Tim Kastelle

Category: book riffs

  • Psychology and Spreading Ideas

    Tim wrote a great post last week on being responsible for getting your ideas to spread. This is important because a lot of us spend time working on a good idea and then take very little time working out the best way to get people to listen. I’m often guilty of this too and its a big trap for technical specialists who get wrapped up in the idea to the point where they believe that it should be obvious to everyone.

    I’m reading Daniel Kahneman’s “Thinking Fast and Slow” at the moment and it’s a real jewel-box of information on what pschychological science has taught us about decision making and rationality. Kahneman has revolutionised economics and business theory by introducing the inherrent biasses and limitations to challenge our assumptions of rational behaviour. No matter how smart you think you are, our brains are wired up with a surprising capacity to be tricked.

    The main idea in his book is that we have two thinking systems. System one is intuitive and fast. It runs in our minds most of the time without much effort but is affected by emotions. On the other hand system two is more critical and logical. Engaging system 2 requires more deliberation and effort. Sometimes system 1 tries to do work that should be done by system 2 and that’s when we get problems. Kahneman uses the following example:

    A bat and a ball cost $1.10 in total. The bat costs $1 more than the ball. How much does the ball cost?

    The answer that I thought of when reading the book was ten cents. That’s obvious isn’t it? Well the correct answer is 5 cents. I felt a bit stupid until Kahneman explained that this question tricks about half of Ivy League undergrads as well. The deception works because it invites system 1 to jump to the first answer with a simple question and some round numbers. Most of us only engage system 2 after we find out we are wrong.

    One part of Kahneman’s book discusses the psychology of credibility. In essence, if you want people to listen and believe you then it’s important to find ways to engage system 1 and keep system 2 quiet. Experiments have found that people in a state of cognitive ease, where they don’t need to strain to take in ideas and information, have a quiet system 2. If you want to fire-up system 2 to get a more critical and sceptical response to what you are saying then the best way to do that is to impose cognitive strain.

    Busy slides with many complex diagrams, long paragraphs on the slide and hard-to-read fonts are all presentation faults that will activiate system 2 and make you less credible. If you are hard to hear or speak in long and convoluted sentences you will be judged as being less credible as well. Sure, you might have the facts right but that is not how the mind works.

    Have a look at this presentation by Seth Godin. Is he credible or not? Have a look at his slides.

    On the other hand, system 1 engages quickly with familiarity and repetition. We feel at ease with familiar concepts when a I good speaker gives us something that we can relate to or when there is a repetition of themes. My favourite speech of all time does this superbly (and apparently Martin Luther King wrote it in one night).

    Credibility is in the eye of the beholder. Understanding how people form perceptions of what to believe can go a long way to help you spread ideas.

  • Your Customer Isn’t “Everyone” – Seven Innovation Thoughts Triggered by Peter Drucker

    Of all the management writers I’ve read, Peter Drucker probably generates more interesting quotes per page than anyone. Tom Peters and Joseph Schumpeter come close too. Here are six thoughts triggered by reading his book Management Challenges for the 21st Century:

    1. And yet very few institutions know anything about the noncustomers—very few of them even know that they exist, let alone know who they are. And even fewer know why they are not customers. Yet it is with the noncustomers that changes always start.

      Drucker is making a slightly different point here – that we need to look outside of our normal networks to innovate. True. But also, your customer isn’t “everyone.” Even Coke has more noncustomers than customers in the world. A big part of strategy is making the choice of who your customer will be. This choice is based on value, however:

    2. The starting point has to be what customers consider value. The starting point has to be the assumption—an assumption amply proven by all our experience—that the customer never buys what the supplier sells. What is value to the customer is always something quite different from what is value or quality to the supplier. This applies as much to a business as to a university or to a hospital.

      Innovators often don’t know what their idea is for. This is a strong argument for two things: empathy and experiments.

    3. Everything improved or new needs therefore first to be tested on a small scale, that is, it needs to be PILOTED. The way to do this is to find somebody within the enterprise who really wants the new. As said before, everything new gets into trouble. And then it needs a champion. It needs somebody who says: “I am going to make this succeed,” and who then goes to work on it. And this person needs to be somebody whom the organization respects. This need not even be somebody within the organization. A good way to pilot a new product or new service is often to find a customer who really wants the new, and who is willing to work with the producer on making truly successful the new product or the new service.

      New ideas need champions. This is a critical part of piloting or prototyping, which in turn drives experimentation. This is one of the best ways to find out what customers actually value – testing.

    4. Every organization operates on a Theory of the Business, that is, a set of assumptions as to what its business is, what its objectives are, how it defines results, who its customers are, what the customers value and pay for. Strategy converts this Theory of the Business into performance.

      Theories of Business need to be tested – this is how to design the experiments. Start with the assumptions in your business model, then test them to see if they are true.

    5. An entrepreneur who does not learn how to manage will not last long. A management that does not learn to innovate will not last long. In fact, … business—and every other organization today—has to be designed for change as the norm and to create change rather than react to it.

      The main reason is that a policy of systematic innovation produces the mindset for an organization to be a change leader. It makes the entire organization see change as an opportunity.

      The consequence of this is that innovation is a process, which can be managed.

