Archive for category experiments
How to Take Advantage of Surprises
Posted by Tim in book riffs, experiments on 21 February 2012
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
Posted by Tim in book riffs, design, experiments, innovation strategy on 19 February 2012
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:
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
How to Think About the Future
Posted by Tim in complex systems, experiments on 2 February 2012
Imagine that 100 of us have gathered together in a room somewhere. It’s a social event, but I want you to think about a couple of numbers.
If we took the average height of all of us, it would be somewhere around 1.76 meters. What happens to this average if we’re joined by Sultan Kösen, the tallest man (2.51 meters) in the world? Our average height goes up to 1.767 meters. In other words, the average increased by about 0.4%.
Now think about our average wealth. The stats vary, but average net worth in the US is around $120,000. What happens to this average if we’re joined by Carlos Slim, the richest man ($63.3 billion) in the world? Our average net worth goes up to $745,544. In other words, the average increased by 521%! And that’s after Slim lost $11b due to the GFC.
The difference between 0.4% and 521% is the difference between normal and complex.
Height is distributed normally, and in a normal system, the average dominates the extremes. The economy is a complex system, and in a complex system, outliers matter.
That picture is from The Behavior Gap: Simple Ways to Stop Doing Dumb Things with Money
Here is what he says about the importance of outliers:
[O]utliers matter. In fact, they matter so much that they almost make the average meaningless. Because most of our lifetime return is determined by how many of these outliers we experience, it is time we stop ignoring them.
If we’re trying to innovate, our job is to invent the future. So the fact that the economy is a complex system is important.
First off, it’s important because returns to innovation follow the kinds of returns that we see in the wealth distribution. The average return to executing a new idea is small, but a small number are gigantic. This is why it’s important to manage innovation as a portfolio.
Secondly, this has a big impact on how to think about the future. Complex systems are impossible to predict. This is a problem, since we don’t like uncertainty.
Here is how Martin King frames the problem:
The problem with long term developments are that they are subject to exponential and combinatorial factors – chaotic things that we are not good at understanding at the best of times. To compound things change cycles themselves are becoming faster.
Instead of thinking of the future as something to predict, we should think about it as part of a pattern. Greg Fisher wrote an outstanding post (read it!) discussing the importance of pattern recognition in complex systems. Here is part of his prescription:
There is a relationship between patterns and prediction. In fact, I would note that not only do patterns exist and persist, we must rely on them in every day life. We make decisions in the present assuming the persistence of some patterns e.g. I will withdraw £50 from a cash machine today for spending over the next few days. I do not expect everyone else in the UK to switch to the Thai Baht during that period. Furthermore, it is particular patterns – many of which we might call institutions – that are responsible for our civilised society and a relatively high standard of living.
But – and this is to assert the point further – it is important to emphasise that the world will change and so too will the patterns around us. By “expect to persist” in my definition of patterns I was referring to making reasonable judgments that some patterns will remain broadly the same over a particular period.
How should we respond to this? Geoffrey Morton-Haworth has written an excellent post on Learning From Patterns. First he talks about the most common example of a complex system: the weather. And he says:
We cannot control the weather but if we recognize its patterns we can manage around them. And we can do the same in complex relationships.
He then discusses the work that Edward Tufte has done on effectively assessing complex data. Morton-Haworth includes this worksheet from Tufte:
Here is how he concludes:
Tufte argues for good method. That is “a shrewd intelligence about evidence, a clear logic of display and analysis, placing data in the appropriate context for assessing cause and effect”. In short, he talks about the need for “a coherent architecture for organizing and learning from images”.
A complex relationship outlasts its components, just as the ant colony outlives the individual ant, and in so doing develops a purpose of its own greater than the free will of its parts. While individuals may only be involved for a matter of months or just a few years, a complex relationship can learn, change, grow and adapt over five, ten, fifteen or more years. Nevertheless, because our lives take place at lower levels, we frequently don’t know the contribution we make to complex relationships. But we can help its intelligence to emerge.
