Tim Kastelle

Tag: Lean Startup

  • Those That Get It Don’t Need It, and Those That Need It Don’t Get It

    Those That Get It Don’t Need It, and Those That Need It Don’t Get It

    Here’s a central problem with trying to get any new idea to spread – often, those that get it don’t need it, while those that need it don’t get it.

    It’s a paradox.

    This leads to problems for people that have new ideas.

    Problem 1a: you end up talking to the wrong people. It is easiest to talk to the people that get it – even though they don’t need your idea. Back in my startup days, we often went to First Tuesday in Brisbane to try to build our network. We’d talk to all the other local startups about the problems that we shared, and it was great – they really got it!

    Did it help us build our business? No. No, it did not.

    We were talking to the people that were easy to talk to – the ones that got it. But they didn’t need what we had built. To grow a business we had to talk to the people that needed our ideas. That was a lot harder. It was frustrating, and difficult. Mainly because:

    Problem 1b: you might be solving a problem that people don’t yet realise they have. This problem is the opposite of the first one. This makes it really hard to talk to them, because when they hear your idea, they’ll hate it.

    When this happens a lot, you’re in what Seth Godin calls the Gulf of Disapproval:

    Here’s what he says:

    Start at the left. Your new idea, your proposal to the company, your new venture, your innovation—no one knows about it.

    As you begin to promote it, most of the people (the red line) who hear about it don’t get it. They think it’s a risky scheme, a solution to a problem no one has or that it’s too expensive. Or some combination of the three.

    They need your idea, but they don’t get it.

    The key to solving this paradox is to find the small number of people that will get it and that need it. Even for huge breakthrough ideas, this original group is usually pretty small.

    These people are the blue line in Godin’s drawing.

    Here is how Steve Blank describes them:

    Earlyvangelists are a special breed of customers willing to take a risk on your startup’s product or service. They can actually envision its potential to solve a critical and immediate problem—and they have the budget to purchase it. Unfortunately, most customers don’t fit this profile.

    Earlyvangelists can be identified by these characteristics:

    • They have a problem.
    • They understand they have a problem.
    • They are actively searching for a solution and has a timetable for finding it.
    • The problem is painful enough that they have cobbled together an interim solution.
    • They have, or can quickly acquire, dollars to purchase the product to solve their problem.

     

    How do we find these people? We start by building a model of who we think they are, and what we think they need. This model is almost certainly wrong. We fix that by going out and talking to these people to learn about the problems that they are actively trying to solve.

    Because we’re trying to identify problems, we’re not pitching during these conversations. We’re learning. If we do that enough times, we’ll figure out what a small group of people really need right now, and, with luck, we can build it for them.

    People usually can’t explain what they need, especially if the idea is genuinely new. So you need to look for evidence of problems. In my experience, the sign that we’re really onto something is in Blank’s fourth point – when we find people that have already hacked together a solution of their own. This is strong evidence.

    It turns out that our two groups of people aren’t mutually exclusive – there’s a small overlap:

    When we have a new idea, our job is to figure out what these people need, who they are, and how to find them. Once we’ve done this, then more people will start to get the idea, and more people will also start to need it.

    That’s the only way to cross the Gulf of Disapproval.

  • The Magic in Lean Startup is Hypothesis Testing

    The Magic in Lean Startup is Hypothesis Testing

    I’ve been running Lean LaunchPad programs for a couple of years now, and all along, I’ve thought that the number of customer development interviews that a team does is a good indicator of how successful they’ll be. And when we measure interview numbers against progress on the Investment Readiness Levels, the correlation is very strongly positive.

    Consequently, I thought that the magic in lean startup is customer development. I was wrong.

    In our work at Era Innovation we had an experience that invalidated this hypothesis.

    We’re doing some work for a corporate client and we’ve got a team (that’s doing ALL of the work and hard thinking on this) that has gone through two LLP iterations looking at two different problems.

    The first one was textbook lean startup. We started with a business model canvas, worked out the hypotheses, and the team started interviewing. After more than 130 customer development interviews, they had made two major pivots. They supported the final business model idea with a couple of minimum viable product tests, and everyone was happy, including the clients.

