Innovation Versus Vision

Innovation vs. VisionTake a look at the “Home” and “About” web pages of the world’s most innovative companies such as Google, Facebook, Twitter, Apple and Gore. There is a word you will seldom, if ever, see on their web pages: “innovation”. That is because these companies do not strive to be innovative.

Now look at averagely innovative companies, the ones that come up with new products that aim to compete with products developed by leading innovators. Most likely you will see the word “innovate” and its derivatives, such as “innovative” and “innovation” all over the place. That’s because these companies are striving to be innovative.

So, what’s going on here? The truth is, truly innovative companies, those the come up with breakthrough products and services, those that are game-changers in their sectors or that create new sectors, do not aim to be innovative. Rather they relentlessly strive to follow a unique strategy. Facebook originally aimed to create the ideal social network. Now they are trying to become an alternative to the world wide web itself. Apple has relentlessly focused on making beautifully engineered and designed gadgets such as mobile telephones, computers and pads.

Visionary Leaders

Their leaders, such as the late Steve Jobs and Mark Zuckerberg, are not innovators. They are visionary leaders who so focused on their strategies that they are probably dreadfully boring at cocktail parties! But, by sharing their visions and their enthusiasm for their visions with their employees and business partners, they enable their companies to innovate. However, that innovation is focused on achieving unique strategic aims, rather than innovation for innovation’s sake.

In such environments, employees understand that top management is eager to implement ideas that help them in the pursuit of strategy. Indeed, the sole purpose of most teams is in one way or another to achieve strategic aims. Middle managers in visionary companies know that their jobs depend on working towards strategic goals. Innovation, on the other hand, is not important to them. That may seem ironic as they are following the best practices of innovation. But the key is that their aim is not to be innovative. It is to meet strategy based goals.

Unique Strategy

What do I mean by unique strategy? Many companies, especially large companies involved in varied business lines, tend to have bland strategies, such as to be the best in every sector in which they operate. Their strategic statements tend to be generic and could be used equally effectively by just about any company – even one in a completely different sector.

Visionary companies, on the other hand, tend to have much more specific strategic aims. For instance, Google’s original strategic aim was to produce the most relevant search results by using a special algorithm in their search engine. Gore aims to manufacture revolutionary products to solve specific industrial and medical problems.

Averagely Innovative Companies

Averagely innovative companies on the other hand tend to have blander strategic aims, such as to make high quality products. Their web sites are littered with the word “innovation”. Lenovo, for example, makes fine quality computers that can be found in households and businesses globally. It is an admirable, growing company. But it is not a particularly innovative company. As a result, the words “innovation” and “innovative” appears numerous times on their “About” page.

I have never worked with Lenovo, so I do not know what their situation is like internally. But I have worked with similar companies: quality, well run businesses that have recently decided to become more innovative. One of the first steps such companies take is to use the word “innovation” more frequently in corporate documentation. This is followed by hiring people to be innovation managers and to launch programmes to promote innovation.

Idea management software, or at least suggestion scheme software, is installed to capture ideas. Very possibly innovation consultants and trainers are hired to help guide the innovation initiative.

As a result of these activities, the company does indeed become more innovative. However, the innovation efforts tend to be unfocused. The result is usually incremental and medium level improvements on products, services and processes. It is extremely rare that these initiatives result in breakthrough innovation.

This is not a bad thing. Often, averagely innovative companies produce products that are better in many respects than the innovators. Apple may have led the pack with their innovative smart phone. But now many averagely innovative companies have produced smart phones that better Apple’s iPhone in various ways – and often for a lower price. Moreover, not everyone wants an iPhone. Many people want simpler, cheaper or less stylish telephones.

Being Realistic

If your company is not an innovative leader, if it is not focused on relentlessly pursuing a unique strategy, you need to be realistic about where innovation can take you. In theory, you can transform your company into a visionary company that becomes a true innovator like those cited at the beginning of this article. This tranformation will probably mean replacing your CEO with a visionary leader who is willing to make drastic changes to every aspect of your company, starting with its strategy. She will probably need to sell off vast chunks of your business, transform the way you work and get rid of a lot of employees. Those who remain will need to learn to work in new ways. They will also need to legitimise their activities in line with strategy. Budgets, project management, approval methods and much more will need to be changed.

If you work in a medium to large company, you are probably smirking to yourself right now, thinking “Jeffrey is crazy. That’s never going to happen in my company!” And you are right. It is extremely rare that a company, except a very small one, will make such drastic changes. The board and shareholders are unlikely to authorise such actions. Even if an innovative leader is taken on as CEO, employees reluctant to change will do everything that they can to impede her changes and guarantee their jobs. After all, when things change in large companies, most people worry about their own stability and future rather than their employer’s innovation potential.