    6. The first change policy, therefore, throughout the entire institution, has to be Organized Abandonment. The change leader puts every product, every service, every process, every market, every distribution channel, every customer and end-use, on trial for its life. And it does so on a regular schedule. The question has to be asked—and asked seriously—“If we did not do this already, would we, knowing what we now know, go into it?” If the answer is “no,” the reaction must not be “Let’s make another study.” The reaction must be “What do we do now?” The enterprise is committed to change. It is committed to action.

      If we were starting today, would we do this? This is the blank slate approach described by Jeffrey Phillips, and it is an important tool for evaluating your business model.

    7. To try to make the future is highly risky. It is less risky, however, than not to try to make it. A goodly proportion of those attempting to do what this chapter discusses will surely not succeed. But, predictably, no one else will.

      Or, as Wayne Gretzky said, “You miss 100% of the shots you don’t take.” You improve your chances of succeeding by systematically looking at the drivers of innovation opportunities.

    Here it is in a nutshell: Innovation drives change, and change drives growth. We can’t know in advance what will work, so we need to use experiments to discover what customers value. It’s risky, but less risky than doing nothing.

  • You Are Responsible for Getting Your Ideas to Spread

    So you’ve had a great new idea for making something better, and you’ve figured out how to make it real. It could be a new thing, a new method, anything. But no one is picking up on your idea.

    What’s wrong?

    Often, people in this situation think that the problem is that others just don’t understand their great idea. After all, it’s a great idea – it’s value should be obvious, right?

    But often the problem is really either that maybe the idea isn’t so great, or, maybe you’re not communicating that value very clearly.

    Let me illustrate these with examples from literature.

    First off is a quote from a character in Headhuntersby Jo Nesbo (which is an entertaining thriller, though not exactly literary):

    An artist who maintains that he has been misunderstood is almost always a bad artist who, I’m afraid to say, has been understood.

    In other words, the idea isn’t so good.

    Now consider this from an interview with David Foster Wallace on Salon from just after Infinite Jest came out. It includes this quote about the state of Literature:

    If you, the writer, succumb to the idea that the audience is too stupid, then there are two pitfalls. Number one is the avant-garde pitfall, where you have the idea that you’re writing for other writers, so you don’t worry about making yourself accessible or relevant. You worry about making it structurally and technically cutting edge: involuted in the right ways, making the appropriate intertextual references, making it look smart. Not really caring about whether you’re communicating with a reader who cares something about that feeling in the stomach which is why we read. Then, the other end of it is very crass, cynical, commercial pieces of fiction that are done in a formulaic way — essentially television on the page — that manipulate the reader, that set out grotesquely simplified stuff in a childishly riveting way.

    What’s weird is that I see these two sides fight with each other and really they both come out of the same thing, which is a contempt for the reader, an idea that literature’s current marginalization is the reader’s fault. The project that’s worth trying is to do stuff that has some of the richness and challenge and emotional and intellectual difficulty of avant-garde literary stuff, stuff that makes the reader confront things rather than ignore them, but to do that in such a way that it’s also pleasurable to read. The reader feels like someone is talking to him rather than striking a number of poses.

    This addresses the second issue – thinking that the problem is a lack of understanding. It drives me crazy every time I hear someone say that their new product or service isn’t doing well because they have to ‘educate the customer.’ To me, this idea reflects the same problem that DFW is talking about – blaming your customer. I’m not saying that everything you do has to be dumbed down. But if people don’t understand the benefits of your new idea, then you haven’t presented it well. Even if the idea is just a concept, you have to be able to explain it in clear English (or whatever language you’re using). And if the idea is a product or service, you have to make it reasonably easy for people to see how it’s good for them.

    When I was doing industrial water treatment, a lot of people in the industry spoke about water treatment like it was a black box that the clients could never understand. I always took that to mean that they didn’t understand it themselves well enough to explain it. So I practiced talking about it until I could explain water treatment to people with pretty low education levels who often had English as a second language. That’s how I got the idea ‘working with Tim is a good thing’ to spread.

    Bottom line – to get your ideas to spread you have to connect with people in a way that is meaningful and useful to them. If you can’t do this, it’s your fault, not theirs.

  • How to Take Advantage of Surprises

    If the budget that you’re managing blows out one month and you’ve spent 200% of your allocated funds, what happens?

    In most organisations, a negative surprise like this leads to painful forensic investigations. To improve efficiency, it is important to stamp out negative surprises like this.

    Conversely, what happens if one of your revenue areas brings in 200% of what’s expected. In most organisations, this would be greeted happily, but these positive surprises are not usually investigated as closely as negative ones.

    This is a mistake.

    Peter Drucker discusses these positive surprises as unexpected occurrences, one of the seven triggers of innovation opportunities. He has several interesting examples of positive surprises and the opportunities that they present in his book Innovation and Entrepreneurship.

    In the first example, Macy’s actually tries to stamp out the positive surprise. Here is the story:

    More than thirty years ago, I was told by the chairman of New York’s largest department store, R.H. Macy, “We don’t know how to stop the growth of appliance sales.”

    “Why do you want to stop them?” I asked, quite mystified. “Are you losing money on them?”

    “On the contrary,” the chairman said, “profit margins are better than on fashion goods; there are no returns, and practically no pilferage.”

    “Do the appliance customers keep away the fashion customers?” I asked.

    “Oh, no,” was the answer. “Where we used to sell appliances primarily to people who came in to buy fashions, we now sell fashions very often to people who come in to buy appliances. But,” the chairman continued, “in this kind of store, it is normal and healthy for fashion to produce seventy percent of sales. Appliance sales have grown so fast that they now account fo three-fifths. And that’s abnormal. We’ve tried everything we know to make fashion grow to restore the normal ratio, but nothing works. The only thing left now is to push appliance sales down to where they should be.”