This is why it’s important to think about inventing the future. Another important feature of complex systems is that the systems co-evolve with their parts. In simple terms, small changes among the parts can cause large changes within the system.
These are the small number of innovations that end up having big impacts. And how can we find these? We can’t predict which innovations will hit big – knowing this is one of the important outcomes of thinking about the economy as a complex system.
Harold Jarche does a nice job of framing some of the issues here:
When we move away from a “design it first, then build it” mindset, we can then engage everyone in critical and systems thinking. Workers in agile workplaces must be passionate, adaptive, innovative, and collaborative. Autonomy is the beginning.
Instead of innovating based on prediction (design, then build), which leads to big bets, we need to innovate based on experiments. This leads to little bets.
In a complex economy, the way to think about the future is this:
- We can’t predict the future.
- But we can learn about the patterns from which the future will emerge.
- In fact, while we can’t control the future, we can influence it.
- The best way to influence the future is by innovating through experiments.
What experiment can you try today?
Three Things You Can Do With a Business Model
Posted by Tim in business models, experiments, innovation strategy on 17 January 2012
Yesterday I looked at Eight Models of Business Models and Why They’re Important. However, in writing about how different people conceive of business models, I didn’t have enough space to address the really critical issue with them:
What can you actually do with a business model?
Once you’ve defined your business model, here are three ways that you can do with it:
- Test Your Organizational Design for Consistency: one of the key issues in looking at business models is that they must be internally consistent. If your value proposition is that you’re the cheapest, this has a direct impact on the choices you need to make about who to hire, how to train them, the relationships you’ll have with customers, who your customers need to be, etc.
In response to yesterday’s post, Graham Hill said on twitter:
None of the business model work passes muster. There are dozens, maybe hundreds of dependent variables. Not repeatable.
This is a valid point, if your objective is to try to replicate someone else’s business model. Bob Sutton makes a similar point in a great review of Inside Apple: How America’s Most Admired–and Secretive–Company Really Works
by Adam Lishinsky. It is one of those reviews that is just a nice piece of writing – worth reading whether or not you’re actually interested in the book.
Sutton raises an important point:
Apple is nearly the exact opposite of the kind of organization hyped by people like Gary Hamel and even Peter Drucker. It is centralized, secretive, fear-ridden, punitive, and not much fun for most people who work there. But it works because the pieces of the “organizational design” fit together, or at least did fit together when Jobs was there, in an elegant way. The secrecy is so severe that, when products are launched, even senior people are surprised by the final product because people are on a strictly “need to know” basis. But this is offset with a system of roles and responsibilities — and crucial to all of it– is what Apple calls the DRI, the directly responsible individual, a centerpiece of the organization. There is clear responsibility placed on individuals, not so much on groups and committees. Although groups and some committees do exist, the DRI can always be found and is where attention is focused. Which means that that it is clear where to go to provide guidance, to integrate their work with others, and who will be fired, blamed, and replaced — and celebrated too.
…
My point here, and this follows an old conceptual perspective called “contingency theory,” is that other organizations that want to be like Apple –and that seems like so many now — need to be especially careful about copying individual pieces, because the reason it works is that the multiple elements fit together.The point here is to be wary of picking up one part of someone else’s business model and dropping it into yours. If the whole business model isn’t consistent, you’ve got problems. So unless you have Apple’s intuitive sense of what customers need, it’s very dangerous to say “Apple doesn’t do focus groups, so we won’t do focus groups.”
- Innovate the Business Model: Henry Chesbrough and Richard Rosenbloom tell two stories of business model innovation in the copier industry in their paper The Role of the Business Model in Capturing Value from Innovation. When Haloid Corporation tried to launch the first Xerox machine, they used the same business model as the mimeograph machines that they were competing against.
The initial launch of Xerox machines failed, because they cost six times the machines they were competing against. It took an innovation in the business model to succeed. Instead of trying to replace a mimeograph, Haloid decided to try to replace a secretary. This meant a new value proposition, a new market segment (only large firms), a new revenue model (leasing instead of purchasing), and so on. With the new business model, and with no change to the underlying technology, the Xerox machine took off.