Not surprisingly, such change is extremely rare. The closest example that comes to my mind is Nokia, which started life as a rubber works and eventually became a Finnish industrial conglomerate involved in many industries. It was only in the 1990s that Nokia rid itself of many of its lines and focused on mobile telecommunications. And, during the 90s, Nokia was an innovative leader in GSM technology.


Most likely, you are not going to transform your company into an innovative leader. But, as I have said, there is nothing wrong with that. Most companies are not innovative leaders. But, by focusing on incremental and medium level improvements to products, services and processes, you can nevertheless have an extremely successful company. In fact, in many industries, such as fast food, soft drinks and construction, there has been little breakthrough innovation in recent years.

Moreover, you can learn from innovative leaders. Most importantly, review your strategy. Is it unique to your firm or is it the kind of strategy that just about any firm could claim. If so, make it better.

Once you have done this, do not launch an unfocused innovation initiative. Rather, ensure that your innovation initiative is aligned with strategy. This can be done through brainstorming, ideas campaigns and other activities that generate ideas to solve specific strategic problems. Do not simply focus on being innovative. That tends to result in a lot of small ideas that improve bits and pieces of your operations, but do not make a big difference to your company. Rather focus on your strategy and use innovation as a tool that enables you to do that.

This article first appeared in Report 103. image credit: forbes

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Innovation Versus Vision


Best US Universities for Innovation Transfer?

Best US Universities for Innovation Transfer?

Comparing Innovation Transfer Activities of U.S. Research Universities

A university’s ability to create and share innovative technology and know-how should be evaluated in a holistic way that includes both academic and commercial activities. In this article I compare the innovation transfer activities of U.S. research universities in a new, multi-faceted way: by counting and mapping universities according to their ability to

  1. Publish Papers
  2. Generate New Inventions, and
  3. Attract Industry Research Funding

Why these three axes? A university’s scholarly publishing equals its ability to share knowledge via traditional channels; its invention activity reflects faculty interest in, and whether commercialization activity is valued on campus; industry funding equals the value of informal interactions between university and industry scientists (I’ll explain this one later). Combined, these arenas provide a holistic picture of a university’s activity in generating and sharing new technologies and scientific know-how.

To visually depict these comparisons, I made four bubble charts (click on each to open). The first bubble chart maps the usual suspects — the top 22 best-funded large U.S. research institutions. The remaining charts look at a new playing field, one where universities are compared according to their performance per million dollars of federal research funding, a view that triggers the emergence of a refreshing new set of highly performing universities.

This analysis represents university activity for the year 2010. The data on publications comes from the ISI Web of Science database. The data on disclosures and industry funding come from annual metrics collected by the Association of University Research Managers (AUTM, 2010). (If you spot data oddities or omissions for your university, let me know.)

1. Comparing the top 22 research universities

This bubble chart compares the biggest U.S. research universities. Here’s how to make sense of this chart (it’s best to view this on a large screen):

  • the vertical axis represents total number of publications for the year 2010
    Number of Publications versus Inventions

    Click to Enlarge

  • the horizontal axis represents how many inventions university researchers disclosed that year
  • the size of the bubble represents how much industry funding the university got that year

So, if a university bubble is high up on the chart, that university produces a lot of papers. If a university sits out to the far right, it creates a lot of new inventions. The bigger the bubble representing a particular university, the more industry funding that university received in 2010.

Not surprisingly, Harvard researchers publish a significantly larger total number of papers than those at other universities. Duke, University of Colorado and Washington University of St. Louis have high levels of research funding from industry sources. CalTech researchers are strong in both paper publishing and creating inventions: on average, for each invention reported by a CalTech researcher, six scholarly papers were published.

The University of Texas and University of California systems aren’t depicted here for the simple reason that their numbers are so large they compress the rest of the university bubbles into a messy blob. If you’d like to imagine these two gargantuan university systems in this chart, visualize two bubbles roughly one-third larger than the big red bubble that depicts the University of Colorado floating in the upper right hand corner. In other words, when it comes to the absolute number (not corrected according to federal funding) of papers published, new inventions and industry research funding, Texas and California perform very well.)

2. Universities that publish the most papers per federal dollar

Scholarly publications, or what some people call “open science” remains the largest, most critical source of university research to industry product development efforts. For this chart, I set up a level playing field. To figure out a university’s publication activity independent of the size of its federal research budget, I calculated how many publications each university churned out per million dollars of federal funding. This way, a new group of universities emerge as top performers.

Number of publications per million dollars of federal funding

Click to Enlarge

On this chart, the venerable Harvard shrinks in comparison to the Universities of Arkansas and Alabama. In fact, the number of publications from the University of Arkansas was so large — 165 papers per million dollars of federal research, almost three times more than the next-up university — that I checked and double-checked the count in ISI’s web of Science. (If this number is incorrect or deserves further explanation, please let me know or comment below. I can re-make the chart if needed.) The University of Akron appears to be a well-rounded university as it ranks in the top twenty according to publishing per federal dollar, and also dominates its peers according to new inventions and industry funding.