    “The only thing left now is to push appliance sales down to where they should be.”

    Eliminating negative deviance is usually good, but eliminating positive deviance is not so good.

    The outcome was that Macy’s languished. In contrast, Bloomingdale’s noticed the same trend, and put extra effort into promoting appliances. Consequently, they jumped from fifth in sales among NYC department stores to a strong second.

    Drucker has two more interesting examples:

    A German chemist developed Novocain as the first local anesthetic in 1905. But he could not get the doctors to use it; they preferred total anesthesia (they only accepted Novocain during World War I). But totally unexpectedly, dentists began to use the stuff. Whereupon – or so the story goes – the chemist began to travel up and down Germany making speeches against Novocain’s use in dentistry. He had not designed it for that purpose!

    …entrepreneurs know that their innovation is meant to do. And if some other use for it appears, they tend to resent it. They may not actually refuse to serve customers they have not “planned” for, but they are likely to make it clear that these customers are not welcome.

    This is what happened with the computer. The company that had the first computer, Univac, knew that its magnificent machine was designed for scientific work. And so it not even send a salesman out when a business showed interest in it; surely, it argued, these people could not possibly know what a computer was all about. IBM was equally convinced that the computer was an instrument for scientific work: their own computer had been designed specifically for astronomical calculations. But IBM was willing to take orders from businesses and to serve them. Ten years later, around 1960, Univac still had by far the most advanced and best machine. IBM had the computer market.

    Positive surprises create opportunity. Here are some dos and don’ts for dealing with these surprises:

    • DON’T just analyse negative deviance, DO analyse positive deviance as well. Drucker suggests that for every regular meeting you have to address problems, you should also have one focussed on opportunities. Particularly those provided by these positive surprises.
    • DON’T try to fit these anomalies into business as usual. Macy’s and Bloomingdale’s both analysed the unexpected change in appliance sales. But their responses were quite different. Macy’s treated positive deviance just like negative – and they tried to stamp it out. Bloomingdale’s changed the way they did business.
    • DO get market feedback as soon as you can, and DO pay attention to it! You may think you know how to deal with the opportunity, but data changes everything. The Novocain and computer examples both show the value of data. Dentists buying Novocain and businesses buying computers were both unexpected – they were surprises. IBM was ready to respond to the data that showed that businesses were interested in computers, even though they didn’t understand this demand. Univac was not.

    There is often an automatic negative reaction to any surprise, positive or negative. It’s important to understand that both types of surprise provide innovation opportunities.

    To take advantage of these opportunities, you must be prepared to analyse the surprise, gather data to test the opportunity, listen and respond.

    That’s how to take advantage of surprises.

  • Why Innovation is Less Risky Than You Think

    One of the most common excuses I run across for not innovating is risk aversion. Organisations don’t innovate because they’re risk averse, or so they say.

    But is innovation really so risky? Yes, a new idea might not work. But in many cases, not innovating is even riskier. Here is how Peter Drucker puts it in his classic book Innovation and Entrepreneurship:

    Entrepreneurship, it is commonly believed, is enormously risky. And indeed, in such highly visible areas of innovation as high tech – microcomputers, for instance, or biogenetics – the casualty rate is high and the chances of success or even of survival seem to be quite low.

    But why should this be so? Entrepreneurs, by definition, shift resources from areas of low productivity and yield to areas of higher productivity and yield. Of course, there is a risk they may not succeed. But if they are even moderately successful, the returns should be more than adequate to offset whatever risk there might be. One should thus expect entrepreneurship to be considerably less risky than optimization. Indeed, nothing could be as risky as optimizing resources in areas where the proper and profitable course is innovation, that is, where the opportunities for innovation already exist. Theoretically, entrepreneurship should be the least risky rather than the most risky course. (emphasis added)

    This is the point that Clayton Christensen, Stephen Kaufman and Willy Shih make in their article Innovation Killers (link to pdf). They illustrate it with this great diagram:

    When we assess the potential risk of innovating, it is normal to assume that things will continue as they currently are. In a stable environment, it might be safe to assume that taking the ‘do nothing’ option will result in stable returns.

    Drucker’s point is that if you are in an industry that is primed for innovation, then even if things seem stable, assuming continued safe returns is extremely dangerous.

    Then how can we tell if our industry is primed for innovation?

    Drucker addresses this in the book (and there is a short summary in this HBR article as well) – he identifies seven drivers of innovation opportunity. These are things that change the environment. Drucker contends that these can be identified through analysis, and that regularly conducting such analyses is a central part of the discipline of innovation.

