Thirty years ago, I was a product manager for a startup company that created high-performance, multiprocessing minicomputers. Powerful, scalable, and based on open standards, they offered exceptional price/performance. In 1988, Electronics magazine gave its Computer of The Year award to the flagship model
As we introduced the system, we described the “ideal” customer: a medium to large organization that used Unix-based systems, and ran large database applications, especially Oracle applications. We trained the sales force, produced some modest direct mail campaigns, and launched.
Then reality set in. In the first three months, we sold about 45 machines to some 30 different organizations. We gathered data about our new customers and looked for correlations that would help us target prospective customers more precisely. We found nothing — no patterns in terms of size, SIC code, geography, application, and so on. The data were almost random.
We were stumped. So, we decided to interview the key decision maker in each account. We created an interview guide and fanned out to visit customers
After our visits, we dug into our findings. Again, we found no useful patterns in the demographic data. Then we started describing the key decision makers. Who were they? Why did they decide on us?
Most of the decision makers were men in their early thirties who had recently been promoted to a position typically described as VP, Data Processing. They replaced an older person who had held the same position for more than ten years. One of our marketers had a flash of insight: “It’s almost like the decision maker is saying, ‘I’m the new sheriff in town. We’re going to do things my way. This is one of my first big decisions … and we’re going to buy a hot new machine from a startup company. I’m going to make my mark.’”
It turned out to be a very accurate description. Nominally, our customers were buying our machines to run large applications. But psychology was perhaps more important. We estimated that roughly 60% of our customers fit the “new sheriff” profile.
We decided to market specifically to new sheriffs. We trawled through organization profiles and identified those that had a new VP of Data Processing. We sent each new sheriff a fairly intense mail campaign coupled with calls by our local sales rep. The campaign succeeded rather well. From the time of the launch, we grew to $300 million in revenue in about two years.
I didn’t know it at the time, but we were practicing an art that today is called, the “jobs to be done theory of innovation.” (Click here for a good introduction). Developed by Clayton Christensen and his colleagues, the basic idea is that demographic information doesn’t reveal why a person chooses to purchase a new product or service. If we misunderstand the job to be done, our innovations will miss the mark.
Our startup company, for instance, positioned around big machines for big databases. We wanted to offer ever bigger, pricier machines. The new sheriff profile, however, changed our thinking. To get in the door, we needed to make it easy for the new sheriff to buy something on his own authority. So, we introduced an entry-level machine priced just below a typical VP signature limit.
Similarly, think about why men buy pajamas. We might think they simply want to stay warm. But men in America typically don’t buy pajamas until they have a daughter who is three years old. Their motivation is not to stay warm but to preserve their modesty. If we misunderstand that, we’ll produce far too many cozy, warm, flannel pajamas that men will never buy.
In my experience, good marketers and salespeople use the jobs-to-be-done method naturally and intuitively. They’re good observers and naturally ask a basic question: why do people buy these products? They dig into the data but, more importantly, they observe how people behave and ask insightful questions. The management guru, Ted Levitt,was a natural at this. He noted that people don’t buy gasoline for their cars. Rather, they buy the right to continue driving.
The jobs-to-be-done theory suggests that the key to innovation is sociology, not technology. Do you want your company to be more innovative? It’s time to add more marketers and salespeople – and maybe a sociologist and anthropologist – to your development team.
How old are people when they’re at their innovative peak? I worked in the computing industry and we generally agreed that the most innovative contributors were under 30. Indeed, sometimes, they were quite a bit under 30.
Some of this is simply not knowing what can’t be done. I’ve seen this with Elliot. He doesn’t know how a computer is “supposed” to work. So he just tries things … and very often they work. On the other hand, I do know how a computer is supposed to work and I sometimes don’t try things because I “know” they won’t work. Elliot just doesn’t have the same limits on his thinking. That can be a great advantage in a new field.