3. Universities that attract the most industry research funding per federal dollar

To represent the informal interactions between university and industry scientists, I chose to map how much industry funding a university receives. First, I picked out 20 universities that attract the most industry funding per million dollars of federal funding. Then I charted these 20 by publications (vertical axis) and invention disclosures (horizontal axis) and set the bubble size to the amount of industry funding the university received according to a million federal dollars.

You’re likely wondering why I would use industry funding as a measure of the value and intensity of the informal interactions between a university’s and industry scientists. True, few measures exist of non-contractual, informal ways that university researchers share their knowledge (conversations, consulting engagements, informal collaborations). But that’s not the primary reason. Turns out that the amount of funding a university researcher receives from companies may be the downstream result of this chimerical, yet widely acknowledged informal channel of university knowledge transfer. Why?

According to research described in a February, 2007 article by Branco Ponomariov and P. Craig Boardman, “We find that involvement in informal interaction is associated with higher probability of undertaking collaborative research with industry as well as with a higher allocation of research time to collaborative research with industry.” In other words, if individual faculty members are intensely and productively involved with their industry-based colleagues, they are more likely to eventually attract an industry sponsor for their on-campus research.

Top Industry Research Funding Recipients

Click to Enlarge

Interestingly, this research offers another compelling reason that university patents are not good measures of innovation. Ponomariov and Boardman discovered that although active faculty partnered with industry scientists to bring a commercial product to market, these joint research projects did not involve university-owned patents. Nor did owning or working in a private company (e.g. a startup) increase the odds that a university researcher would have strong connections to industry. Instead, researchers state that

“Curiously, having worked with industry personnel in work that has resulted in a patent does not have a statistically significant effect on the dependent variable. Having worked at a private company as an owner, partner or employee is also not statistically significant.”

Out of this group, Duke receives the most industry research funding per million federal dollars, followed by University of West Florida and then West Virginia University. The University of Maryland dominates according to both number of new inventions and publications.

4. Universities that invent the most new inventions per federal dollar

Finally, new university inventions. I selected this group of 20 universities by dividing their total number of formally disclosed new inventions by how much federal funding they received. Similar to the chart above, the vertical axis = number of publications per million dollars of federal funding; the horizontal axis = number of inventions per million dollars of federal funding; the size of the bubble = industry research contracts.

First, a disclaimer on counting new inventions as a measure of university innovation transfer: reporting a high number of invention disclosures is a laudable achievement. It’s a good sign that university researchers trust and value their university’s formal technology transfer process. However, keep in mind that the majority of university researchers (even those considered high-performing) disclose few or no inventions to their university’s technology transfer office. Yet, university faculty and graduate students are keenly aware of how their research applies to real-world challenges, and they continue to perform cutting-edge research that makes tremendous contributions to industry innovation.

The reason I chose the number of new inventions a university generates each year as a meaningful measure of a university’s innovation transfer ability is that new inventions serve as a significant indicator of a university’s commitment to, and skill in commercializing research. For this reason I like new inventions more than patents as a measure of a university’s innovation climate; patents tend to reflect local policy and the size of a university’s patent budget.

New inventions versus federal funding

Click to Enlarge

Here’s what I like about correcting for differences in federal funding: some of these universities actually disclosed a relatively small absolute number of total inventions. Yet, if you count invention bang for the buck, these small schools are actually turning their research into reported inventions at a brisk rate, e.g. University of Akron, Michigan Tech and South Dakota State.

Overall, Brigham Young University turns its federal funding into the largest number of new inventions, disclosing on average, five inventions per million dollars. Louisiana Tech, Auburn and the New Jersey Institute of Technology do well here too, although the relatively small size of their bubbles indicates that they earn somewhat less in industry funding.

What’s next?

Policy makers, university administrators and others spend a lot of time and effort trying to figure out how to track and chart a university’s skill in generating and sharing new knowledge. Where they fall short is that most metrics today count contractual units of knowledge, for example patents issued, new startups and license revenue earned by university-owned patents. Instead, we need to expand how we measure how effectively universities translate federal research funding into new knowledge and new technologies by honoring channels that do not involve intellectual property. Of course the formal university technology transfer process is a valuable channel that should be valued and maintained. However, the fact remains that most industry scientists keep tabs on innovative university research by reading scholarly articles, informally interacting with their university-based colleagues and if that goes well, by eventually funding on-campus research.