    The seven drivers are:

    1. Unexpected Occurrences: Drucker stresses that we should look for outcomes in our business that surprise us. These can be positive surprises. He talks about Macy’s department store in the 1950s identifying an unexpected surge in appliance sales relative to clothes. This actually reflected the start of a major shift in consumer behaviour. Or the surprise can be negative, like the failure of the Edsel. In both cases, you have to identify the surprise and learn from it. Macy’s identified the surprise, but didn’t act. It was Bloomingdale’s that took advantage of the change in behaviour. On the other hand, Ford did learn from the Edsel, which led to the extremely successful introduction of first the Thunderbird, and then the Mustang.
    2. Incongruities: these are differences between expectations and results, or between beliefs and reality. A great example of this is in the shipping industry. For a long time, it was assumed that the best way to drive down costs was to reduce the time it took to get between ports. However, this is an incongruous belief. The real problem in shipping is when the ship is idle. So the best way to increase returns is to get in and out of port as quickly as possible. Recognising this incongruity is what led to the invention of containerization – which almost immediately led to a 60% reduction in shipping costs.
    3. Process Needs: these arise from problems within a production process. Photography provides a good example. When it was invented, it quickly became very popular. However, a big impediment to amateur photography was the need for the use of heavy glass plates. George Eastman saw this process problem, and worked to replace the glass plates with cellulose film. Doing so is what led to a market-dominant position for his company, Kodak, within 10 years of the introduction of his lightweight camera.
    4. Industry and Market Changes: here are some of the examples Drucker used in 1985:

      In a similar fashion, changes in industry structure have created massive innovation opportunities for American health care providers. During the past ten or 15 years, independent surgical and psychiatric clinics, emergency centers, and HMOs have opened throughout the country. Comparable opportunities in telecommunications followed industry upheavals—in transmission (with the emergence of MCI and Sprint in long-distance service) and in equipment (with the emergence of such companies as Rolm in the manufacturing of private branch exchanges).

      You can see similarly structural changes now driven by the internet in a wide range of industries.

    5. Demographic Changes: these are usually fairly predictable, but often ignored. For example, how many of you are still not considering the enormous opportunities provided by the increase in ageing consumers that we are currently going through? This is the best innovation opportunity ever.
    6. Changes in Perception: here is Drucker again:

      All factual evidence indicates, for instance, that in the last 20 years, Americans’ health has improved with unprecedented speed—whether measured by mortality rates for the newborn, survival rates for the very old, the incidence of cancers (other than lung cancer), cancer cure rates, or other factors. Even so, collective hypochondria grips the nation. Never before has there been so much concern with or fear about health. Suddenly, everything seems to cause cancer or degenerative heart disease or premature loss of memory. The glass is clearly half empty.

      Rather than rejoicing in great improvements in health, Americans seem to be emphasizing how far away they still are from immortality. This view of things has created many opportunities for innovations: markets for new health care magazines, for exercise classes and jogging equipment, and for all kinds of health foods. The fastest growing new U.S. business in 1983 was a company that makes indoor exercise equipment.

    7. New Knowledge: these are opportunities that arise through invention – and often this is the only one of these drivers that we consider.

    Obviously, these drivers often overlap. The main point is to use them as tools to identify the areas that we should be thinking about.

    If you do this, you are on your way to practicing innovation as a discipline, rather than as a lottery. And if you do that, innovation can be much less risky than doing nothing.

  • Evidence-Based Innovation Management

    Yesterday I made the case for evidence-based management in general. Today I’d like to talk about what this means for managing innovation.

    The case in favour of evidence-based management is made in Hard Facts, Dangerous Half-Truths And Total Nonsense: Profiting From Evidence-Based Managementby Jeffrey Pfeffer and Bob Sutton. They talk about a few innovation examples, and there are a few things that we know about managing innovation.

    There’s not space to summarise all of the research evidence here, but these are some of the things that know:

    • Innovation is risky, but it drives growth: this is what Pfeffer and Sutton say:

      We’ve also seen that there is no innovation without failure. Most organizational change efforts have a high failure rate—from mergers, to new product introductions, to technological change efforts. But the rub is that the only thing more dangerous than changing an organization is never changing it at all.

    • Innovation works best if you manage it as a process: I think about innovation as the process of idea management:

      The biggest mistake I see firms make is to think that innovation is only about having great ideas. That’s part of it, but you also have to select the best ideas to pursue, make those ideas work, and then get the executed ideas to spread.

    • Great new ideas are most often combinations of old ideas: here are Pfeffer and Sutton again:

      It sounds ironic, but even creativity is mostly sparked by old ideas. Both major creative leaps and incremental improvements come from fiddling with ideas from other places and blending them in new ways. Better ideas result when people act like “nothing is invented here” and seek new uses for others’ ideas. This holds for even the most creative companies like Apple, 3M, IDEO, Genentech, Google, Capital One, and Cirque du Soleil. Unfortunately, too many companies are plagued by the not invented here syndrome, where people insist on using homegrown ideas, especially ideas that can be ballyhooed as new and different. There are, after all, substantial rewards for pretending that the same old ideas are brand-new. Managers can impress bosses with cutting edge ideas. Consultants can sell clients unique services. Gurus can land lucrative book contracts and speaking fees by peddling the next big thing. And journalists can sell newspapers and magazines by giving readers the latest scoop.

      It’s usually smart to be cautious if someone is pushing an idea that they claim is completely novel.

    There is a great example of evidence-based innovation management in Game-Changing Strategiesby Constantinos Markides.

    It’s a very good book. Markides looks at business model innovation, particularly how to manage it in larger firms. One of the crucial issues in that case is this: how can you effectively manage multiple business models within one firm? This is something that larger firms usually have to come to grips with when trying business model innovation.

    You have a couple of choices to make here. The first is: should the new business model be run in a separate business unit, or should it be integrated into the main business? The second is whether or not you should enter one of these configurations in a phased manner.

    The strong recommendation is that you should always set up a separate unit – think about Michael Porter talking about being stuck in the middle if you try to achieve multiple objectives.