Haloid Corporation changed their name to Xerox, and they dominated the market for nearly 30 years. Until another business model innovation started to seriously erode their market share.
Business model innovation is a powerful form of innovation. So once you’ve described your business model (or that of your industry), start thinking about how you can change it.
- Use it to Test Your Market and Your Assumptions: Steve Blank likes to say that a business model is just a set of hypotheses about the market. So you can use the business model to test your assumptions about what will work as you introduce new ideas.
Experimenting is a crucial part of innovation. You can use business model analysis to identify the assumptions that underlay your innovation – this tells what experiments you need to try.
Blank documents the process in his fantastic Lean Launchpad series, where he talks about nine teams in his entrepreneurship class at Stanford used the business model concepts to launch start-ups. Here is a description of one of the experiments:
The first team to present was D.C. Veritas, the team building a low cost, residential wind turbine. During the week they interviewed 7 more companies and consultants, developed case studies for 20 different cities in 5 states, and finalized the bill of materials for the wind turbine. But the big project for the week was testing and analyzing Customer Acquisition Costs. The team put together their sales funnel and started testing demand.
The results were disappointing. The most optimistic estimates showed that the residential wind turbine market was less than $20m in year 5 and the costs to acquire the customers made this a money-losing business.
After regrouping the team decided that a major pivot was in order. Perhaps residential customers were the wrong target? Maybe the wind turbine they were building was better suited to a different customer segment? They had gotten feedback from consultants and industry experts that cities and utilities might be a more receptive audience. What if they redesigned the wind turbine to be embedded into street and highway light poles? Then they could serve cities, lighting companies and utilities. Using the business model canvas, the changes to their business were obvious.The business model can be a great tool for guiding innovation experiments.
Yesterday we mainly talked about how business models can be described. Once you’ve described your business model, then what? These are three ideas – you can use it to: test your organisation design, innovate the business model itself, and define innovation experiments to test the assumptions of your firm.
Mimeograph photo from flickr/nicksarebi under a Creative Commons License.
Do Posts Asking if Something Kills Innovation Kill Innovation?
Posted by Tim in experiments, innovation strategy on 11 January 2012
One really good way to get traffic to your blog is to take a shot at a broad class of people, and do it with a catchy title. The latest version comes from FastCoDesign, which published the post “Do Innovation Consultants Kill Innovation?”
Gregg Fraley and Jeffrey Phillips wrote quick responses, both affirming the negative.
I tend to agree with Gregg and Jeffrey. But the authors of the original post, Jens Martin Skibsted and Rasmus Bech Hansen, do raise an important point – which is that big firms need to move from innovation-thinking to innovation-doing.
In their recommendations, Skibsted and Hansen make the same mistake that these ineffective big firms often make – they mistake creativity for innovation. All of the post makes this conflation:
People with strong, creative talents are essential to the development of innovations, and the difference between success and disaster is largely defined by the selection of a good team–not by its processes. Just as a company can hire an ad agency or designer to create an ad or a product, companies in all industries need to find ways to tap into a network of people, small companies, or institutions with real inventions and show them some faith.
Sometimes a company will have to breed and nurse the talent itself. Sometimes the talent are guns for hire. But companies should have the confidence to give them the freedom to explore the high-risk messiness and the fuzzy, nonlinear ways in which innovation grows.
Let’s say for a moment that creativity is purely the realm of creative genius – I don’t necessarily agree, but we’ll grant that for a minute. The innovation problem that big firms have isn’t a creativity problem – it’s an execution problem. Here is what Phillips says about this:
The authors have a point – some innovation can be risky, messy and non-linear. But that doesn’t mean the entire innovation capability should be left completely to chance! For anything to get done in a modern business, someone needs to be responsible and there needs to be some structure, some knowledge and some best practice. We can’t wait for the immaculate conception of innovation – we need to provide knowledge, tools, understanding and some people and process who understand how these things work.
The common issue here is that people always forget Sturgeon’s Law – 90% of everything is crud.