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Test Your Innovation IQ

Test Your Innovation IQEveryone knows that innovation means coming up with the next great idea in your industry, right? Actually, there’s a lot more to it than that. Test your ability to separate innovation fact from fiction by answering the following questions true or false:

  1. Innovation is the act of coming up with new and creative ideas.
  • Innovation is a random process.
  • Innovation is the exclusive realm of a few naturally talented people.
  • The biggest obstacle to innovation is a lack of organizational resources and know-how.
  • The most important type of innovation involves bringing new products and services to market.
  • Teaching employees to think creatively will guarantee innovation.
  • The most powerful way to trigger your brain is to simply ask it a question.
  • Most companies pursue incremental rather than disruptive innovation.
  • Most companies are not structured to innovate.
  • Listening to your customers is a great way to innovate.
  • Answers:

    1. False. In business, innovation is the act of applying knowledge, new or old, to the creation of new processes, products, and services that have value for at least one of your stakeholder groups. The key word here is applying. Generating creative ideas is certainly part of the process. But in order to produce true innovation, you have to actually do something different that has value.

    2. False. Innovation is a discipline that can (and should) be planned, measured, and managed. If left to chance, it won’t happen.

    3. False. Everyone has the power to innovate by letting their brain wander, explore, connect, and see the world differently. The problem is that we’re all running so fast that we fail to make time for the activities that allow our brains to see patterns and make connections. Such as pausing and wondering….what if?

    4. False. In most organizations, the biggest obstacle to innovation is what people already know to be true about their customers, markets, and business. Whenever you’re absolutely, positively sure you’re right, any chance at meaningful innovation goes out the window.

    5. False. It’s certainly important to bring new products and services to market. But the most important form of innovation, and the #1 challenge for today’s business leaders may really be reinventing the way we manage ourselves and our companies.

    6. False. New ideas are a dime a dozen. The hard part is turning those ideas into new products and services that customers value and are willing to pay for — a process that requires knowledge about what your customers want and need, coupled with implementation.

    7. True. Ask a question and the brain responds instinctually to get closure. The key with innovation is to ask questions that open people to possibilities, new ways of looking at the same data, and new interpretations of the same old thing.

    8. True. Most companies focus on using internally generated ideas to produce slightly better products (incremental innovation). Then they strive to get those slightly better products to market as quickly and as cost-effectively as possible. This approach is quicker and cheaper than disruptive innovation. But it rarely generates the results that lead to sustainable market leadership.

    9. True. Most organizations are physically set up with accounting in one area, marketing in another, and management off by itself. Employees rarely interact with other departments unless they need something to get their jobs done. And leaders and departments often withhold information, believing that it puts them in a position of power. Innovation requires teamwork, communication and collaboration, not isolated silos.

    10. Trick question! The answer is “it depends.” Research shows that customers can be a good source of ideas for improving existing products and services — if you’re looking to achieve incremental innovation. However, by itself, customer research is not sufficient for generating disruptive innovation because it only uncovers expressed, or known, customer needs. Disruptive innovation solves problems that customers didn’t even know they had or were unable to clearly articulate to themselves or their vendors. It redefines the market at a very fundamental level or, in many cases, creates a new market.

    If you got 8 or more correct answers, give yourself a pat on the back. If you scored between 4 and 7, I recommend some more research and work on these critical leadership skills. If you scored less than 4, wake up and smell the burnt coffee! Get some help.

    If you’re not constantly looking to improve your products, services, systems, and managerial processes, you will fall behind. And once you fall behind, it can be very difficult and often impossible to catch up!

    Link to the original post: Test Your Innovation IQ

    Rethinking the Product Development Funnel

    Rethinking the Product Development FunnelAs originally envisioned, the product development funnel implied that a well-defined product development process exists. However, the original funnel, and others that have followed, are increasingly seen as lacking. This article proposes a new funnel that addresses these missing elements.

    The icon of a funnel has been in use for several decades now as a visual depiction of the new product development (NPD) process. It works well because it implies that product development is, in fact, a refinement process that takes us from the earliest stages of a project – with a lot of fuzzy ideas and fuzzy thinking – to the final stage of new product launch. However, in reviewing the many funnels that have been proposed and used over the years, there is a growing realization that most are lacking in a few important ways. In this article, we will review some of these funnels, discuss their strengths and weaknesses, and ultimately propose a new one that addresses these weaknesses.

    Evolution of the Product Development Process

    One of the earliest attempts to create a “flowchart” diagram of the product development process appeared in Urban and Hauser’s 1980 textbook, Design and Marketing of New Products1. (See Exhibit 1.)

    Exhibit 1: New Product and Development Service Process

    New Product and Development Service Process

    Source: Urban and Hauser “Design and Marketing of New Products” (1980)

    Having been close to these authors at the time of their writing, it is evident that most of the real world examples that led to this flowchart came from the world of consumer packaged goods (CPG), a realm in which ideas were plentiful and most did not require any particular technical expertise to imagine – e.g. a new flavor of soup, a new brand of toothpaste, or a new dishwashing liquid. In this world, most of the action deals with marketing issues such as the screening of ideas, product positioning, advertising and messaging, and sales forecasting. The creation of prototypes to test was usually neither prohibitively expensive nor technically daunting, and so the process almost always included real world “test marketing”, i.e. launching the product in a small geographic area in order to test its viability before the major investment of a national or international launch. In this world, little attention was paid to the idea generation process.