    The second problem was trickier. Because the issues seemed so broad, the team launched straight into customer development interviews – and we expected the insights about the best business model to emerge from that data. It turns out that this is about a billion times tougher than we expected.

    The difference is that the second time around, we didn’t test hypotheses right from the start.

    Ash Maurya wrote a great post about this very issue this week (and he also addresses it in his excellent new book Scaling Lean).

    He says:

    We start by guessing a new law or a new theory. Then we compute the consequences of our guess. Finally, we compare those computations with experiments or observed experiences. According to Richard Feynman, this simple statement holds the key to science: “If your guess disagrees with experiment, it is wrong. It doesn’t matter how beautiful your guess is, how smart you are, who made the guess, or what their name is … it is wrong.”

    Here’s his diagram:

    innovation experiments

    And here’s how he frames it for business:

    Innovation experiments are no different. Achieving breakthrough, then, is less about luck and more about a rigorous search. The reason the hockey-stick trajectory has a long at portion in the beginning is not because the founders are lazy and not working hard, but because before you can find a business model that works, you have to go through lots of stuff that doesn’t.

    Breakthrough insights are often hidden within failed experiments.

    Tina Seelig also talks about this in her book inGenius:

    Trained scientists know this well and, therefore, do their best to design experiments that answer an important question, no matter what the specific results. They know that each experiment offers valuable clues on the path to understanding. As the saying goes, “Genius is the ability to make the most mistakes in the shortest period of time.” Each of those mistakes provides experimental data and an opportunity to learn something new. Like scientists, we need to stop looking at unexpected results as failures. By changing our vocabulary, by looking at “failures” as “data,” we enhance everyone’s willingness to experiment. That is a big idea!

    The data on lean startup hypothesis is that nearly 70% of them are invalidated. That’s a lot of learning!

    And we learn best when we test specific hypotheses and business models. That is why, as Steve Blank says, Build-Measure-Learn isn’t just throwing things agains the wall to see what sticks. That’s a waste of resources, and definitely not lean!

    Instead, if we are rigorous in our approach, our pivots will be evidence-based.

    In a guest post for Pollenizer, Alistair Croll says:

    Adopting a “pivot” mentality isn’t an excuse to randomly walk all over a market. As Eric Ries has explained, a pivot is an adjustment, akin to keeping one foot on ground as you rotate gradually. It’s iterative, honing in on the right product for the right market until it just clicks. Pivoting isn’t hopping.

    …

    Of course, this sounds like work. A corollary of not pivoting without knowing why is that everything you do must produce learnings. That means interviewing customers—and those who didn’t buy what you were selling. It means building experimentation into your product. It means analyzing results using metrics that drive your business plan, rather than meaningless vanity metrics.

    Which brings us back to customer development interviews. It is an essential lean startup tool – you can’t succeed without them.

    But we must use them to test hypotheses. Data without hypotheses is much less magical.

    The magic in lean startup is hypothesis testing.

    Note: Two tools that you can use to track and test your hypotheses are the Lean Startup Progress Board from Strategyzer and all of the great tools from Pollenizer.

     

  • Treating Discovery Like Execution Will Kill Innovation

    Treating Discovery Like Execution Will Kill Innovation

    “In the end, these projects are all business model problems.” That’s what I said to my friend Steve Adelman about the projects we collaborate on as part of the Wharton-UQ Global Consulting Practicum program.

    “Why aren’t they market entry problems?” he replied.

    I was stumped by his question for a couple of days. Then I realised what was going on.

    GCP is a program where teams of 10 MBA students, 5 from Wharton and 5 from UQ (or other partner schools), work on a project for an Australian organisation that is trying to increase their business in North America. The program was originally designed as part of Wharton’s marketing program – so the projects were conceived as market entry problems, exactly as Steve said. However, out of the fifteen projects that we’ve run so far, only about three have been market entry problems, the rest have been business model problems.

    What causes the difference?