Do innovation professionals innovate poorly? Most of them probably do, maybe even 90% of them.
Do innovation consultants give people bad or useless advice? Most of them probably do, maybe even 90% of them.
My advice from the last time I talked about this still holds:
Nothing is always absolutely so.
Now, that’s a really bad point to try to build a blog post around. It’s always a lot harder to explain why there are exceptions to every rule. It’s easier to make big categorical statements. It’s more fun, it’s easier to make lists out of them, they get more tweets, and +1s, etc.
It’s a lot harder to figure out how to identify the 10% of something that isn’t crud. But if you’re looking for a management consultant, here are some of the questions you can ask that might help:
- Do they have experience with my type of problem?
- Do they use one-size-fits-all tools or do they really learn about what’s going on inside an organisation?
- Do they only focus on the easy part (pointing out what’s wrong), or do they have useful things to say about execution as well?
That’s just a start, and you can build a similar list of questions for everything.
The concern that I have about the original article is this: the authors are taking aim at the two groups that are most likely to care the most about improving innovation inside of firms. Demoralizing these groups may well kill innovation – and that’s not good.
Innovation is Impossible
Posted by Tim in book riffs, experiments, variety on 5 January 2012
James Altucher recently suggested that “Eat All You Want of the Foods You Love and Still Lose Weight” would be a great book title – that no matter what was inside, it would sell. It’s easy to see why. Many of us like to eat all we want of the foods we love, and we also want to lose weight, so if we could do both at the same time, wouldn’t that be great?
Well, maybe.
In his new book Relentless Innovation: What Works, What Doesn’t–And What That Means For Your Business
Everyone understands from the beginning how difficult it is to create compelling new ideas in any sutation, uch less to convert those ideas into viable products and services. To compound the difficulty, executives are asking for disruptive ideas while expecting the business to continue to operate at full effectiveness and efficiency. Middle managers receive these messages and understand the unspoken dichotomy in the request: create radical, valuable new products and services but don’t upset the status quo.
Phillips nails the problem – many firms want an innovation program create radical, valuable new products and services but don’t upset the status quo.
If that’s what you want, innovation is impossible.
Relentless Innovation is a very good book. One of the key points that Phillips makes is that one of the major obstacles to innovation is the emphasis that many firms have on efficiency. You can innovate to become more efficient, and many firms do this well. However, to be successful over time, you also need to develop new products and services, and you can’t do this just through efficiency.
Here is a big part of the reason for that. Efficiency is all about reducing variation. When you’re a manufacturer, and you’re using statistical process control to improve the quality of your products, then this is great.
However, innovation that creates new products and services, requires increased variation. You have to try things that you’ve never done before, experiment, fail, learn, and get feedback from customers. This is the diametric opposite of increasing efficiency. Here is how Phillips puts it:
You must shift your thinking to recognize that experimentation and prototyping is as much about discovery and new insights as it is about validation of internal perspectives and theories. Your firm must make it far easier to test ideas, gain new insights, and “fail forward.”
In addition to increasing your experiment rate, Relentless Innovation includes a number of other practical steps you can take if you find yourself in a situation where innovation is impossible (you can check out Jeffrey’s blog too for more – Innovate on Purpose).
To innovate well, you have to become comfortable with disturbing the status quo. Deborah Mills-Scofield addresses this very well in a recent post – listing status quo objections to innovation and good response to each.
You also have to be able to maintain a focus on efficiency while also generating great new ideas. Efficiency reduces variation, but great new ideas increase variation. This is another of the ten tensions in innovation that must be balanced. In each of these situations, you need to think “both-and”, rather than taking an “Either-or” approach.
If you want to innovate without changing anything, then innovation is impossible. To get around this problem, you need to align innovation with your strategy, and build a capability for innovating consistently within your organisation. It’s not easy, but it is possible. Relentless Innovation gives us some good ideas about how to do this, and that makes it worth a read.
Note: If you want a more conventional review of the book (I’m lousy at reviewing), check out this one by Jorge Barba.