    However, Markides has found that the answer isn’t so straightforward. He studied 68 firms that tried to manage dual business models. Of these, 42 created a separate business unit, while 26 did not.

    And of the forty-two that created a separate unit, only ten were successful. This implied that separation on its own is not enough to ensure success – thirty-two firms (out of forty-two) created a separate unit but still failed!

    The findings were similarly mixed for those following an integrated strategy. Even though conventional wisdom says this shouldn’t work, in a number of cases it was a successful approach.

    What should we make of this?

    In comparing the successful firms to the unsuccessful ones, this is what Markides found:

    1. Separate units were more successful if they had a high degree of autonomy to make financial and operational decisions, but not when they had autonomy on strategic decisions.
    2. Firms were less successful in managing separate business models if the units used different evaluation and incentive systems.
    3. Running separate business units works better if the CEO of the new unit is an insider rather than an outsider.
    4. Firms that allowed the new unit to develop its own culture were more successful than those that expected them to adopt the dominant culture of the parent.

    This is a tricky mix. It shows that running separate business units requires a combination of autonomy and control, and the choices of what you keep control over are crucial to success. These results are not immediately obvious, which is a great benefit to this research – it provides useful insight.

    This is typical of evidence-based management. It is harder to do, because there are no clear-cut answers that are always right. While it’s convenient to think that there are, one-size-fits-all solutions usually come from charlatans.

    Knowing the boundaries of a great idea is very important – when will the idea work, and when won’t it? Good research can show you the circumstances in which a particular idea is more likely to be successful. We don’t know everything about managing innovation, but we do know a fair bit now.

    Practicing evidence-based innovation management doesn’t guarantee success. But it improves your chances. And in a complex environment, that’s a great outcome.

  • The Case for Evidence-Based Management

    How can we be better managers?

    I just finished reading Hard Facts, Dangerous Half-Truths And Total Nonsense: Profiting From Evidence-Based Managementby Jeffrey Pfeffer and Bob Sutton. It is a must-read book, and they have one simple recommendation for managing more effectively: make better use of the evidence that shows us how to be better managers.

    They start by saying that most people probably think that managers already do this.

    But if you thought any of that, you would be wrong. Business decisions, as many of our colleagues in business and your own experience can attest, are frequently based on hope or fear, what others seem to be doing, what senior leaders have done and believe has worked in the past, and their dearly held ideologies—in short, on lots of things other than the facts. Although evidence-based practice may be coming to the field of medicine and, with more difficulty and delay, the world of education, it has had little impact on management or on how most companies operate. If doctors practiced medicine the way many companies practice management, there would be far more sick and dead patients, and many more doctors would be in jail.
    …
    When the late Peter Drucker was asked why managers fall for bad advice and fail to use sound evidence, he didn’t mince words. “Thinking is very hard work. And management fashions are a wonderful substitute for thinking.” If you are willing to do the hard thinking required to practice evidence-based management, if you want to reap its benefits, you need to recognize your blind spots, biases, and your company’s problems and take responsibility for finding and following the best data and logic.

    One of the reasons that we make poor use of evidence is that often we make mistakes interpreting the numbers. There are two common errors here.

    The first is looking at the numbers for a particular action, and thinking that they don’t apply to you. I call this the lottery mistake. As the web-cartoon Saturday Morning Breakfast Cereal points out, you won’t win the lottery:

    And yet, people still buy lottery tickets. There is evidence to suggest that there are possible psychological benefits from buying a lottery ticket, regardless of how bad the odds are. But a lot of people buy lottery tickets “because someone has to win, right?”

    We see similar wishful thinking in innovation management. Executives announce a new commitment to innovation. And the hope for a big win (like hitting the lottery). But they fail to put into place any system that would actually make their firm better at innovating.

    That’s the lottery mistake – someone has to win, right?

    The second error is misunderstanding averages. Pfeffer and Sutton discuss studies that looked at mergers and acquisitions through the 1980s and 90s. Over 70% of those that took place in that time period either failed to create value, or actually destroyed value in the firms involved.

    People make two mistakes with numbers like this. First, they will sometimes cite a counterexample, and then act as though that disproves the study. It doesn’t. The counterexample simply falls into the 30%. Statistics that show that something is true the majority of times don’t mean that they are true for everyone.

    The second mistake is to believe that this means that all mergers and acquisitions are doomed. Again, these results don’t apply to every one.

    In fact, we know a fair bit about what makes mergers and acquisitions successful. This excellent post from Accenture summarises the evidence well. They are more likely to succeed when:

    • One firm is bigger than the other: this reduces power struggles.
    • The two firms are geographically close to each other: M&A success decreases with distance.
    • There is a good cultural fit between the two firms: this is the key one. Mergers based on strategic or operational synergies tend to be less successful than those based on cultural fit.

    If you use this evidence, you can beat the odds. Throughout the period most of that research covered, Cisco acquired 57 firms, and nearly all of these acquisitions were successful in terms of building value.

    They didn’t do this by hoping that they would beat the odds. They did it by understanding them.

    That’s evidence-based management. Pfeffer and Sutton do a great job of explaining why you should try it yourself (check out Greg Satell on how to do this).

    The research shows that if you do, you’re more likely to succeed.

  • Don’t Be First to Market, Be First to Scale

    Often when people have an idea for a great new product or service, they rush to be first to market with it. We keep hearing about first-mover advantage and how you need it.

