How creating experiences for underserved markets can be a key innovation strategy

GUEST POST from John Bessant

It’s summertime, at least here in the northern hemisphere and chances are that August is a holiday month. Which might well see you sitting somewhere and watching an exotic sunset, glass of something suitably refreshing in your hand. As you see that golden disc slip below the horizon and the wonderful display of red shifting colour begins to settle towards nightfall you might spare a thought for the memory of Tom Gullick who died this month. Because for many of us jetting away to our exotic location might not be happening were it not for his innovation efforts….

An avid bird spotter (he held the record for the most birds (over 9000) spotted by an individual) he was a bit of a Don Quixote figure, not least because he took up residence in La Mancha in Spain and pursued a conservationist crusade during his later years, saving at least one species of duck from extinction.

But he has another claim to fame — as one of the founding fathers of the low-cost travel experience. His bird-watching abilities gave him good observational skills and led him to spot an opportunity in classic entrepreneurial fashion — and then to go after it with a passion.

Working for a hundred year old firm of shipping brokers (Clarksons) he was given the task one day of getting people to Brussels to attend the World Fair Expo58. The entrepreneur in him rose to the challenge; he chartered a DC-3 plane to fly from Southend airport and arranged coaches, refreshments and even a tour guide, offering the entire package for £6. It worked well but his interest was piqued when he found out that another entrepreneurial member of the party had seen his offer and bought a sheaf of tickets, reselling them via an advert placed in the London ‘Evening Standard’ newspaper for £8! That was the trigger.

He managed to persuade his rather conservative employers to set up a travel subsidiary and became a pioneer of what became known as package holidays. Coming after years of post-war austerity his offer of trips to places abroad, whether the tourist sights of Belgium or Switzerland or the beaches of the Costa del Sol was avidly taken up. He pitched his offer to those previously underserved by the travel industry, offering experiences to those whose normal holidays might be spent in a beach hut at Bridlington. As he later explained in an interview,

“My happiest moment was standing at Rotterdam airport at the end of the day, watching the buses return from the bulbfields with the Dakota DC-3s lined up waiting to take our customers home. They were so happy because they had never believed they could afford to go abroad.”

Within a decade Clarksons had become one of the major players in a market which was exploding with pent-up demand. They offered a wide variety of experiences, from wine tours in France to trips along the Norwegian fjords; their sunshine holiday business was particularly popular , built on a fixed price all inclusive model delivered at low cost. His skills as negotiator helped deliver this — he could guarantee to fill hotels or aircraft if the supplier was prepared to offer competitive pricing.

Other companies joined the party and it wasn’t just the British who were travelling; increasingly horizons for European tourists were extending. Inevitably this brought a highly competitive edge to the market and required a business strategy based on investing in being able to deliver experiences by assembling the different elements and engineering them down to a package with a thin profit margin. He found increasing difficulty in explaining this to a company whose core business in ship-broking operated on very different models and so he finally left in 1972, retiring to Spain to begin his second career as a bird watcher /conservationist.

He got out just in time; by 1974 cut-throat competition from the growing number of players, a devalued UK pound and the emerging oil crisis with its pressure on fuel costs forced Clarksons into collapse in the middle of the summer tourist season.

The business model hadn’t failed — but it did need development and fast learning. All the pieces were there — interesting destinations, hotels, buses, aircraft and so on. The difference was in the market — classically growing at the edges of an existing one. But bringing this new business model to life required reframing how both product and process worked. Margins were thin and so new approaches were needed to booking and operations, while delivering the package experience required a new profession — the tour rep — who would need enormous versatility (dealing with everything from entertainment, health and safety, administration, currency exchange, etc.) while all the while smiling for the customers!

It wasn’t disruptive innovation — yet. What was going on was learning to serve a very different market with a new experience based on lower cost. But the learning about how that model might work sowed the seeds for what later did become a revolution which transformed the travel industry. The evidence for this is with us today. Low-cost airlines like Ryanair, Wizz or Easyjet have revolutionised the world of short-haul travel and although they faced heavy setbacks during the Covid-19 crisis they are now returning to profitability and popularity.