Disclaimer: I know and like Jeffrey, and I received a free pdf of the book. I also bought my own copy. I’m writing about the book because of its quality, not because of who wrote it or how I got it.
Innovation Obstacle: Gumption Traps
Posted by Tim in book riffs, experiments on 29 December 2011
Imagine that you have a great idea for how to make things work better at your job – it shouldn’t take too much effort, I’m sure you have plenty. Now think about an idea like that you had, but never acted upon – what happened? You probably thought of all the obstacles to executing the idea. People won’t go for it, they hate change, my boss won’t let me try it out, the organisation is too risk-averse, I didn’t get any recognition or support for the last idea I had, and so on. So you decided that the situation wasn’t really that bad, you could live without improving it if you really had to.
You just fell into a gumption trap.
Gumption Traps
Joe McCarthy talks about Gumption Traps in a series of typically excellent posts. The idea comes from Robert Pirsig’s Zen and the Art of Motorcycle Maintenance: An Inquiry into Values. Here is what Pirsig says about gumption and Gumption Traps:
I like the word “gumption” because it’s so homely and so forlorn and so out of style it looks as if it needs a friend and isn’t likely to reject anyone who comes along. I like it also because it describes exactly what happens to someone who connects with Quality. He gets filled with gumption.
A person filled with gumption doesn’t sit around dissipating and stewing about things. He’s at the front of the train of his own awareness, watching to see what’s up the track and meeting it when it comes. That’s gumption.
…
Throughout the process of fixing the machine things always come up, low-quality things, from a dusted knuckle to an accidentally ruined “irreplaceable” assembly. These drain off gumption, destroy enthusiasm and leave you so discouraged you want to forget the whole business. I call these things “gumption traps.”
There are hundreds of different kinds of gumption traps, maybe thousands, maybe millions. I have no way of knowing how many I don’t know. I know it seems as though I’ve stumbled into every kind of gumption trap imaginable. What keeps me from thinking I’ve hit them all is that with every job I discover more. Motorcycle maintenance gets frustrating. Angering. Infuriating. That’s what makes it interesting.
I highly recommend reading all of McCarthy’s post on this, but here is part of what he says about Gumption Traps:
Pirsig uses motorcycle maintenance as a metaphor for life, and explores a variety of gumption traps – externally induced out-of-sequence reassembly, intermittent failure and parts problems as well as internally induced traps arising from value rigidity, ego, anxiety, boredom and impatience – and ways of addressing and overcoming them.
In innovation, we have both internal and external Gumption Traps.
How to Avoid Gumption Traps
There are a few things that we can do to avoid Gumption Traps. The first is that we have to be doing something that we believe in. This provides powerful motivation to act on our ideas. As is often the case, Hugh MacLeod captures this idea perfectly:
There are also some very useful steps to follow in Nine Things Successful People Do Differently
The nine things are not really surprising, but they are powerful. The one that most directly addresses the Gumption Trap is number 6: Have Grit:
Grit is a willingness to commit to long-term goals, and to persist in the face of difficulty. Studies show that gritty people obtain more education in their lifetime, and earn higher college GPAs. Grit predicts which cadets will stick out their first grueling year at West Point. In fact, grit even predicts which round contestants will make it to at the Scripps National Spelling Bee.
The good news is, if you aren’t particularly gritty now, there is something you can do about it. People who lack grit more often than not believe that they just don’t have the innate abilities successful people have. If that describes your own thinking …. well, there’s no way to put this nicely: you are wrong. As I mentioned earlier, effort, planning, persistence, and good strategies are what it really takes to succeed. Embracing this knowledge will not only help you see yourself and your goals more accurately, but also do wonders for your grit.
Grit and Gumption are pretty much the same thing. Grit is an important part of innovation. Grit helps us learn from experiments that fail, instead of despairing. Grit helps us push our ideas even if the boss doesn’t directly support them.
Grit helps us change the world, if that’s what we’re trying to do. And we should be.