    The only problem with first-move advantage is that it doesn’t seem to exist. The academic research on the topic shows that there is no such thing.

    The first definitive work on this was done by David Teece in 1986 (pdf version of the paper here). He found that innovators capture about 20% of the profits generated by their new ideas. Followers and imitators capture slightly more. Suppliers get some of the benefit, but the big winners are customers, who get about 40% of the benefit of new ideas.

    The study was done 25 years ago, but subsequent work has consistently found similar results.

    Part of this is because innovations diffuse across an S-Curve – and this usually takes longer than we expect it to.

    How can innovators try to capture more of the profits generated by their great ideas? It’s not by being first to market. In his excellent new book Sidestep & Twist: How to create hit products and services that people will queue up to buy,James Gardner suggests that one way to address this problem is use network effects to accelerate the S-Curve – to be the first to scale.

    He uses the five factors that Rogers identified as the key characteristics that drive adoption, and reframes them for the network economy. His thesis is that by taking advantage of networks, you can move through the slow part of the S-Curve more quickly. This increases your chance of winning.

    The five factors are:

    1. Observability: how easy is it for people to see how the innovation works? In networks terms, does usage increase through viral effects? Think about the social games on Facebook, like Farmville. Every time one of your friends played Farmville, you get an update about it. This continues until you either start playing Farmville yourself, or you block all updates related to it. That’s observability.
    2. Trialability: how easy is it for people to try out the new idea? In a network, trials are experiments. The more people test out your idea, the more likely it is that emergent properties will show up. This is how you find unexpected new uses for your idea, which expands your number of users, moving you through the S-Curve more quickly.
    3. Consistency with how we currently behave: can we fit the new idea into our existing routines? Or does it require us to do new things? The former leads to faster adoption. Within a network, this leads to herd behaviour. Gardner’s example of this is Amazon’s recommendation engine, where the more people use it, the more valuable it is, leading to more people using it.
    4. Relative Advantage: is your new idea substantially better than what it’s competing against? One way to get this is to build in effects where the more people use it, the better/cheaper/faster it gets. Think about Amazon’s recommendation engine again. Or Google. Google wasn’t the first search engine (not even close), so how did it come to dominate? By being substantially better than the others, and by using an algorithm that improved search results the more people used it.
    5. Complexity: is the new idea simpler to use than its competitors? In network terms, does increasing use lead to great simplicity? For this, Gardner talks about how “crowds of people, learning and sharing together, make it less difficult for others to join their crowd.” His example is Wikipedia – which is trying to catalog a huge amount of knowledge. The more people participate in Wikipedia, the easier this gets. And their value proposition improves.

    I love the idea of using network effects to move through the S-Curve more quickly. This quick recap doesn’t do the idea justice – it’s worth checking out the book to learn more.

    In the meantime, if you are innovating and you are early in the diffusion curve, ask yourself this: how can I take advantage of networks to increase the use of my great idea? If you think about this in terms of the five factors from Rogers, your chances of capturing the value that you create will increase.

    So don’t try to be first to market. Try to be the first to scale.

  • Two Problems Caused by the Innovation Diffusion Curve

    The economist Rudi Dornbusch succinctly describes the way that ideas spread:

    Things take longer to happen than you think they will and then they happen faster than you thought they could.

    It’s the innovation S-Curve in words, this is what that looks like graphically:

    And the problem is that the value for X is larger than we expect it to be – that’s the essence of Dornbusch’s quote.

    I ran across the quote in a post by Andrew Hargadon discussing how sustainability in business is taking longer than expected to arrive. Hargadon explains why X is big:

    Forget all the names and dates you learned in elementary school, great social and technical revolutions begin with a whisper, not a bang. They take decades to develop and then, when they do, they change everything overnight.

    Take the industrial revolution. It started with a whisper: three different technologies slowly emerging in the 1700s. Coal slowly replaced wood as the dominant source of fuel; the steam engine slowly replaced animal and wind power (to pump water from coal mines); and large ironworks slowly replaced local craftsmen and blacksmiths. For decades, these technologies and the businesses and lifstyles that surrounded them grew and evolved. Then, all of the sudden, the last few decades of that century and the first few of the next saw an explosion of innovation across all industries—from textiles to shipping to railroads to iron and metalworks.

    The impact didn’t come from any one of these technologies, it came from the interaction between them.

    This slow diffusion causes two problems for firms. The first is that if you are a powerful incumbent, you see the slow diffusion and you think that it will continue to expand along path C – slow and steady. The consequence of this is that when the change does happen, even though there have been warning signs for ages, it still takes you by surprise.

    There is a quote from the CEO of a major book store in Game-Changing Strategiesby Constantinos Markides:

    We were late in implementing [the web] but not in evaluating it. And our evaluation was that this thing did not make sense. yet every time I tried to explain our reasons why we wouldn’t do it to Wall Street, my share price went down! Even in 1997 when online distribution of books went from zero to 6 percent, superstores increased their share from 10 percent to 22 percent – yet our stock price dropped by 40 percent. So in the end, we decided we had to do something.

    This is exactly what path C thinking sounds like. And the problem it leads to is this (via Boing Boing):

    But there are also problems for innovators in the S-Curve. The long delay in diffusion causes a lot of firms to go out of business trying to catch the new wave.