But the package holiday was not a new idea. Some 100 years previously a certain Thomas Cook was looking for a new offering for his growing travel business. Originally a printer and Baptist lay preacher he’d built his original business organising day trips; his first didn’t run too far (the relatively short hop from his home town of Leicester to nearby Loughborough) but in 1841 it gave birth to his travel business. Four years later and he’d clearly tapped into a rich potential market; his trip to the seaside at Liverpool was booked by 1200 people and he had to repeat it two weeks later for another 800 happy travellers.

Cook began to extend his trips across the Channel and by 1863 had seen the possibilities of offering people the opportunity to see far-off places and sights (like the famous Mount Rigi in Switzerland) for themselves. In doing so he pioneered what effectively became the package tour, organizing not only the travel (by road, rail, boat, even mules) and accommodation but also providing guides to help conduct the tour.

Mind you his tour was not for the faint-hearted. In her diaries an intrepid young woman, Jemima Morrell described in detail a world of 4am alarm calls, 20-mile hikes and other challenges — not least of which was also being able to dress for dinner every evening in the hotels in which she stayed! But she clearly felt it was worth it for the experience.

“The days spent on foot, or by the sides of mules, afford the greatest satisfaction …..It was then that, away from the life of the city, we were taken into the midst of the great wonders of nature and seemed to leave the fashion of this world at a distance … It was an entire change; the usual routine of life was gone. All memory of times and seasons faded away and we lived only in the enjoyment of the present.”

Thomas Cook’s ideas changed several things. From the point of view of Switzerland it helped transform a poor rural economy into a travel destination; today the Swiss Alps are one of the world’s most popular tourist destinations.

Cook also created a system-level innovation, much as Henry Ford was to do with the motor car fifty years later. Putting together a successful package tour involves much more than simply arranging travel and tickets. Cook pioneered the complex logistics, arranged for integration of different travel and accommodation options, provided a system of coupons (the forerunners of traveller’s cheques) to help pay for goods and services, developed a network of guides and other support staff and printed brochures not only as sales tools but as a way of engaging customers in imagining and dreaming about the journey they were about to embark upon. In doing so he can rightly be considered one of the founding fathers of an industry which today is worth over $7 trillion.

What he pioneered was ‘experience innovation’ — not simply offering travel but a whole new experience. In his case it was the wonders of the Grand Tour brought to everyone, in Clarksons’ case it was the joys of sun-drenched beaches, endless sangria and exotic food to try.

‘Position innovation’ of this kind begins with a reframing of possibilities — classic entrepreneurial territory. Tom Gullick saw an opportunity in meeting needs for a different group and packaging the experience up rather than leaving them to select their own. Travel agents were involved at the time because the infrastructure for booking wasn’t developed; the later low cost revolution saw increasing disintermediation and the emergence of platform models where you buy your travel, car hire, hotel, parking, currency, anything else you might want, all of it via a one stop online platform.

But in order to meet the needs of an unserved or underserved market requires close interaction and fast learning with that market. By definition it doesn’t exist yet in developed form so the process of co-evolving with it is going to involve a bumpy ride. The Clarkson experience was one of experimentation and building fast learning — from the prototype of Expo58 to the slick summer exodus managed in the heydays of the 1970s.

This is challenging but it also means that position innovation can provide a valuable learning laboratory for those wanting to get out of the box which serving the mainstream market can sometimes represent.

A good example is the fascination (and huge market potential) of digital wallets and mobile money. We’re still (well I am) excited by the fact that we can now use our phones to pay for a whole raft of goods and services and the potential is of course huge. Given an accelerating shove by Covid-19 the shift towards a cashless society has been rapid. Not for nothing is Elon Musk trying to create a new brand X (why do I think of washing powder when I hear that?!!!) and give the bird the bird? It’s got little to do with messaging and everything to do with trying to emulate the power of the WeChat platform in China which is an ‘everything app.’ But at its heart it is about mobile money — so much so that even beggars in China have QR codes so that you can support them with a click of your phone button.