While youth may be an advantage in software, it’s not true in many other fields. In pharmaceuticals, for instance, the most innovative people are in their 50s or even 60s. It takes that long to master the knowledge of biology, chemistry, and statistics needed to make original contributions. Comparatively speaking, it’s easy to master software.
Indeed, as knowledge gets more complicated, it takes longer to master. According to Benjamin F. Jones of the Kellogg School of Business, “The mean age at great achievement for both Nobel Prize winners and great technological inventors rose by about 6 years over the course of the 20th Century.” The average Nobel prize winner now conducts his or her breakthrough research around the age of 38 – though the prize is typically awarded many years later.
Aside from domain knowledge, why might you want a little gray hair to fuel innovation in your company? According to a recent article by Tom Agan in the New York Times, one reason is the time necessary to commercialize an innovation. As a general rule, the more fundamental an innovation, the longer it takes to commercialize. Ideas need to percolate. People need to be educated. Back-of-the-envelope sketches need to be prototyped. Lab results need to be scaled up. It takes time – perhaps as much as 20 to 30 years.
Who’s best at converting the idea to reality? Typically, it’s the person or persons who created the innovation in the first place. So, let’s say someone makes a breakthrough at the Nobel-average age of 38. You may need to keep them around until age 58 to proselytize, educate, socialize, realize, and monetize the idea. In the meantime, it’s likely that they will also enhance the idea and, just possibly, kick off a new round of innovation.
So, what to do? Once again, diversity pays. Mixing employees of multiple age groups can help stimulate new ways of thinking and better ways of communicating. Ultimately, I like Meredith Fineman’s advice: “Working hard, disruption, and the entrepreneurial spirit knows no age. To judge based upon it would be juvenile.”
My father, who was the first in our family to go to college, went to a land-grant university (Texas A&M). My sister went to a land-grant university (Clemson). I went to a land-grant university (Delaware). My wife went to a land-grant university (Purdue). My wife’s parents went to a land-grant university. (Wisconsin)
Abraham Lincoln set up the land-grant system through the Morrill Act of 1862. The federal government granted land to each state. The state used the land to set up a college to teach the practical arts, including agriculture, engineering, and military science.
The system worked. Land-grant colleges became social elevators that allowed lower-and middle-class kids to pursue higher education affordably. They also became engines of innovation, fueling an innovation boom that catapulted the United Sates to leadership positions in multiple industries in the late 19th century. We’re still riding the echo of that boom. I’ve often wondered about the return on the land-grant investment. The economic value created by the system must be orders of magnitude higher than the original cost.
The genius of the system is that it’s a platform, not a solution. For instance, Lincoln didn’t identify the inefficient harvesting of cotton as a national problem and jump to the conclusion that the government should invest in the cotton gin. Instead, he created a platform that allowed many people to pursue an education, investigate problems, and develop solutions on their own.
I bring this up because we seem confused about what role the government should play in stimulating innovation. I hear it in my IT/innovation classes all the time. Some students argue that government should get out of the way and let private industry solve every problem “efficiently”. Others argue that government should have a role but they have a difficult time describing it.
Ultimately, I think it’s fairly simple. The government should invest in platforms, not solutions. The land-grant system allowed millions of people — including me — to take something from America and then turn around and make something for America. (It’s not true that we’re either makers or takers. We’re usually both.)
In the recent past, the best example of platforms that stimulate innovation are probably the Internet and the human genome project. The massive brain mapping project — the Human Connectome — that President Obama recently announced could become the next great platform. On the other hand, the government investment in the solar panel manufacturer, Solyndra, was solution picking rather than platform building. It didn’t work so well.
So, I’m all for government investment in platforms that can stimulate innovation. By the way, I don’t claim that this is an original idea of mine. Steven Johnson makes much the same point in his book, Where Good Ideas Come From. But I do think it’s an idea that needs to be popularized. That’s why I’m writing about it. I hope you will, too. In the meantime, I’ll give credit where it’s due by saying, “Thank you Mr. Lincoln for helping my family get an education.”
