ever.” The Atlantic also has a good piece about the game by Ian Bogost, entitled The Quiet Revolution of Animal Crossing.
What Nintendo has managed to do, in a very timely manner, is tap into an emerging business sector. A sector predicted by the Map in the form of an “inland sea”.
On spotting this feature of the Map, we tweeted about it, first in October of last year, and then in November:
It was clear from the Map that there was untapped revenue sandwiched between well established business sectors. A clear opportunity awaiting forward thinking companies currently resident in any of those neighbouring sectors.
Entry into this sector has also been eased, rather than hampered, by current events. A far greater number of people have been forced to become “digitally capable”, and opportunities to interact virtually are being sought as a replacement for face-to-face interaction. People may have to be physically distant, but they are looking for ways to remain socially close. As indicated by the map, offerings in this space are limited. This is what an inland sea represents.
Nintendo has managed to take a step into this territory by capitalizing on its games development capability (Software Development). With sufficient foresight (i.e. access to the Map), DIsney could have protected themselves, in part, via a similar move. Exploring ways to offer immersive but virtual experiences, in addition to the very physical experiences that they specialise in.
It’s reasonable to assume that these might have had low adoption rates, and been of low benefit initially in terms of revenue, but having this capability would have made their offering more adaptable to unexpected circumstances. It’s also possible that such an offering, far from detracting from their core business, could have created an adoption path into the physical disney experience. A “try before you buy” option for people who otherwise might not be willing to commit the time and money to fly their family across the Atlantic to visit Disneyland in Orlando.
Interestingly, Disney consider themselves to be early adopters of virtual experiences; ahead of the game. In some ways this is true, but only within their existing sector. Rather than creating virtual experiences, they have incorporated VR into their physical experiences. Although it might at first appear to be diversification, it actually represents even greater specialisation.
And so, we return to Nintendo and the successful launch of Animal Crossing New Horizons. Where one company finds success, others will inevitably follow. Some will just see it as another game, and miss the significance. Others will recognise the situation and attempt to open up in this sector from elsewhere on the map – we predict slow progress and probably failure.
Those moving in from the neighbouring sectors will have a high chance of success. There are already signs. Amongst recent acquisitions, Apple has bought Voysis (a digital voice assistant) and NextVR (a combination of virtual reality and live events), both useful capabilities when entering the world of immersive experiences. What is more, they have the Interaction and Immersive Media sector firmly surrounded and should find it easy and profitable to make this move.
Another neighbour is Facebook who is in the process of launching “Facebook Horizon” on its VR platforms. Facebook Horizon is a virtual reality universe that allows you to build your own environments and games. You can then play and socialize with friends or explore landscapes created by other users. This has, of course, been attempted before, but as with all new ventures, the time has to be right.
To assume Interactive and Immersive Media is all about virtual reality headsets would be a huge failure of imagination. VR is only a small corner of this business sector. We’ve considered how organisations like the BBC might revive the concept of “event TV”. People are already attempting to recreate the group viewing experience online, but it takes real effort. An organisation in the Media Broadcasting space would be well placed to enter the interactive arena by creating a truly joined up virtual experience.
Companies have dabbled with the influencer market, but again, no one has really capitalised on this opportunity. Companies in the Hosting & Search or Advertising & Marketing Sectors could partner with a software development company in a platform play around this space.
Finally, social media platforms are certainly where people interact virtually, but the key purpose for those interactions are to share opinions, garner likes or enter into divisive arguments. They may be called “social” but they often are far from it. There is a clear place in the Immersive and Interactive Media sector for a platform that allows people to gather in a more positive way. To share in a social experience as we might at a club or event.
The list goes on; far too many to mention here. It is, after all, a whole new business sector potentially worth many billions of pounds in the UK alone (the Map indicates a potential initial value of £13bn).
And so, in summary, as you consider what to do next, remember these three points:
- Specialisation leaves you exposed during times of change (the hummingbird)
- Diversity coupled with flexibility are the best traits for survival (Nintendo vs Disney)
- Knowing where and how to diversify is paramount for success (the Map)
Interactive and Immersive Media is an interesting route for diversification but if that’s not your area of the Map, then it’s not the right option for you. That’s why we intend to cover the other “continents” in this series. In part 4, we’ll explore another emerging business sector predicted by the Map – Robotics. We will show where it is positioned relative to other business sectors and who is best placed to exploit it.
The best way to understand something is by example, so to explain the Map we’d like to share a journey with you; a journey that explores diverse regions of the Map, charting where troubles were encountered, but ultimately great success was found.
Play your cards right
As our first foray into the use of the Map to chart organisational exploration, let’s talk about a company that’s been on a long and interesting transformation journey – Nintendo. Founded in 1889, the company began by selling handmade playing cards used for gambling (their official history skirts carefully around this issue).
We can therefore place Nintendo’s origins firmly in the Home and Office region of the Map, which includes toy making. The fact that the cards could be used for gambling does not place Nintendo in that sector – it is the users of the cards who operate in that space. This is an important rule when using the map. The trade of those who consume a company’s products or services should not be conflated with that of the producer.