The next time you face a Gumption Trap, if you’re not finding a way around it, think of Halvorson’s nine ideas. They can help you change the world.
Two Reasons Why You Must Change Your Mind
Posted by Tim in evolving economic entities, experiments, time on 22 December 2011
One of the frustrating things about following politics is the idea, apparently deeply engrained, that you must never change your mind. If you do, you’re a flip-flopper, or wishy-washy, and you’re clearly not to be trusted.
The main problem with this line of thinking is that it is utterly and dangerously wrong. We live in a dynamic world, and our brains are dynamic – if you’re not changing your mind all the time, it’s a danger sign.
There are two very good reasons to change your mind: the facts have changed, or you have learned something.
Changing Facts
To those of us that take innovation seriously, Joseph Schumpeter is the patron saint of economists. He was the first person to really articulate the importance of innovation and how central it is to economic growth. Just to give you an idea of how important he is, here is a picture of picking out a new kitten last year, who is now named Schumpeter!
One question that Schumpeter considered in his first groundbreaking book, The Theory of Economic Development, is this: which type of firm is more innovative – small or large?
It’s a question he kept coming back to. Here is how Adrian Wooldridge put it in The Economist (and in another signal of the regard in which Schumpeter is held, his weekly column there is called “Schumpeter”):
Joseph Schumpeter, after whom this column is named, argued both sides of the case. In 1909 he said that small companies were more inventive. In 1942 he reversed himself. Big firms have more incentive to invest in new products, he decided, because they can sell them to more people and reap greater rewards more quickly. In a competitive market, inventions are quickly imitated, so a small inventor’s investment often fails to pay off.
Now, the big or small question is still interesting, but that’s not what I’m concerned with today. Instead, look at how he phrases this – “Schumpeter… argued both sides of the case.” This idea often comes up, and people usually try to say that Schumpeter was being slippery by trying to have things both ways.
But here’s the thing – Schumpeter changed his mind because the facts changed. In 1909, big firms didn’t innovate at all. The largest firms were mostly extractive. Nearly all new ideas came from smaller firms. Corporate R&D was just starting at the time, in Edison’s workshop and in the labs of the chemical companies that were trying to make new dyes for clothes.
A lot changed between then and the 1940s, including the innovation process. By the middle of the century, invention and innovation both were dominated by large corporate R&D. That was the birth of the mass market, an economic environment built by and favouring large firms.
Schumpeter changed his mind because the facts changed.
Learning Something
Here’s a quote attributed to John Maynard Keynes:
When the facts change, I change my mind. What do you do, sir?
One of the implications implicit in that quote is that Keynes was always right. Unfortunately, most of us aren’t as infallible as he was. So we have to learn by being wrong.
This is a crucial innovation skill. We have a hypothesis about how we can make the world a better place – we have a great idea. The only way to turn it into an innovation is to experiment.
Often, our initial assumptions are wrong. By experimenting, we figure out which ideas work, and which don’t – we learn. And by learning, we change our minds.
Dynamics Minds for Dynamic Times
We live in a dynamic world. More importantly, we are learning machines. Both of these facts mean that we should be changing our minds all of the time. Rather than being a sign of weakness, a changed mind is a sign of someone that knows something more than they used to.
We should be learning all the time. Changing your mind is a sign of learning. We shouldn’t avoid it, we should seek it out. As Edward de Bono says:
If you never change your mind, why have one?
The Exigency of Extrapolation
Posted by Tim in complex systems, experiments, selection on 12 December 2011
Noun 1. exigency – an unstable situation of extreme danger or difficulty;
I’ve had some jobs in which I’ve performed pretty well, and some where I haven’t been quite so good. Probably the worst job I’ve ever done was part of my portfolio when I was managing sales & marketing for a polytechnic in New Zealand. The specific job was new course evaluation.
We put in a modified stage-gate type process to evaluate potential new courses. It was my job to fill in the numbers. The one number that drove everything else was the expected number of students. If the expected number of students was high, we’d try to run the course. If not, we’d kill the proposal.