    Next, in 1986, Robert Cooper published the first edition of his popular book, Winning at New Products2. In it, he presents a diagram of the product development process that breaks it into five stages preceded by a process he calls “discovery,” a process that includes idea screening. Only two years later did he give this process a name: Stage Gates®3, a name that he actually trademarked and is now in use at companies worldwide. (See Exhibit 2.)

    EXHIBIT 2: Stage Gates® NPD Process

    Stage Gates NPD Process

    Source: Cooper “Winning at New Products” (1986, 1993, 2001)

    Cooper’s “client” for this process diagram was usually either the research and development (R&D) director or a high-level NPD manager who needed to deal with a portfolio of products, all at different stages of development. What he advocated was a formal management review process in which product development teams were required to come before this high-level management committee to present their project so that management could make an informed decision, using consistent criteria, as to whether to promote a project onto the next stage of development or to kill it.

    Notice, however, that Cooper’s discovery process – which includes idea generation and screening – precedes the main Stage Gate process. At this earliest stage of new product development, little budget is required, and in many cases, no team has even been assigned to work on the project.

    The earliest use of a literal “funnel” that I was able to find appeared in Wheelwright and Clark’s 1992 textbook, Revolutionizing Product Development4. (See Exhibit 3.)

    EXHIBIT 3: Earliest Use of a Literal Funnel to Describe NPD Process*

    Earliest Use of a Literal Funnel to Describe NPD Process

    Source: Wheelwright & Clark “Revolutionizing Product Development” (1992)

    Their diagram consists of three major stages, which they label Investigations, Development, and Shipping of Products5. As with the previous two, the emphasis at the start is on screening of ideas. Little is said as to where the ideas come from or how they are generated.

    At approximately the same time, Michael McGrath, one of the founders of the consulting firm PRTM, published his book, Setting the PACE in Product Development6. McGrath’s first stage deals with Concept Development, usually a piecing together of ideas into a full product description. Similar to Cooper, McGrath advocates a periodic management review process that he calls Phase Reviews.But in almost every other way, they are the equivalent of Stage Gates. (See Exhibit 4.)

    EXHIBIT 4: McGrath’s PA CE® NPD Funnel

    McGrath’s PA CE® NPD Funnel

    Source: McGrath “Setting the PACE in Product Development” (1992)

    One other noteworthy thing about McGrath’s process is that he includes the development of a formal “business case” as a major phase before the project moves onto formal development. As with Cooper, his focus is with a portfolio of projects which need to be weeded through periodically. And again, little is said about where the ideas come from. They clearly precede the entrance to his funnel.

    A similar diagram was put forth in 2005 by MIT’s Center for Innovation in Product Development (CIPD)7. It has some of the characteristics of all of the preceding diagrams – a literal funnel, with multiple projects proceeding in parallel. But once again, the “discovery” process falls outside the funnel in a stage called “Opportunity Identification and Idea Generation” with little advice about how to go about it. (See Exhibit 5.)

    EXHIBIT 5: MIT ’s CIPD Take on NPD

    MIT ’s CIPD Take on NPD

    Source: MIT Center for Innovation in Product Development (CIPD, 2005)

    What’s Missing?

    All of these diagrams represent important contributions to the field of NPD. But all are lacking in at least one important respect: They begin just after the point in the process where the idea has already been generated. Once the idea has been put forth, they all deal quite well with the process of concept generation and evaluation, design and engineering, etc.

    Unfortunately, it is in these earliest stages of the NPD process that practitioners have the greatest difficulty and where researchers have placed their greatest emphasis in recent years. In fact, they have given this segment of the process a particularly colorful name: The “Fuzzy Front End” of NPD. Certainly these stages have become less fuzzy during the past decade, but they are still the most challenging and least subject to a clear “one-size-fits-all” process.

    As stated earlier, the purpose of this article is to propose a new funnel – one that gives greater emphasis and better definition to the “Fuzzy Front End.” Many of these activities require some form of market research. The validity of that conclusion should not be lessened by the fact that the focus of my work over the years has been in this area.

    A “New” New Product Development Funnel

    The process that has evolved in my work and that of my firm during the past decade or so consists of five major stages: Discovery, Definition, Design, Development, and Delivery. In each stage of this process, the product developer is faced with the need to answer a series of difficult questions listed below.


    • What is the opportunity that we might want to pursue?
    • Who is the customer that we want to target?
    • What are their major problems, from a high-level perspective, in achieving the task they have chosen to undertake?


    • What detailed needs must we satisfy?
    • How should we measure how well we’re satisfying them: that is, to what specifications should we design?


    • How can we satisfy those needs: that is, can we come up with better features and solutions than those that already exist?
    • How do we describe these features and solutions to our customers such that they will find them compelling and believable?