In 1959, some 70 years later, after Japan was opened up to the world following the end of the Second World War, Nintendo struck a licensing deal with Disney. This allowed them to include some of the latter’s characters on playing cards. Nintendo were very much playing to their strengths, staying within the same sector and leveraging a partnership with a different type of business to increase their own dominance in their area of expertise. The move was a game-changer, and the subsequent boost in revenue allowed the company to go public three years later.
Exploring uncharted territory
During this time, ownership of the company passed down through various sons, cousins and nephews, and at the time of flotation was in the hands of a rather dissolute young fellow. With all this new money burning a hole in his pocket, he decided to ‘transform’ Nintendo.
He invested in taxis, instant rice, vacuum cleaners and even – dare I say it – love hotels. If we look at these journeys on the Map, each one involves a foray into an disconnected region. This is a situation for which our hypothesis predicts failure, unless treated as a genuine start up and given time to mature and grow (of the order of ten years or more). Despite significant resources at his disposal, the new owner failed to make a success of each of these ventures.
Feeling the way, step-by-step
Only one idea stuck – making toys, born out of the company’s knowledge and expertise in making card games. As electronic games turned out to have the highest margin, the 1960s and 1970s saw Nintendo experiment in this field, with many hits and misses. We examined these individually, and found that the progress of successes across the map was a logical one. The first real success came in the form of a mechanical arm known as the “Ultra Hand” which took Nintendo from Home & Office into the neighbouring territory, Mechanical & Electrical.
Another of the hits was a “light gun”, and in 1972 the first video game console was released (the Magnavox Odyssey), with Nintendo’s gun as an accessory for a shooting game. This established Nintendo in Electrical & Mechanical and started their move into Electronics as they became the exclusive supplier of the console in Japan, allowing them to build up their understanding of that market through partnership.
From the mid-70s to 1981, Nintendo used their knowledge of (a) video game consoles, and (b) the dynamics of card games to start making their own video games, culminating in Donkey Kong. At the same time, they launched their own gaming device – the “Game & Watch”; this handheld console has an impressive legacy that can be traced via the Gameboy to the 3DS. With the company now well established in Electronics, the move into Software Development was strategically sound. The highlights of the journey look like this:
Familiar ground in tough times
In 1983 came the great video game crash, which saw the US video game market shrink almost overnight by some 97%. To maximise short-term revenues, Atari dropped their standards and aimed for quantity over quality by allowing bad games onto their console. This culminated in the disaster of the ET movie tie-in game.
Nintendo watched the debacle with interest, learned from the mistakes of others, and as a result, applied a strong focus on quality. Only the very best games would get their seal of approval from now on. The company, in hard times, had shifted focus back to their foundation in games. This allowed them to succeed where other companies with a predominantly electronics background were less well equipped.
In 1985, nearly 100 years into their journey, Nintendo launched the Nintendo Entertainment System, or NES. Again, you can trace a direct line from that through the SNES to the Wii and now the Switch (which also relates to Nintendo’s first console in 1981, by being designed to be portable).
A journey of some 131 years, and where actually have Nintendo gone in that time? The journey from card games to video games is not a very long or complicated one – indeed, it’s an easily understood evolution, with failures learned from, and successes pursued.
In part 3, we will look more broadly at the territory Nintendo shares with many others, and demonstrate how they’ve managed to keep their share price rising whilst others in the same area are suffering. In doing so we’ll introduce an interesting bonus feature of the Map – the “inland seas” that predict emerging industry sectors, and show who might be best placed to move into them.
This is the story behind the Business Evolution Map, a unique tool developed by the Equal Experts Strategic Advisory Practice to bring predictability to the apparently unpredictable. If you’re looking to diversify, or wondering where the next threat is really coming from, this is the tool for you. Tested, and logical, it provides insight and guidance, free from human bias and hidden agenda.
What is it?
Simply put, the Business Evolution Map is a pictorial representation of the economy, laid out in such a way that helps show which journeys into new sectors are likely to succeed, and which are destined for failure. You may already have seen some examples of its use via twitter; if not and in the interests of “show not tell” here they are:
Each one of these examples shows a small portion of the Map and traces the diversification journeys of three very different companies. In each case, the success or failure of the endeavour correlates with the predictions of the Map. The first explains where Carrillion went wrong, the second predicts success for Amazon (not surprisingly), and the third predicts failure for Ineos.
The regions of the Map represent business sectors, and the countries within each region represent the sub-sectors. The countries are then laid out in such a way that the boundaries represent the points at which these sectors merge.
These boundaries are dictated by the people involved in the related businesses, and more specifically their skills, mindsets and networks. Take, for example, the Transportation & Storage sector; at the heart of this is the Freight sub-sector, and the other sub-sectors then cluster around it.
By structuring the Map in this way, we have created a tool which predicts that diversification into neighbouring “countries” will be successful (typically within 3 years), whilst attempts to jump across the Map to disconnected business sectors will be no more successful than a brand new start-up (taking up to 10 years or more to achieve the same level of success).