I developed a very elaborate model, based on historical data. I knew how many enquiries we could generate from advertising, how many of those we could convert into applications, and how many enrolments we got per application. In fact, figuring out these numbers was one of the biggest innovations that I executed there, and this model was of tremendous use in trying to figure out around November how many total students we could expect at the start of the new school year the next February.
However, the model was terrible at predicting new course enrolments. Why? In large part, because we’re really lousy at figuring out how something new will perform. We rejigged our new course approval process after I pointed out that we hadn’t approved a single new offering in over 6 months – our process was killing everything.
I was originally going to call this post The Perils of Prediction, but Greg Satell beat me to that title. Also, the specific problem that I’m talking about is really extrapolation. You should read all of Greg’s post, but here’s part of what he says:
The problem starts when smart people in nice suits and lab jackets proclaim that “the data says…” In truth, the data never says anything. We interpret it in one way or another and there are lots of ways to interpret it incorrectly.
Data is, after all, messy. It doesn’t spring forth whole, but must be collected in some way. We count, measure, survey, aggregate, slice and dice, picking up errors all the time. We need to make choices about which data we want to focus on and which fades into the background.
How do we deal with this? Usually by finding some numbers from the past and extrapolating them. However, there are a few problems with this approach, including:
- We tend to think in straight lines, but there aren’t any straight lines in business: that’s really the point being made by the xkcd cartoon. Taking a straight line and extrapolating it into the future almost never gives us the right answer.
- It’s really hard to tell what kind of curved line we’re on: this complicates things too. Even when we have historical data, it is nearly impossible to figure out what kind of engine is generating the output. Take a look at this data from an interesting post on climate change:
Is it likely that the data will progress in a straight line? Or will it level out at some point? Or will it increase exponentially? We don’t know. But when we’re predicting, it pays to consider what circumstances might lead to each of these outcomes.
- Even when things are accelerating quickly, they tend to level out: innovations spread through an s-curve, and this is a very common pattern in business.
This is one of the issues with everyone talking about the singularity – it assumes that exponential growth will continue forever. It might, but usually exponential growth levels out, and then it looks like an s-curve.
- However, by the far the biggest problem with extrapolation is that if we depend on extrapolation for predicting, we will never anticipate something new happening: extrapolation can only predict that things in the future will be mostly like things in the past. Here’s how Greg puts it:
And that’s what most analysts miss. The future is hard to predict not just because of our cognitive biases or inexplicable natural events, but because we have the power to make our own future.
The first new course that we approved at my Polytechnic after we scrapped the stage-gate process was a program that offered free computer and internet lessons to people in the community, particularly targeted at older adults. And the number of enrolments that we got went so far beyond anything that we had ever seen before that it was almost impossible to believe.
None of my models could have predicted that. When we innovate, our job is to invent the future. The exigency of extrapolation is that if that is the tool we use to predict, we won’t be able to invent anything that doesn’t already exist. And what kind of innovation is that?
What is Influence, Really?
Posted by Tim in connect, experiments, networks on 11 December 2011
One of my colleagues is doing research on social network use, and she asked me to help get people to take her survey. It takes about 8 minutes to fill it in. I was glad to help, and to do it, I set up a test.
First I posted the link on my Facebook page and asked my friends to take the test. About 20 out of 188 did so.
A few days later, I posted a link on Twitter, and asked everyone following me to take the test. Another 20 did, out of around 3000.
And now, here’s an interesting question: how do you blog readers stack up against my Facebook Friends and Twitter Followers? My bet is that you’ll win – to prove me right:
Click here to take the survey yourself!
The contrast in results between Facebook and Twitter illustrates some important lessons about influence. A lot of people have been talking recently about how to best measure online influence. Like innovation, influence is another thing that is awfully hard to measure.
One big problem is that influence is pretty hard to define in the first place. What does it mean? To me, influence is about getting people to take action. If that’s the case, you might think that I am lot more influential on Facebook (where about 11% of the people on my list of friends took the survey) than I am on Twitter (where about 0.7% of the people on my list of followers took the survey).