    • Which of these prospective features or solutions are actually worth investing in?
    • Which should we actually include in the final product?
    • If we do, how much will people be willing to pay for them?


    • The final shakedown process: Can we reliably produce it, sell it, maintain it, and make money doing it?

    Our funnel, shown in Exhibit 6, focuses on the activities that product developers must go through in order to answer these questions satisfactorily. Many of these activities are quite daunting. They require strong analytical skills, technical skills, discipline, intellectual honesty (with oneself), creativity, and of course, a little luck!

    EXHIBIT 6: A “New” New Product Development Funnel

    A “New” New Product Development Funnel

    Source: Visions Author Gerry Katz, Applied Marketing Science Inc.

    Navigating Through The Funnel

    The new funnel shown in Exhibit 6 lays out a series of activities that companies typically go through and that I believe is more representative of what really happens. The most important difference between this funnel and the ones presented earlier is that we have added two new levels at the top. Together, these two levels attempt to better describe the “Fuzzy Front End” and attempt to more explicitly define what it takes to get through them.

    The discovery process comprises four key elements:

    1. Exploratory Research

    Exploratory research is usually a completely informal activity. It can involve informal discussions with customers, attending conferences or trade shows, and especially poring over previous studies that are likely to be sitting on shelves gathering dust. Most companies have a wealth of information already in their possession that no one has paid any attention to for years. This is usually a good starting point, because it is likely that someone in the organization has dealt with problems in this arena before, and exploring past thinking often provokes new thinking for the current environment.

    2. Secondary Sources

    The goal of secondary source research, as described by Abbie Griffin, is to “get smart inexpensively.” Until about 15 years ago, this almost always required library research. But the web has completely revolutionized this step. It is remarkable how quickly you can assemble a great deal of useful information from secondary sources, most of it completely for free. There are also many syndicated studies available – i.e., reports written by various consultants or industry associations that cover an entire industry or market segment in great detail – often for a very reasonable price. Again, the underlying assumption is that somebody, somewhere has researched this question before, and if you can assemble enough facts that no one else has quite pieced together just yet, you can often find some great opportunities. Web research is a skill that requires some experience, and ironically the people who have the most are usually your youngest employees, because they’ve been doing it since they were in the third grade! In general, the younger you are, the better you are at web-based secondary source research.

    3. Ethnography

    Ethnography, in a nutshell, is research by observation – watching customers actually use products and services to accomplish various tasks. While there are many uses for ethnography, the most common and useful is as a form of discovery. Some practical constraints that must be dealt with, but if you can get around them, ethnography is often highly instructive. These constraints may involve issues of privacy (e.g. defense-related industries, healthcare, or personal care), or they may involve the so-called “Hawthorne Effect,” the phenomenon that observation may alter behavior. But until about 20 years ago, most product developers rarely if ever got to see their products in actual use – a fact that is hard to believe in today’s world.

    4. Online Communities

    The use of online communities has exploded in the past few years. They go by many names: social networks, communities of interest, communities of enthusiasts, user-generated content (UGC), etc. While many question their value as sources of new ideas, they have already proven to be great sources for problem identification. By following what customers are talking about online, companies can get great insights regarding what problems they ought to consider addressing. If you are in a widely used consumer product category, there is likely to be more content already available than you could ever wade through. But if you are in a business-to-business category, the available content might be quite limited. For those cases where the amount of content is overwhelming, there have been many attempts to use artificial intelligence algorithms to try to wade through and make sense of the data. The jury is still out as to their usefulness. But even a cursory reading of what is being talked about on-line can be a great form of discovery.

    The definition process comprises three key elements:

    1. Target Definition

    Once a company has completed its discovery process, it is time to put a stake in the ground. This usually begins by explicitly identifying what market or population it wishes to target for a prospective new product or service. This target definition can be extremely broad or extremely narrow, but it is important to
    be as explicit as possible, because this will drive almost all of the development activities going forward. For instance, we could define our target as “users of home office printers who generate fewer than 500 pages per month” or “physicians who treat at least 20 diabetic patients per month.” The target definition usually involves some form of market segmentation, although the segments could be based on personal demographics, company characteristics, geography, products used, technology maturity, or any of several other criteria.

    2. Needs Assessment

    This is the stage where Voice of the Customer (VOC) research is called for. The acronym VOC has become badly bastardized in the past five years. For many, it has become a euphemism for almost any kind of customer contact or market research. Many now use the term as a synonym for Customer Satisfaction Measurement (CSM) or Customer Relationship Management (CRM) techniques. In truth, the term came specifically from the field of NPD. It is defined as the process of gathering, organizing, and prioritizing customer wants and needs. To the naïve, this simply means asking customers what they want. But such an approach almost always backfires, because it assumes that the customer will be able to tell you about the exact features or solutions you ought to include in your new product. Unfortunately, most customers aren’t all that creative, and so they simply play back all of the features and solutions that already exist in the marketplace, which rarely leads to anything more than a me-too product. Experienced practitioners in this area make a clear distinction between needs and solutions to those needs. The need is the benefit that the customer is trying to derive when using the product or service – the job or task he or she is trying to accomplish. The solution is the way in which the product delivers that benefit. If asked using the proper techniques, the customer is usually able to articulate the former quite well. It is only the company’s job to come up with the latter.