    It comes down to discovery versus execution.

    Steve Blank framed this problem as: existing firms execute, startups have to search before they can execute.

    discovery versus execution

    This includes Steve’s resources that go with each part of the process – and you can see that the tools that go with search/discovery are very different from those suitable for execution.

    Since all of our project organisations already exist, it’s natural to treat expansion as an execution problem. However, it’s not so simple. Product-Market Fit does not map across borders – which means that market entry becomes a discovery problem again. Our least successful GCP projects have been ones where we’ve treated discovery problems as execution problems – and it’s a common issue for existing organisations.

    All this is another way of saying that existing organisations need to be able to do both: discovery and execution.

    Blank originally looked at the two as either/or, and the big problem that he identified was startups trying to act like established firms. In other words, if they skipped discovery and went straight to execution (e.g. writing a business plan first), it increased the chance of failure.

    As he’s been working more with established firms trying to innovate, his view has evolved – now he shows how discovery and execution integrate:

    horizons-with-bus-model

    This, of course, is another version of the ambidextrous organisation issue that Ralph Ohr has been digging into deeply.

    In my work on the GCP projects, as well as with CSIRO and in consulting with private firms, I’ve seen that the problem of treating discovery like execution is pervasive in all kinds of organisations.

    With the lean startup movement, we now have a set of tools that startups can use to make sure that they do discovery first, then execution. The toolkit for established organisations is still being put together. Here are some thoughts to help with that:

    • Discovery is a lot messier than execution. Business-as-usual works for execution problems, but for discovery problems we have to invent a new business-as-usual. This means that uncertainty is much higher, because we don’t know what will work. This is fine to admit within a startup, but often dangerous in established organisations, where giving the illusion of false certainty is often viewed as less risky than honestly discussing the likelihood of success. Consequently:
    • Discovery processes can look like incompetent management. We’ve run into this on some of our GCP projects. The team asks what seems like a straightforward question about expected returns, competitive advantage, or supply chains. When the client doesn’t have an answer, the team often loses faith in them. Not knowing these things is indeed a danger sign for an execution project, but it’s completely normal for a discovery project. That’s why it’s critical to understand what you’re working on.
    • Business-as-usual metrics will kill discovery projects. I frequently see organisations go through a discovery process to build a new business model to support a great new idea, then blow the whole thing when they plug the result into their normal measures of success. In an execution project, we can use all the normal accounting tools – return on investment, expected margins, etc.

    This last point is crucial, and possibly the biggest issue for established firms that are looking to add business model innovation to their toolkit. Fortunately, there are some good thinkers working on this.

    Start by looking at Ralph Ohr’s work on ambidextrous innovation – this has been a top agenda item for a few years now.

    Paul Hobcraft just wrote a great post on building innovation metrics.

    The article that I still use on this topic is by Geoffrey Moore in the Harvard Business Review. It’s from 2007, and one of the first (and still one of the best) applications of the three horizons framework that I’ve seen. Includes a number of very practical steps for established firms to use to support innovation in H2 and H3 projects.

    One deceptive issue in discovery projects is that the initial target market looks a lot smaller than established firms might be used to. Even firms that are used to targeting a mass consumer market need to think about Product-Market Fit for a small niche for ideas that are potentially disruptive. New business models start in niches – ignoring small initial markets is one of the most effective way to kill a discovery project.

    This means that even for established organisations, tools like lean analytics and pirate metrics for startups are essential. These focus around issues like traction, retention, and other measures that indicate that we’ve hit the target for our niche. These metrics look very different from execution metrics – using multiple sets of metrics is an essential element of ambidextrous innovation.

    The project that Steve and I just finished working on was definitely a discovery project. If we had realised this from day one, it would have made the team’s job easier.

    To succeed at innovation, we need to stop treating discovery like execution. They’re two different things, and we need to be good at both of them to thrive.