So the cutting edge of innovation — right? Not necessarily. If you live in East Africa you might greet a lot of this with a yawn, it’s nothing special or new. Because M-PESA has been enabling this for over a decade, so much so that the mobile money app running on any phone now accounts for over 50% of all GDP transactions in Kenya and is widely used across much of Africa. What is M-PESA? Essentially it’s a mobile money app which was developed originally as a joint venture between Safaricom (Vodafone’s South African subsidiary) and the UK Department for International Development. The name comes from the Swahili pesa meaning money and the original idea began as a development aid project to enable microfinance repayments. Launched in 2007 it is now used by close to 20 million people and the system processes more payments than Western Union does across its entire global network.

What’s gone on, of course, is a classic piece of position innovation where learning has driven improvements and developments, not only to serve the new market but in products and processes to enable it to grow. And it’s created a platform economy, bringing in shops, transport, government services, etc. — and all of this with the innovative support of the central bank. It’s a model which has grown beyond Kenya to have world-wide impact. For example a valuable spin-off has been the use of this technology to enable more efficient aid distribution to help cope with humanitarian crises.

So position innovation matters and should form a key plank in our innovation strategy. It’s at the heart of the innovator’s dilemma — the emergence of innovation at the fringes of your core market was the threat so potently identified by Clayton Christensen. How you deal with that strategic challenge is still a major question — but making sure you spot emerging position innovation early at least buys you time to think through a strategic response.

And for entrepreneurs looking for new opportunities the challenge is simple — spot a market before it exists and then grow it! Not an easy thing to do when you can’t analyse or run focus groups about it. But — as Thomas Cook and later on Tom Gullick found out, being prepared to experiment can pay off handsomely. Which is why it might be worth raising a holiday glass in their direction to remind ourselves of the challenge…

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Why innovation thrives on being able to reframe

GUEST POST from John Bessant

Mexico City, Olympic Games, 1968. The stadium is packed, the wider world looks on via TV coverage. Everywhere there’s an air of expectancy but also an awareness that at such high altitude it’s going to be hard for athletes to beat their best. Records are there to be broken, you have to hope for something special.

And in the high jump event they weren’t disappointed. The record for men’s high jump had hovered around 2.23 m for several years. But a young 21-year-old was about to change that; Dick Fosbury, representing the USA broke this with a height of 2.24 m and won the gold medal.

What was so spectacular was not just the achievement but the way it was accomplished. Unlike other athletes who did the (apparently obvious) thing of running and then jumping over the bar, swinging one leg behind the other, Fosbury paused at the moment of jumping, turned his back and flopped over it backwards. Why would anyone do that?

Fosbury had an answer — he’d been working on it for five years before his Mexico success. He’d been frustrated with the limits of the traditional ‘straddle’ jump and experimented with alternative ways of getting over the bar, finally hitting on and perfecting his backwards approach. The big advantage of doing this was that it gave him a lower and different centre of gravity and allowed for more clearance over the bar.

The ‘Fosbury flop’ as it quickly became known opened up new possibilities for the sport; within ten years it had become the dominant mode for all jumpers and helped move the world record to 2.45 m which was set in 1993 by Javier Sotomayor. These days anyone attempting the high jump has come to resemble the ‘fish flopping on the deck of a boat’ as one newspaperman described Fosbury’s Mexico model.

What Fosbury’s feat reminds us of is the power of reframing in innovation. Innovation can take place anywhere along a continuum from doing what we do better — incremental — to doing something completely different — radical. And it can cover what we offer the world — product or service — and the ways we create a deliver that offering — process. That gives us plenty to keep us busy in our innovation day.

But sometimes we can reframe, look at what we’re doing in a different way, identify novel approaches. For example we can rethink the positioning of our innovation — opening up a new market segment or moving into a new geographical area. There’s plenty of learning and pivoting involved in doing that — as Netflix discovered when it began to extend its offer from the USA to Canada and then Europe and beyond.