    3. Design Specifications

    One of the most difficult challenges in NPD arises from the natural tension that often exists between marketing and engineering. It usually plays out when someone from marketing describes a customer need, and the engineer says, “Just give us the spec!” The problem is that customers speak in soft
    customer language, and engineers need to design to hard technical specifications: that is, something that is measurable and controllable in the design such that they can objectively decide which of several alternative designs will best deliver on the customer need. This dilemma requires that the need be “translated” into a technical specification. Customers can give us clues as to how they evaluate these things, but this process usually requires a good deal of technical expertise. So for example, if the customer says he or she needs a “powerful computer”, shall we try to increase the level of MIPS, RAM, or some new spec? To do this intelligently, we must get a really detailed understanding of how customers define soft concepts like “powerful.” Quality Function Deployment (QFD) deals with this exact phenomenon. But whether you use a rigorous technique like this or not, this translation into design specs is something that your engineers will need one way or another.

    This stage involves the structure of the product or service.


    Whereas all of the earlier funnels started with ideation, in our funnel, ideation takes place only after navigating through the “Fuzzy Front End.” The reason for this is that ideation works better when it is focused on the right problems. Rather than just asking almost anyone – employees or customers – for new product ideas, when a company has given careful attention to all of the activities in the “Fuzzy Front End,” they begin ideation focused on an empirically identified set of unmet or poorly met customer needs. In this way, the brainstorming can be focused on particular problems, whether they are about technical issues, engineering problems, business process improvement issues, marketing, maintenance, or manufacturing. For example, if customers identify the issue of their windshield wipers failing to work well in icy conditions, a team could ideate on new solutions to this problem, such as the use of different materials on the wipers, or ways to melt ice off of the windshield or the blades.

    Clearworks - Customers, Connections, Clarity

    The brainstorming process has been the subject of a great deal of innovation in the past five to 10 years, mostly due to the advantages afforded by the web. Whereas traditional brainstorming has always been a face-to-face process, the web obviates the need for co-location, allows for anonymity (which removes politics from the process), and lets the process proceed asynchronously, providing “soak time” – i.e., the ability for participants to think before having to respond. Another area of innovation in brainstorming is the use of creative incentive systems that reward both creative thought and collaboration. Contests, games, and other forms of reward have been shown to add a combination of competition and fun to the process8.

    2. Concept Development

    Once ideation has been completed, the company is ready to piece together the best ideas into one or more “concepts.” A concept is simply a description of a new product or service that the company might consider creating. A concept can be conveyed in many forms, ranging from a simple one-sentence description to a multi-paragraph description to a photo, a drawing, a model, or even a working prototype. While many concepts look like a primitive form of advertisement, the emphasis is on facts, features, and benefits, rather than on creative marketing communication. The goal is to convey what is new and different about the envisioned product in a way that is compelling and believable to customers. The chosen form is usually dictated by what it will take to explain the features and benefits of the new concept. In general, the more complex the concept, the more detail will be needed to convey it.

    3. Concept Evaluation

    Now that we have one or more concepts, it is time to take them to customers for evaluation. The first question is whether to do this qualitatively or quantitatively – or both. This decision is based on many factors, some dealing with practicalities and others with personal preference. In general, B2B products are evaluated qualitatively, while B2C products are more often evaluated quantitatively. But there is significant value to both approaches. Much of this decision has to do with the difficulty and expense of recruiting a sufficient number of customers to participate in market research. Most consumer products simply have larger populations from which to choose.

    Finally, we come to the development process.

    1. Feature Trade-off

    Once team members have settled on the details of the product they would like to build, it is time for bench engineering and IT development to begin. We have now settled on a target market and population, identified the key unmet needs, decided what specifications to design to, brainstormed on the features and solutions that would best address those needs, and evaluated those ideas with real customers.

    But there is at least one major decision that remains before the company can decide on whether the major investment in design and engineering is warranted: If we are actually able to create these features and solutions, will it be worth it? And to answer this question, we need to try to figure out how much people will pay for a given feature, solution, or benefit. Many product developers like to simply ask customers how much they would pay for a given feature. But this technique has always proven to be of little validity, because customers usually “game” the system by “low-balling” their answers. After all, if a car dealer were to ask you how much you’d be willing to pay for a given vehicle, would you actually tell them the truth? The best way to answer this question is with the use of a market research technique called conjoint analysis, created by Paul Green and V. Srinivasan at the University of Pennsylvania’s Wharton School in the mid-1960s. The term is actually a contraction of the words considered jointly.