  • Key Issues in Innovation Management – Revisited – Part 1

    Key Issues in Innovation Management – Revisited – Part 1

    At the beginning of 2013, Tim Kastelle and I identified four key issues in innovation management for the time to come. From our point of view, all of the issues pinned down at that time have gained significant importance, are being intensively debated and can still be considered cutting-edge for companies to stay ahead in managing innovation. Let’s have a brief look at each of them:

     

    Differentiating and integrative innovation concepts

    Innovation can’t be tackled through broad-brush recipes or tools. It comes in various facets and requires integrative concepts and approaches that account for the integration of those different contexts as well as their reciprocal tensions. An increasingly popular case in point is the “Three Horizons” framework, which aims at integrating an organization’s entire innovation spectrum by means of three distinct time and scope categories – all of which feature particular purposes, conditions and requirements. Another example is the recently introduced strategy framework by Martin Reeves, Knut Haanæs, and Janmejaya Sinha from BCG. Here, dedicated strategies and innovation approaches are defined for different business environments, characterized by the factors predictability, malleability and harshness (see figure below). In most cases, companies have to deploy multiple strategies simultaneously in order to serve different environments they operate in. Let’s remind: One size does not fit all.

    Differentiated Strategies

     

    Reinvention through business model innovation

    Accelerating dynamics and pace of disruption in most industries, in particular triggered by the perfusion of new technologies, lead to decreasing life times of existing  business models. This requires companies to proactively or reactively innovate their business models in order to remain competitive. In either case, companies are frequently forced to reinvent themselves by changing the way they have been doing business so far. Recent research has confirmed successfully disrupting as well as outperforming companies to be significantly more engaged in business model innovation. However, it has also turned out that creating new or innovating existing business models by (re-)combining internal and external capabilities requires in particular protected spaces – separated from but connected with the core – and CEO sponsorship.

    Outperformer Innovation Portfolio

     

    Open and social co-creation

    Enabled by the accelerating pace of digitalization, a new model for value creation is taking shape – spreading in almost any industry. Rather than on delivering mass-scaled products or services, the focus is on creating data-based platforms that enable a number of stakeholders, such as customers, partnering companies and third party contributors, to participate in co-creating highly contextualized solutions. Popular examples are: Apple’s App Store, Google Play, GE, Amazon, Alibaba, Kickstarter, AirBnB, 3M or Ebay. The value proposition of those open platform business models is a more customer-centered, even individualized delivery of services and solutions by leveraging access to an entire ecosystem. The platform environment is mostly characterized by a tension between collaboration and competition of the participating companies, often referred to as coopetition. IDC predicts that by 2018 more than 50 percent of large enterprises – and more than 80 percent of firms with advanced digital transformation strategies – will create and/or partner with platforms. Platform business models tend to affect, oftentimes disrupt, multiple industries over time. For one, attached ecosystems are mostly cross-industry. For another, the underlying business model logics or deployed digital algorithms often allow to be transferred from a core industry to an adjacent one. Uber is a current prime example: they are attempting to expand into healthcare and travel.

    Even traditional producers of physical products can benefit from platforms by augmenting their products with communities. These communities stimulate social engagement around the product through participation in forums, sharing, collaboration or even user-driven innovation by co-creating new products. Social interactions strengthen emotional attachment and trust on the customer side, resulting in increased brand premium and prevention from being commoditized in increasingly competitive and transparent environments. Cases in point: Lego and Burberry.

    The challenge for companies will be to find their positions in those upcoming platform ecosystems. Not every company has the capability and influence to act as an orchestrating platform builder. But even participating in other firms’ ecosystems can be highly attractive, as demonstrated by e.g. several app developers. As can be seen from the figure below, co-creation platforms feature some special characteristics that have a major impact on shaping new market environments:

    • Created solutions are integrated and often cross-industry.
    • Interactions are complex and unpredictable. Leadership through vision and influence, rather than command and control, is required.
    • Network effects occur, inducing strong monopoly tendencies.
    • Winner-takes-all dynamics play out.

    These will be major rules, most upcoming 21 C businesses will play by. We are headed towards a co-creative platform economy.