We can be more radical and change the story we tell and who we tell it to — think about Starbucks and others and the way they repositioned coffee from a simple hot drink to something consumed in as many varieties and combinations as fine wine. Or Haagen Dasz and others who reframed the idea of ice cream as a sensuous adult pleasure rather than as a treat for kids on hot days. Or Henry Ford, bringing the motor car from the small luxury goods market to being a ‘car for Everyman at a price every man can afford’.

This kind of reframing opens up new possibilities but poses new challenges. Just as Fosbury and his followers had to rethink so much of their approach and learn new tricks (not least about where and how to land!) so this kind of position reframing requires major modifications to our product/service offering and our delivery processes.

Think about low-cost airlines — if your main idea is to offer flights at half the price of your competitors, you’ll quickly find out how fast you can lose money. The only way you can make that model work, selling seats to a market who otherwise couldn’t afford to fly will be to radically change your processes, stripping costs and complexity out of everything from booking to check-in, to boarding right through to turnaround time management. Master those tricks and you not only have a viable business model, you’ve got something which the rest of the pack have to catch up with.

But reframing doesn’t stop there; business model innovation is a very hot topic these days and at heart it is finding ways to change the game by replacing one business model with an alternative. Business models set out the architecture through which an innovation can deliver value– for which market segments, with which value proposition and so on. Business model innovation is all about replacing that system with a different better one.

We’ve got plenty of examples of this happening — think about Uber or AirBnB and how they’ve not only become successful new models themselves but also offered templates for others to use in different fields. Or what Spotify and the music streaming services have done to entertainment by changing the model from ownership to rental.

It’s the same with capital goods makers like Caterpillar or Rolls Royce. Instead of selling products on a one-off basis they now offer the functionality of those products to their customers on a rental basis, charging them for ‘power by the hour’ for example.

Once again such reframing isn’t trivial, it drives innovation in product and service and it requires major rethinking about the processes which create and deliver such service. If Rolls-Royce, General Electric and others are being paid for the number of hours their engines are keeping airliners in the air then they need to work hard to ensure that they are reliable and well-maintained. So, they’ve had to innovate in their products to improve reliability through intelligent condition monitoring, they’ve had to install skilled staff at airports making sure engines are quickly and regularly checked and serviced and they’ve had to rethink their financial and support operations to reflect the changed approach.

Beyond all of this comes the possibility of completely reframing the whole approach to innovation — innovation model innovation. Sometimes it’s not enough just to tweak and adapt the product or service development pathway or revise arrangements for process improvement. Sometimes there’s a need for a radical rethink.

A good example of this is the experience of Procter and Gamble and their emerging response to the opportunities and challenges posed by ‘open innovation’. Faced with a world in which ‘not all the smart people work for you’ they reframed their approach to creating thousands of new products from one which had dominated a century and a half of growth through Research and Develop. They replaced it with a new strategic approach to innovation, with the deceptively simple label of ‘Connect and develop’.

The challenge was easy to express and CEO Alan Lafley did so in 2000 when he launched the program. In the future P&G would get 50% of its innovations from outside as distinct from the previous model which was 100% home grown. Simple to say but it has taken then 25 years to turn the challenge into a viable and successful new model. On the way they’ve had to re-engineer so much, finding ways to identify and filter external ideas, to assimilate them and deploy that new knowledge in new directions for the business and simultaneously to make better use of knowledge which P&G had created and then never found a home for.

There’s a useful Greek word — paradigm — which can be used to describe the way we see the world. It’s like the mental spectacles through which we see which problems to focus on and which solutions might be relevant. Change the spectacles — reframe to a new paradigm — and everything looks different, opening up new and fruitful possibilities. It’s an idea which has been put forward by Thomas Kuhn the philosopher of science to explain how our thinking progress follows a pattern of ‘punctuated equilibrium’ — long periods of working within a particular frame followed by a transition to a new way of looking and thinking.

Paradigm innovation is powerful and influential; it changes the world. Which might be a fitting epilogue to the fulsome obituaries which have appeared in response to the sad news of Dick Fosbury’s death last month. He was a true innovator and he changed the way athletes thought about their challenge, helping them aim and reach higher.