Let’s say that the city of Groverton has 100,000 residents and produces X number of innovations per year. Down the road, the city of Pecaville has 1,000,000 residents. Since Pecaville has ten times more residents than Groverton, it should produce 10X innovations per year, correct?
Actually, no. Other things being equal, Pecaville should produce far more than 10X innovations. In predicting innovation capacity, it’s not the number of people (or nodes) that counts, it’s the number of connections. The million residents of Pecaville have more than ten times the connection opportunities of the residents of sleepy little Groverton. Therefore, they should produce much more than ten times the number of innovations.
In Where Good Ideas Come From, Steven Johnson makes the point that connections are the fundamental unit of innovation. The more connections you can make, the more likely you are to create good ideas. Scale doesn’t matter — more connections are better at a very small scale or a very large scale. This is where cities come in. In terms of innovation, larger cities have multiple advantage over smaller cities, including:
Does this work in real life? Johnson provides some very interesting anecdotes. More recently, last Friday’s New York Times had an article (click here) on manufacturing and innovation. The article argues that more innovation happens when designers are close to the manufacturing floor. Why? Because of information spillover. Researchers claim that offshore manufacturing reduces our ability to innovate precisely because it reduces information spillover. Connectivity seems to work on the manufacturing floor as much as it does in big cities. Scale doesn’t matter. Bottom line: if you want to be more innovative, get connected.
We know a lot about what innovation looked like in the past. What does it look like in the future?
That’s the question that Arthur D. Little (ADL) researchers asked of more than 100 Chief Technical Officers and Chief Innovation Officers in a recently published white paper. (Click here). The ADL researchers identified five key trends that should drive innovation management over the next decade. Today, I’ll summarize the trends. In the future, I’ll to delve into each one in more detail.
The most important trend — as rated by the CIOs and CTOs –is customer-based innovation — “finding new and more profound ways to engage with customers and develop deeper relationships with them.” B2B companies have traditionally emphasized customer-based innovation. After all, B2B companies have relatively few but relatively deep customers relationships. According to ADL, however, even B2C companies are now focusing less on the product itself and more on the “ownership experience”.
The second trend is proactive business process innovation. I read widely on innovation and almost everything I see has to do with product innovation. ADL says this is changing but that “there is still much to be done to develop a convincing innovation management approach that is sufficiently systematic and repeatable to generate new, innovative business models.” The first objective is to deliver “thick value” — long-term relationships with multiple touch points as opposed to “thin value” transactions.
Third is frugal innovation which may be better known as reverse innovation. Rather than innovating in high-value (and high-cost) knowledge economies, frugal innovation uses low-cost emerging economies to create products with “less” rather than “more”. Developing a new idea in India, say, will often result in a lower cost product than developing the same idea in Europe. Frugal innovation often changes entire supply chains rather than individual products.
Fourth is high speed/low risk innovation. The CIOs and CTOs say they expect even more time-to-market pressure in the next decade. Additionally, they think that product life cycles will continue to accelerate. At the same time,the customer’s ability to identify and publicize flawed products has expanded dramatically. So, even as the pressure to accelerate continues, the pressure to deliver flawless products also increases. How do you deliver high quality products in ever faster cycles? You change your business process. ADL expects to see more gradual product rollouts coupled with more pervasive and proactive post-sales service.
Integrated innovation is the last major trend ADL identifies. The idea here is to take innovation processes out of the New Product Development (NPD) domain and integrate them into all business processes and strategies. Among other things, this requires collaboration across traditional functional divisions. Organizational development experts will focus on building horizontal layers to replace vertical silos. Creating an Enterprise Architecture (EA) to manage knowledge and information could drive this trend.
So, five trends in innovation management – each is interesting in and of itself. Over the next few weeks, I’ll delve into each one in more detail and identify the prerequisites for success in each one. Stay tuned.