    Co-creation platform
    Adapted from: https://nbry.wordpress.com/2014/06/27/massive-platforms-for-cocreation-the-new-normal-22/

     

    Culture of experimentation (and speed)

    In the first place, experimentation is about testing assumptions and hypotheses by means of a scientific learning approach. Recently, experimentation in innovation management is particularly facilitated by intensified use of (rapid) prototyping. In particular for industrial products this, in turn, has been stimulated by maturing 3D printing technologies. A culture of experimentation is becoming increasingly important for companies – in manifold directions:

    • Disruptive and radical innovation: If a company intends to pursue radical, or even disruptive innovation by introducing a novel technology or business model, this can only be accomplished through initial ideas or visions, followed by continuous, iterative testing for customer adoption. It requires a more deliberated approach, such as the lean startup process, design thinking or a combination thereof. Tip: Tim Kastelle has posted a worthwhile series on how to implement lean startup for innovation initiatives.
    • Unpredictable environments: Inherently dynamic and unpredictable industries (such as technology, software, fashion or internet retailing) require experimentation without predefined goals, embedded in the operations, to increase variance. A variety of options enables the company to adapt quickly to changing conditions by selecting the most promising ones and scaling them up. Operating in unpredictable environments relies on an agile organization, following an adaptive, evolutionary and more bottom-up approach, resembling complex adaptive systems, e.g. in biology. A well-suited way to govern this approach is to manage a portfolio of initiatives.
    • Incremental innovation: Even in highly mature industries, such as automotive, experimentation gains ever more importance. While stage-gate processes have traditionally been favored in predictable environments, a new generation of product innovation process is taking hold due to increased pressure: lean innovation. It combines an agile front end with a lean back end in order to increase effectiveness (hit rate) and efficiency (cost and resources) as compared to highly linear stage-gate processes.

    We can see that experimentation comes in different flavors, depending on the innovation context. However, all of them share the following: experimental approaches are a prerequisite for innovation speed and therefore help address time-efficient development and also organizational adoption of new technologies – two of the most pressing issues companies will  be facing in the time ahead. Companies that fail to adapt and implement emerging technologies quickly enough, will be at risk of falling victim to “Digital Darwinism” and becoming obsolete. Conclusion: developing a culture of experimentation is vital for both building new businesses as well as for competing in existing businesses – with distinct characteristics, though.

  • Innovation Force = Innovation Mass x Acceleration

    Innovation Force = Innovation Mass x Acceleration

    We’ve got a problem with our accelerators. Too often, we’re trying to accelerate ideas that aren’t ready for it, and the outcome is lower impact that than we hoped for.

    A couple of years ago, The Startup Genome Project released their first report on the factors that lead to success and failure. The leading cause of startup failure was premature scaling, defined as:

    …spending money beyond the essentials on growing the business (e.g., hiring sales personnel, expensive marketing, perfecting the product, leasing offices, etc.) before nailing the product/market fit.

    That definition comes from Nathan Furr, who goes on to say:

    Oh yes, and by the way, premature scaling also kills innumerable new projects at existing businesses as well, so innovators everywhere, beware.

    …

    In other words, they are doing things that seem to make sense, like investing to build the product, hiring good people to help them sell it, developing marketing materials, and essentially doing all the kinds of things that big companies with lots of resources do when they are executing on a known opportunity. But most startups are chasing an idea: the founders, no matter how much they believe in their idea, are operating on a guess about an unknown opportunity with a potentially unknown solution. All these unknowns mean we need to manage the process of coming to market differently and number #1 on the list is to avoid spending money scaling the business before you have really nailed what customers want and how to reach them.

    This ties in with another piece of data from the Startup Genome Project report – that the successful startups pivot on average just under two times. This is part of nailing what customers want and how to reach them. Their results look like this:

    Pivots and success

     

    This shows that your best chance of success come when you pivot about two times. You can go overboard with pivots though too, which leads to worse outcomes. If you scale prematurely, you end up with zero pivots, which also leads to worse outcomes.