Image Credits: Eugene Register-Guard (From the Medford Star-Tribune, 1964), John Bessant (Doodly)

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How Machine Learning is Transforming the Innovation Game

GUEST POST from John Bessant

One of the difficult parts of being a parent is when your kids grow up and you lose the excuse to play with their toys. And in my case one that I particularly miss is the Transformer series. Originally developed in the 1980s and accompanied by a TV spin-off these robots could masquerade as ordinary vehicles like cars and oil tankers. And then, at a crucial moment, they could reassemble themselves into well-armed fighting robots able to save mankind on a weekly basis from all sorts of alien threats. The toys were masterpieces of engineering; the underlying story clearly had staying power since there is a new generation of transformers (and video /movie accompaniment) today.

For their time they were symbols of the power of transformation, being able to adapt and repurpose to deal with new challenges. And these days we have a much more powerful and real example of such power in the form of a new generation of machine learning models.

Machine learning has its roots back in experiments with ‘artificial intelligence’ in the 1970s but has come to represent a powerful technological trajectory as the idea of mimicking human neural networks and their learning capabilities has been explored. We’ve seen with increasing frequency many bastions fall to these models; it seems a lifetime ago (1996 actually) that IBM’s Deep Blue beat chess champion Gary Kasparov deploying something of a brute force approach. But by 2016 Google’s Alpha Go model managed to beat the world champion Lee Se-Dol at the much more complex game of ‘Go’. And recent contests at which machine learning seems to have ‘beaten’ human opponents include those like poker which involve not only strategy but the ability to bluff — essentially requiring computer models to imagine what an opponent is thinking and then generate a diversionary move.

As Jang Dae-Ik, a science philosopher at Seoul National University, told The Korea Herald after AlphaGo’s victory ‘This is a tremendous incident in the history of human evolution — that a machine can surpass the intuition, creativity and communication, which has previously been considered to be the territory of human beings…..Before, we didn’t think that artificial intelligence had creativity…..Now, we know it has creativity — and more brains, and it’s smarter’.

At heart these developments reflect a fundamental shift in machine learning applications and models. In the early days models were used to help with highly focused activities — for example applied in data mining where they might be searching for something specific. But now we have generative AI, which does what it says on the tin — generates something new. And this brings the uncomfortable challenge to our perception of ourselves as the only ones capable of creativity — generating novel and useful solutions to challenges.

A quick review of the growing literature on ‘artificial creativity’ shows that there are grounds for worrying. Machine learning models can now ‘create’ music, literature or visual art to a standard which makes it increasingly difficult to detect its non-human origin.

For example, the Next Rembrandt project was an attempt by a team of art historians, data scientists and engineers to teach a machine to think, act and paint like Rembrandt. The documentary film of this venture highlights the challenges and complexities involved in producing a painting which convinced many — 347 years after the painter’s death!

In similar fashion, there are a number of websites featuring music composed by AI in the style of — and often hard to distinguish from — the original composer. And in 2016 IBM’s Watson AI engine produced a trailer for the horror movie ‘Morgan’. This involved Watson ‘watching’ and analyzing hundreds of examples of trailers and then selecting scenes for editors to patch together into their film. This cut the time for the process from over a week to less than a day.

Which brings us to Chat — GPT and the explosion of interest in this particular model. It was launched by the OpenAI company in November 2022 as the latest in a series of generative models with the capability to come up with its own answers to questions posed to it. (Amongst its predecessors is Dall-E, a powerful image generator). The GPT stands for Generative Pre-Trained Transformer, a class of model which they have been working on for some time.

At its heart the Chat GPT model (and its equivalents in the labs of Google/Alphabet, Meta and many other companies) is a machine learning model trained on billions of facts. It has the ability to explore and analyse those and ‘learn’ how to synthesis coherent and credible answers to questions posed by a very wide and diverse audience. Within two weeks of its launch Chat GPT had attracted over a million users, and the demand is now so high there is a waiting list to access it. People have been experimenting with its capabilities to create songs and poems, write newspaper articles, answer exam questions and even to enter and pass the preliminary tests for people wishing to qualify as medical professionals in the USA!