    The underlying idea is this: If you were to ask customers whether they wanted this or that feature or benefit in a new product, they would likely tell you that they wanted everything and all at the highest possible level. But that is not how product choices are actually made. In the real world, people are forced to make trade-offs. So, in conjoint analysis, people are given choices between several different configurations of the product that explicitly force them to make these trade-offs. And if we give people enough different choices using a carefully constructed experimental design, we can determine the weight they put on each attribute. For instance, if you were asked to choose between a sport utility vehicle (SUV) that gets 17 miles per gallon of gasoline and costs $30,000 versus a sedan that gets 26 miles per gallon and costs $25,000, you are implicitly being forced to make trade-offs between body style (SUV vs. sedan), mileage (17 mpg vs. 26 mpg), and price ($30,000 vs. $25,000).

    Many people use conjoint analysis as an elaborate way to determine the importance of various customer needs. But this is not really what Green and Srinivasan had in mind. Conjoint was intended to be used to determine the importance of various product features – including price. In fact, conjoint analysis is probably the only valid method in all of market research to determine how much someone would pay for a given level of a given feature. And its best use is after needs assessment and ideation have been completed. The goal here is to determine how much people would pay for a new feature or solution. Taken together with cost and volume estimates, this is the key piece of information needed to decide whether to invest in this new feature or solution.

    2. Prototype Evaluation

    Once R&D and engineering have developed a working model of the product, we need to put it into the hands of some real customers to determine whether it works and whether they like it. Most companies do this in several stages. First they might conduct alpha testing: i.e., putting it in the hands of some of their employees or a handful of highly trusted customers. The goal here is to get some initial feedback without revealing the product to the world just yet. If successful, then the next stage is called beta testing. Here we distribute the product more broadly to get more extensive feedback for fine tuning, a practice that is particularly popular with software manufacturers. Finally, we do gamma testing, the equivalent of what the packaged goods companies call test marketing: i.e., real sales of the product, but in a limited fashion in terms of geography or some other characteristic.

    This stage of NPD, involving positioning and launch of the new product, is more critical than it may seem. Many companies falter here. (See the Visions article on a study by Booz & Company in the March 2011 issue9.)

    The product is almost ready to go to market now, and this is where messaging and marketing communications become critical. How will we communicate to potential customers about the availability of our new product? What makes it different and better than existing solutions? When we do go into actual production, can we produce the product at an acceptable level of quality and reliability? And can we provide the needed level of service to maintain the product once it’s in customers’ hands?

    Strengths and Caveats

    What makes this funnel different from the ones we examined earlier is that this one attempts to give greater definition and emphasis to NPD’s “Fuzzy Front End.” It also puts ideation in its proper place. Rather than assuming that it somehow just happens before the product development process even begins, this funnel strongly suggests that ideation shouldn’t even be attempted until after a needs assessment has been completed. This helps to focus the process on ideating about the right things – the key unmet needs in the eyes of customers. Furthermore, if conjoint analysis is to be employed to evaluate pricing and feature trade-offs, it is placed “deeper” in the funnel where it was originally intended to be used.

    Admittedly, there is a deliberate emphasis here on activities that are best addressed with market research. But such a bias is not out of place given the high degree of risk in NPD and market research’s proven role as a way to mitigate that risk.

    One Final Note: Just as there have been more advances in the early stages of the product development process, it is becoming increasingly clear that “Product Launch” has become the new stepchild of NPD. There is still relatively little hard research on this final stage of the process, and perhaps greater attention should now be paid to the “Fuzzy Back End” of NPD.

    1. Urban, Glen and John R. Hauser, 1980. Design and Marketing of New Products, p. 33. Englewood Cliffs, NJ: Prentice-Hall, Inc.
    2. Cooper, Robert G., 2001. Winning at New Products, p. 130. Cambridge, MA: Perseus Publishing.
    3. Stage-Gate® is a registered trademark of the Product Development Institute in the USA; see
    4. Wheelwright, Steven C. and Kim B. Clark, 1992. Revolutionizing Product Development, p. 112.New York, NY: The Free Press.
    5. Wheelwright and Clark’s three-stage process is most similar to the process used today in the PDMA’s Body of Knowledge, although the latter refers to them as Discovery, Development, and Commercialization.
    6. McGrath, Michael E., 1996. Setting the PACE® in Product Development, p. 38. Boston, MA: Butterworth-Heinemann.
    7. Hauser, John R., 2008. Note on Product Development, p. 3. Cambridge, MA: MIT Sloan Courseware.
    8. Toubia, Olivier, 2006. “Idea Generation, Creativity, and Incentives,” Marketing Science 25, 5, September-October: p. 411-425.
    9. Jaruzelski, Barry and Kevin Dehoff. How the top innovators keep winning. Visions, Vol. 35, No. 1,
    p. 12. March 2011.

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