In other words, if you want to leverage the people and capabilities of your existing business and you want success within a three year window, then you need to focus your attention on your nearest neighbours. This doesn’t mean you can’t augment your organisation with capabilities elsewhere on the map, but it does guide you towards a partnership arrangement, or potentially an “acquire and run at arms length” approach.
In the beginning
We created the Map because we believed there was a lack of true understanding or insight into why attempts to diversify either failed or succeeded. There are, of course, many convincing stories told about how companies moved into fundamentally different business sectors, always with a claimed reason for that success, but it appeared to us that those stories started with a conclusion, and then cherry-picked the stories that could be fitted to that conclusion. As we looked across those stories it was clear that each one proposed a different reason, and not surprisingly, the reason was often related to the product or offered by the author.
It was also clear that consultants were advising companies to diversify into sectors that seemed to be connected at first glance, but on closer inspection, these connections were being inferred from the similar words used to describe them. Wellness and Pharmaceuticals were a good example; both made frequent use of language such as “health” and “wellbeing”, but it was our opinion that, although these businesses were involved in a similar outcome, the fundamental nature of what they did was very different; one provided care, whilst the other manufactured medicinal products.
Therefore, the Map started where most innovations do, with a problem to be solved, and also from our personal need to bring an idea to life visually so that it could be clearly communicated, discussed and challenged. What we needed was a solution to this problem, and as is the Equal Experts way, we started with a ‘Bet’ and a hypothesis.
The Bet was that it would be possible to create something akin to a geographical map onto which company journeys could be plotted, and chances of success judged using the physical distances travelled. The hypothesis was that success or failure of diversification (judged relative to a complete start-up venture) was fundamentally driven by the capabilities of the people within the existing company. We envisaged something like this early concept drawing, but much larger:
It’s all about me!
We started, much as the early European explorers did; from familiar territory. Much of what Equal Experts does involves software development, and so it was easy to chart our immediate surroundings, but beyond that was uncharted territory. Our Map, useful though it was in its own way, was much like this very limited picture of the “known world”. Some bits were likely to be reasonably accurate, and others complete conjecture, not to mention the greater parts that didn’t even appear at all.
The next bit took time and some significant working and reworking. Let’s just say that whiteboards were involved and coffee was drunk. Along with the coffee, a huge amount of data was consumed covering the entirety of the UK economy, although the Map works as well in its final form for any of the world’s economies, as it has people as its main driver.
The most important data to us were the relative sizes (by revenue) of each of the sectors, and the core activities undertaken by businesses in those sectors. The former gave us the relative sizes of each country (we used one hexagon per £1bn revenue), whilst the latter allowed us to position each of the countries so that their edges matched, much like assembling a jigsaw without the guiding picture. There is, of course, a lot more detail in the final version of the Map, but here is a bird’s eye view to give you an idea of scale:
Testing the hypothesis
As the Map had been formed from a hypothesis, it needed to be proven, and to be properly tested we needed to use information it was intended to predict, not information from which it was created. For this we turned to those original stories of diversification, and for each one we mapped out the journey in chronological order, making note of successes and failures.
Our intent was to further “tweak” the Map based on our findings, but we were surprised to find far greater correlation than we ever expected. Clearly those long hours trawling for data and arguing in front of a whiteboard had been worth it after all. There are many examples of companies that got it wrong, but to save their blushes, here is an example of one that got it spectacularly right:
As you can see, every move made is between two neighbouring countries. It’s difficult to comprehend how a company that started out making clothes ended up making just about everything, and played a key role in the building of the Burj Khalifa, the tallest building in the world. There’s a lengthy story to tell here, but for the purposes of this blog post, suffice to say, the map makes it much clearer how a textile company comes to dominate so many apparently diverse business sectors, without the need to fundamentally “reinvent” itself, as some might have you believe.
Putting the Map to use
As well as using the Map to gain insights for Equal Experts and guide our own investments and decisions, we also use it to help our clients understand where they truly are as a business (semantics aside), where they might choose to go next, and who might be coming their way. Helping clients find innovative answers to important strategic questions is what we do, and the Map is an essential tool in our arsenal to help us do that logically and effectively.
The best way to describe how we use it is probably to quote some of our clients’ questions that it helps to answer:
“We’ve grown rapidly, and now we’re too complicated. In what should we invest further and of what should we divest?”
“Having exhausted growth in our market, we want to diversify – but how?”
“Everyone is telling me that my business is about to be disrupted – is this true, and if so, by whom, and what can I do about it?”
“Disruption is so unpredictable; new business sectors spring out of nowhere and cause chaos. Is there a way to anticipate what might come next, and who is best placed to exploit those opportunities?”
Not only does the Map take the guesswork out of the strategy process; it also strips away unconscious biases and misleading semantic connections that otherwise can cloud thinking.
In part 2, we will take the journey of that well-known company, Nintendo, and illustrate, using the Map, what worked, what didn’t, and why that might be.