Not surprisingly OpenAI it has seen its valuation rapidly escalate to around $29bn with Microsoft taking a significant share in the business. It’s likely that the next year will see an explosion of interest in such models with new and better variants and increasing competition from other players.

One area where such models may well have a significant impact is in the field of innovation itself. In an excellent article Frank Piller and colleagues explore this — and the implications for innovation management. They point out that there is already increasing use of generative machine learning models in innovation; these include searching large data sources to identify insights around customer needs and using generative models to create marketing and advertising copy for new products and services.

They map their analysis of where and how such models might be used on to a typical representation of the innovation process — the so-called ‘double diamond’ linked to ‘design thinking’. Here there is a front end concerned with exploring the ‘problem space’ — understanding user needs and potential opportunities. Work at this stage involves divergent exploration followed by convergence, closing in on promising directions. It is linked to a second divergent/convergent diamond linked to exploring the ‘solution space’ and then closing in on projects to be taken further.

What they were interested in was the ways in which features of machine learning might help with these activities and the possible impact on how innovation is undertaken — and by whom.

A fascinating feature of their research is that they do so not just on the basis of informed speculation but by putting the Chat-GPT model to the test, giving it some innovation challenges to work on. Thinking about the possibilities for new products in the field of camping and outdoor activity they designed three questions to put to the model, looking for whether and how new insights might be generated to help with:

• Searching through large data sets containing information about potential new directions and trajectories
• Exploring data on customer experience and searching for new insights into potential needs
• Helping create new concepts around which innovations might be developed

All of these are typical tasks which innovation teams undertake in organizations; for example they spend a lot of time at the front end researching what has already been done, drawing in knowledge and building a picture of possible problem and solution space. They deploy a wide range of market research tools including various forms of trend analysis. And they work with a range of creativity tools to generate possible solution options for further progression.

It’s early days but the performance of the machine learning model was instructive. In exploring what is known about camping gear a Google search identified 299 million results which certainly exceeds the capacity of even a small army of human researchers to analyze! The Chat-GPT model did a good job in analyzing and pulling out results of possible relevance, providing at least a powerful first-pass filter.

In its second task the Chat-GPT model managed to make sense of a wide range of customer reviews to generate insights into trends and possible needs — so-called ‘sentiment analysis’. Once again its skill in sifting through the text of thousands of reviews showed potential for providing new insights into emerging and hidden customer needs.

And in the field of creating potential solutions the researchers set the model a brainstorming kind of task — to come jup with novel and useful ideas for new camping products. The strategy here is to prompt the model with some examples of typical brainstorming insights and then allow it to learn how to generate its own. Once again the performance on the task was impressive; not only did it come up with plausible incremental innovation ideas, it also generated some radically new ones which opened up new solution space.

At first glance this kind of performance across several areas of the innovation process might seem worrying. Even though there has been a backlash to the wave of enthusiasm around generative machine learning models the overall trajectory looks ominous in terms of its implications for ‘creative’ tasks in organizations. If machine learning continues to improve how long might it be before we no longer need human beings to work in the innovation process?

The reality seems to point more towards a hybrid model in which AI supports human activity — for example by using it to sift through enormous amounts of data and extract potentially relevant information which its human counterparts can then work with. As the researchers conclude, ‘….by expanding the problem and solution spaces in which NPD (new product development) teams can operate, language models create an opportunity to access and generate larger amounts of knowledge, which in turn results in more possible connections of problems and solutions. This should ultimately lead to qualitatively superior solutions and higher innovation performance.’

So can we relax and not worry about the machines taking over our innovation role? Not really —  if we want to take advantage of the powerful hybrid approach which Frank PIller and his colleagues point towards then we need to start learning some new skills and developing some new working arrangements to capitalise on it.

We’re going to need a lot of innovation model innovation.

P.S. In writing this piece I did NOT make use of Chat-GPT, though I was tempted to try! Researching this piece inspired me to write another innovation song:

Image credit: Wikipedia

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