    Here’s an idea that might help with this – Innovation Force:

    Innovation Force = Innovation Mass x Acceleration

    This has important implications for innovators:

    • You can’t accelerate nothing. So, yes, you must move faster than you think you can. The Innovation Mass in our Innovation Force equation comes from finding Product-Market Fit. If you don’t have this, it doesn’t matter how fast you move – you won’t have the impact you seek.
    • This is important in established firms too. Greg Satell points out that established firms have a lot of built-in advantages over startups, which they shouldn’t give away by trying to act like startups. This is absolutely true. However, one area in which they should try to act like startups is building business model innovation capability. Just as most startups fail, most new product launches from established firm also fail. For Horizon 2 and Horizon 3 innovations (growth-creating ones), Steve Blank shows how business model innovation and the three horizons model integrate:horizons-with-bus-model
      The norm in corporations is to push every new idea through the business-as-usual business model. This reduces Innovation Force. We need Product-Market Fit just as much here, in order to have the desired impact.
    • Product-Market fit must be the goal for both startups and established firms. This increases Innovation Mass – once you have that, then you can accelerate.

    This idea extends to regions as well. Unitus Seed Fund released an interesting report last year, which included an international survey of accelerators. Here is how they map it out:

    StartupEcosystem

    It follows from this that what they’re calling co-working spaces, incubators, accelerators and hyper-accelerators should all have different programs of work, since they are working with ideas at differing stages of development, which in turn have differing needs.

    I visited GridAKL and BizDojo – Wellington last week in New Zealand, and they seem to be getting really good results while working across several of these stages. However, this is rare.

    Based on my experience, there are two distinct stages in developing innovative ideas. The first is to find Product-Market Fit (this is getting up to IRL5 if you are using the Investment Readiness Levels). This is building Innovation Mass. The second stage is to build the rest of the business model (getting to IRL9), getting customers, getting funding, and starting to get some traction. This is Acceleration. If you maximise both, then you will have the biggest possible Innovation Force – which is what drives successful impact for our new ideas.

    These two stages correspond to the Incubator and Accelerator stages in the Unitus model. This shows us how to maximise Innovation Force at an regional level.

    We need places and programs that help ideas get Product-Market Fit. The number of ideas getting to that level is the Innovation Mass for a region. Then we need places and programs that will help people accelerate these ideas – the Accelerators. One part of this that Unitus didn’t look at is Corporate Accelerators, which also play a role here.

    Successful regions need all of this to be happening. However, many of them only focus on the Accelerator part because this is where the glory (and the money!) is – this is the most common mistake I’ve seen.

    Here is another diagram from Steve Blank illustrating the mix of programs needed, with Silicon Valley examples:

    Incubator Accelerator Ecosystem

    We all want our innovations to change the world. This requires Innovation Force. In turn, Innovation Force is the product of Innovation Mass (Product-Market Fit) and Acceleration (building growth with customers). To maximise Innovation Force, we need both Mass and Acceleration to be big, and this is true for startups, established firms, and regions.

  • How to Make Good Lean Startup Hypotheses

    How to Make Good Lean Startup Hypotheses

    When teams start out with lean startup, they often build hypotheses that are too precise – we assume we know more than we do. Imagine that we’re trying to build a startup called ScotchFinder – it’s like AirBnB, but for Scotch! Our first hypothesis often looks like this:

    We believe that: people will be happy to share their rare scotch with others.

    We will test this by: interviewing 30 people outside a liquor store.

    We are right if: more than 70% or people say they like the idea of ScotchFinder.

    Obviously, it’s better to start with this than it is to go into interviews blindly. However, this hypothesis/test combination can be improved in several ways.

    First off, who are the “people?” We often try to approach interviews as though we are looking for a representative sample of the population. This is especially true for scientists, as this is how we’re trained to do surveys.

    But that’s not the best way to do customer development. Remember, we’re looking for our first market segment – a specific group, with a specific problem that we can solve. Once we zero in on this group, our sample might end up being very unrepresentative, and that’s fine.