But I’m the same person – so am I influential or not?
One of the best thinkers around right now on the topic of influence is Valeria Maltoni – here is what she says about Klout’s attempt to measure influence:
I can tell you that Klout knows squat about me and my behavior. Zero, nothing, niente, nada. Got it? The fine folks behind the algorithm have no idea of who gets my emails and calls, which are the tools I use most to conduct my real business.
They know nothing about what I read and why I read it, because they are not reading these articles or talking with me. They are just tabulating the keywords and volume of my Twitter activity. Twitter. Shrink me into 140 characters. Or maybe they are 134 more than those in Pirandello’s play (more context was the lesson there, it is here, too).
Are the people in my life even on Twitter? You don’t know that.
Am I the person you read here every day? (And I thank you humbly and sincerely for reading and thinking about this content.) You are not just the person who is reading. You are much more than one thing you do, so why would I be just the person who is sharing here?
Martijn Linssen has done a lot of good work assessing the success of Klout in measuring influence.
This experiment illustrates some important points about influence:
- You can’t reduce a complex phenomenon to a single number: influence happens in person, online, with people we know well, and with people we’ve never met. This makes it very tricky to measure. This leads to:
- Don’t mistake the metric for what you’re trying to measure: the real problem with things like Klout is that once we have a metric, people will start trying to game the metric. You can do this, but it doesn’t increase your actual influence. The only way to do that is to do things that have a strong, positive impact on people, and to do it consistently. That’s a system that you can’t game – and if you focus too much on managing the metric, you’ll actually get worse at the thing that really matters.
- Influence really happens in networks: Duncan Watts has done a lot of excellent research that shows that the main thing that causes ideas to spread within networks is the extent to which the people in the network are likely to spread the idea. Here is how he put it in a recent post:
When we hear that a raging forest fire has consumed millions of acres of California forest, we don’t assume that there was anything special about the initial spark. Quite to the contrary, we understand that in context of the large-scale environmental conditions — prolonged drought, a buildup of flammable undergrowth, strong winds, rugged terrain, and on so — that truly drive fires, the nature of the spark itself is close to irrelevant.
Yet when it comes to the social equivalent of the forest fire, we do in effect insist that there must have been something special about the spark that started it. Because our experience tells us that leadership matters in small groups such as Army platoons or start-up companies, we assume that it matters in the same way for the very largest groups as well. Thus when we witness some successful movement or organization, it seems obvious to us that whoever the leader is, his or her particular combination of personality, vision, and leadership style must have supplied the critical X factor, where the larger and more successful the movement, the more important the leader will appear.
- Consequently, understanding how ideas spread through networks is essential to understanding influence: this is an idea that Greg Satell has incisively written about. Here’s what he says:
In effect, starting an epidemic is similar to a broadcast search. You are better off casting your net as widely as possible and reaching influential people as well as less influential ones. (See this article for more about broadcast and directed network searches)
Some paths will fail, but the more paths you initiate, the more likely that your idea will infect those who are susceptible to it. Just like delays at any airport can affect large hubs, influence can originate anywhere in social networks.
So the real answer to the question of whether or not I’m influential is: yes. Or no. Or maybe. The one thing that we can say is that my Facebook friends seem to be a lot more willing to act on a request for help than my twitter followers are. But this again is a network effect, and doesn’t actually have that much to do with me personally. The connections on Facebook are different, and people use that network to meet objectives that differ from those that Twitter users are trying to achieve.
Influence is very important, but measuring it is hard. The best way to increase your influence is to keep producing ideas that help people.
Also, if you could retweet this, that would be cool – it would really help my Klout score…
(just kidding – I’d much rather have you fill in Sabine’s survey – and the number of people that have gone to the survey from here is now higher than we got from either Facebook or Twitter!)
(that’s The Minutemen playing their great song Take Our Test)
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Tim was named #5 in the Top 40 Innovation Bloggers in 2011 at Innovation Excellence.
Tim and John were both named in the Top 40 Innovation Bloggers in 2010 at Innovation Excellence.
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