    You can see this in the Kodak case. Here’s the world’s first digital camera:

    It was invented by Kodak in 1975. The problem was, the rest of the economy wasn’t ready for digital cameras yet. Digital memory was still so expensive that you couldn’t actually take usable photos then. Does anyone else remember how crappy the first digital photos were in the late 90s? They were just awful. The supporting technology didn’t catch up with the camera technology for about 25 years.

    That is exactly what Hargadon is talking about – it takes multiple innovations to disrupt an industry.

    Kodak took this to mean that digital cameras would evolve along path C. So they kept the technology on the shelf and waited. If an independent entrepreneur had invented the digital camera, he or she would have gone bankrupt waiting for the supporting technologies. Or, if they were lucky, they might have sold the rights to a big company like Kodak.

    The point is, when you’re early in the S-Curve, it usually takes a lot longer to get to the tipping point than you’d like it to.

    The first step in addressing these problems is being aware of them. However, there are also some positive steps that you can take as well. I talk more about these here.

  • So Where Do Good Ideas Come From?

    I ran across an outstanding post today by John Battelle reviewing Where Good Ideas Come From: The Natural History of Innovation.by Steven Johnson.

    It’s one of my favourite books from the last couple of years, and Battelle does a great job of highlighting the key points in it. He also reminded me of a table that Johnson put in towards the end of the book. It looks at the genesis of what he thought were the most significant ideas of the 19th and 20th centuries. He then assessed whether they were developed in commercial firms or non-commercial organisations, and whether they were generated by individuals or by networks of people.

    Here’s the table:

    This illustrates several important points:

    • It’s never either/or, it’s both/and. Are individuals or networks more innovative? Networks – there’s plenty of research to back this idea up. Nevertheless, individuals still generate plenty of big ideas. It’s the same with market vs. non-market – lots of great ideas come from both. More come from non-commercial environments.

      It’s really easy to argue black and white statements (“Only small firms innovate!” “No – only big ones do!”). But they’re never true. In order to support innovation, we need to look at these dichotomies and figure out which circumstances favour one approach over the other. And then we need to support both. This is true for market vs. non-market, for individuals vs. networks, for big firms vs. small firms.

      Black and white thinking is dangerous.

    • Networks are a critically important source of great ideas. The lone inventor idea is still with us. Here is what Johnson says about networks:

      Ideas rise in crowds, as Poincaré said. They rise in liquid networks where connection is valued more than protection. So if we want to build environments that generate good ideas—whether those environments are in schools or corporations or governments or our own personal lives—we need to keep that history in mind, and not fall back on the easy assumptions that competitive markets are the only reliable source of good ideas. Yes, the market has been a great engine of innovation. But so has the reef.

      The second part of that quote leads to the next point:

    • Non-market organisations are critical components of the innovation ecosystem. Many of the ideas that led to you being able to read this blog post came from non-market networks – the computer and the internet being chief among them. But just to illustrate the first point, smart phones, which aren’t in the table, came from a market network. Nevertheless, it’s important to understand how crucial non-market organisations are to generating big ideas.
    • Most big ideas get turned into innovations by the market. Here is what Battelle says:

      This doesn’t mean those ideas don’t become the basis for commerce – quite the opposite in fact. But this is a book about how good ideas are created, not how they might be exploited. And we’d be well advised to pay attention to that as we consider how we organize our corporations, our governments, and ourselves – we have some stubborn problems to solve, and we’ll need a lot of good ideas if we’re going to solve them.

      Effectively connecting non-market organisations with market-based firms is one of the most important roles of government. In regions that innovate well, these two sectors interact more effectively than in less innovative regions.

    Invention and innovation are two different things. However, we still need to start with a great idea to innovate well. Understanding how good ideas originate is an important part of doing this.

  • Replace Fear of the Unknown With Curiousity

    The Shift Index 2011 is out now, and as with the previous two editions, it is a must-read.

    I am always skeptical of “everything is different now” type arguments, but in this series of reports, John Hagel, John Seeley Brown and a number of other contributors have done a fantastic job of documenting exactly what is changing. It might not be everything, but it’s a fair bit.

    Here are the four key points that they make in the summary of this year’s report:

    • ROA (Return on Assets) performance continues its long-term decline due to deteriorating firm performance
    • Layoffs and other short-term measures taken by firms are not a sustainable solution to improving long-term firm performance
    • Connected individuals, not companies, are the ones harnessing flows and have more power because of it
    • Firms have untapped opportunities to reverse their declining performance by embracing pull

    Hagel and Seeley Brown have a number of recommendations about how to deal with this in their book The Power of Pull
    (discussed here and here). It’s one of the best books of the past couple of years, and I recommend it.

    Another book that deals with these issues is Futuretainment: Yesterday the World Changed, Now It’s Your Turnby Mike Walsh.

    Replace Fear

    The book is interesting. Here is one of the key points that Walsh makes in it:

    Sometimes the best way to win a game is to question why you are even playing it. The rules that govern industries are rarely made in advance – they evolve in periods of rapid change until eventually they themselves become restraints on innovation. But there is one thing you can be sure of: when consumer behavior changes, sooner or later business behavior must follow. The future is already here, you just need to know where to look.

    The book itself is a great example of trying to invent the future. Walsh has deliberately made a book that only works as a physical thing. It has a gorgeous set of photos taken by Walsh (including the one above) as the background on each page. Then it has series of insightful chapters discussing the implications of the big shift. Here is how he describes the approach:

    The first question my publisher asked me was why a book and not a blog? Three years ago when I started working on Futuretainment, that was already a tough question to answer. With eBooks now on the crest of critical mass, it hasn’t got any easier. Last week, my book hit the shelves. Although you can buy it on Amazon, you can’t read it on a Kindle. In fact, with 300 pages of illustrations, original photographs and custom designed typography – it is about as Kindle friendly as a bathtub. That was a deliberate decision on my part, but it comes at a time when the very concept of a book is changing.
    …
    There are two aspects to any book. First, there is the book as an informational construct. Put simply – an arrangement of words, sentences, paragraphs and chapters. However in our attention drained world of 140 characters, this construct increasingly boils down to a simple image – the long tail, the tipping point or the black swan. Despite fervent claims to the contrary, the vast majority of people don’t actually read books. They consume metaphors and debate in status updates.

    Fortunately, there is also a second aspect of books – ‘thingness’. Whether a Sumerian stone tablet, an Egyptian papyrus, an illuminated Medieval manuscript or just a pulp paperback – there is a physical side of books which has its own life.
    …
    Because, as much as I love my Kindle, it is a marriage of convenience. My true mistress will always be books. The smell of print, and the sensual touch of high quality paper will never fail to seduce me. And I can only hope that my book might elicit the same response in you.

    Unlike Jonathan Franzen, who recently discussed why books need to be physical without offering much more of a reason than “because I like them”, Walsh has made a book that demands to be instantiated physically.

    eBooks are a great response to the informational side of books that Walsh discusses. Seth Godin wrote a great post yesterday about how to deal with this.

    But to deal with the ‘thingness’ of books, you need a new business model. You need to create value not just in the words, but in the physical object as well. Walsh has succeeded in both aspects of his book.

    What should the rest of us do? Maybe it’s time to heed Jorge Barba’s advice and get an MBA in curiousity.

  • Innovation Problem: New Ideas Spread Slowly

    There’s a big problem with innovation: ideas spread much more slowly than we expect them to.

    Ideas follow an S-Curve as they spread that looks like this:

    They pick up steam very slowly, until they either die off or hit a tipping point and take off. The slow build-up is the time I’ve indicated as X in the drawing.

    The idea for the S-Curve is based on the great work by Everett Rogers on innovation diffusion.

    Based on his research, the population of users is divided into groups he called innovators, early adopters, the early and late majorities, and the laggards. In the populations that he looked at, the percentages of people in each group look like this:

    You can see these numbers in the survey that Sophos Security released last week on the reactions of Facebook users to the new timeline feature:

    This is being presented as a big problem for Facebook, but if you look at the numbers, they’re actually better than the stats from Rogers would lead us to expect. The survey doesn’t include the laggards, who probably still aren’t on Facebook, but the rest of the numbers map onto Rogers’ pretty well.

    All of the people that hate the new timeline want to go back to the News Feed, another feature that had even worse approval numbers when it was introduced. And now people love it and don’t want it to change.

    That’s the way that ideas spread. People resist, a small number adopt, and eventually over time, the idea wins. If you’re lucky.

    There was another story over the weekend about the diffusion of Edison’t incandescent lightbulbs that tells the same story.

    Here is what they say about adoption of electrical lighting:

    By 1910, more than 30 years after Thomas Edison invented the incandescent bulb in 1879, only about 10 percent of American homes had been wired. Even in the glittering Roaring Twenties, only about 20 percent of homes had electricity — not because of a lack of electrical contractors, but because of a lack of consumer enthusiasm.

    Advertisers proclaimed that homes with electricity would be brighter, cozier and happier, but the public wasn’t buying.

    And this is for a product that was demonstrably better, cheaper and safer.

    Again, the value for X was much longer than expected.

    This is an issue that is addressed extremely well by James Gardner in his excellent new book Sidestep & Twist: How to create hit products and services that people will queue up to buy.

    The book is worth reading and Gardner does a great job of explaining the S-Curve and its implications. One of the key outcomes of this is one that makes a lot of the people that have encountered Gardner’s ideas uncomfortable: breakthroughs don’t pay.

    The long X shows us why. It takes so long for new ideas to spread that whoever introduces them is not always set up to capture the value from them.

    This is kind of scary, because those of us that generate ideas want to think that a great idea will win. But they don’t automatically. One point that he makes is that you work around this by building on existing ideas:

    A lack of genuine originality is a feature of almost every category-defining product in the last decade. Was Facebook the first social network? Certainly not: MySpace, Friendster and a host of others preceded it. In fact, the first real social network was a site called SixDegrees.com, and it was founded a decade before Facebook’s meteoric rise began. Was it Google that created web search? Of course not: the company’s contribution was to improve what Alta Vista and the other web search engines that had pioneered the field were doing already.

    I could spend pages and pages going through examples like these, and will do so later on in this book. But one thing unites all these products and services: they’re built on something that was working well somewhere else.

    Gardner has more good suggestions about what to do about this, and I discuss these more here. But for today, I just wanted to take the Facebook and Edison examples to illustrate the problem that we are trying to address. If you are trying to get ideas to spread, you must develop a good understanding of the idea diffusion S-Curves and what they mean.

    The fact that ideas spread slowly is crucially important to understand. It is part of what makes it difficult to win through innovation. This is why we must manage innovation as a process.

    It’s dangerous to think of innovation only as generating new ideas. That’s not enough. You also have to get the great ideas to spread. They spread through S-Curves, and we have to include these when we develop our innovation strategies.