    The second problem: what kind of liquor store? Are trying to make something for people that already buy premium scotch? Then we’ll want to do our surveys outside a store with lots of good Scotch. What if we’re trying to get people to start drinking Scotch? Then we might be better off doing interviews at places selling lots of beer. What if we’re aiming to get people that normally drink Scotch in bars to start drinking it at home? Maybe we need ScotchFinder drones!

    ScotchFinder drone!

    For each of these three segments, we need to find people in different places, because they’re all living their lives differently. ScotchFinder may eventually serve all three groups of people – but right now, we need the group that is dissatisfied with what they’ve currently got.

    The final problems are with the last question. If we set up the hypothesis like this, then we’ll spend too much of our interview time pitching ScotchFinder, and not enough learning about what problems people are running into when they try to drink Scotch.

    Finally, again, this isn’t a poll. The majority doesn’t rule. We’re looking for a market segment that we can absolutely dominate. What if we do our interviews (or do a survey), and 97% of people say that they (correctly) think that ScotchFinder is a stupid idea. Then it’s dead, right?

    Not necessarily. What if the 3% that do like ScotchFinder are all the same? What if they’re all well educated, double-income no kids couples, living in the same part of town, and for some reason, that part of town has no good liquor stores? The ScotchFinder Drone may well work! And if it does, this might be a segment that we can dominate, which is a great way to start out.

    In our early interviews, we need to set the bar pretty low for our hypotheses. We don’t know enough yet to use percentages, as though our 30 interviews are the same as a mini-survey. We’re actually looking for themes.

    This is what Eric Ries says about this in his upcoming book The Leader’s Guide:

     

    Don’t bog new teams down with too much information about falsifiable hypotheses, and try to resist the urge to critique teams’ hypotheses when they are early in their Lean Startup journey. Because if we load our teams up with too much theory, they can easily get stuck in analysis paralysis. I’ve worked with teams that have come up with hundreds of leap-of-faith assumptions—page after page after page in a tiny spreadsheet of all the things that had to be true for the project to take effect. They listed so many assumptions that were so detailed and complicated that they couldn’t decide what to do next. They were paralyzed by the just sheer quantity of the list.

    Strategy: Simplify.

    Take the photo-sharing product example. We might start out by saying “100% of parents want to share photos.”

    Now we have a prediction that we can test. Creating an MVP has gotten much easier. I can test out my assumptions with the first ten parents I find—and I can find them anywhere: online, in a coffee shop, or wherever. Over time, I can come up with more specific, sophisticated experiments.

    Remember, our goal is to get our teams to write down whatever it is that they believe already—no matter how sloppy, ill-conceived, or foolish those beliefs are. It’s extremely difficult to talk entrepreneurs out of a bad idea; it’s much better to let reality be their teacher.

    Here’s another way to frame our initial hypothesis that might yield more usable learning:

    We believe that: double-income no kids couples in high-power jobs need a great way to socially share Scotch.

    We will test this by: interviewing 30 DINKs (and include a guess about where to find them)

    We are right if: we find a common Scotch-related problem shared by several (more than 6 or so) of them, which they are currently trying to solve.

    That looks vague, but that’s where we start. Once we have the problem identified, then things will become a lot more specific as we move to IRL 4 and above.

    We need to start our lean startup process with discovery – and that is harder to hypothesise. But we can’t look for false precision, that will lead us down the wrong path.

    Note: Over the past year, I’ve been running (with help, of course!) a bunch of Lean LaunchPad programs with the Commonwealth Science and Industrial Research Organisation (CSIRO) aimed at increasing the impact of all the great research that they’re doing. This is part of a series reflecting on what we’ve learned through the course of six programs involving 40 research projects and more than 250 people. The other posts are:

  • Move Sooner and Faster Than You Think You Can

    Move Sooner and Faster Than You Think You Can

    The day before we started our first Lean LaunchPad program with the CSIRO, Bill McKeague and I talked strategy over coffee. We had been working towards doing an LLP for over a year, and we were both excited, and scared.

    At one point, I said to Bill “Well, I’ve got no idea how this will go, but as they used to say in those old Mickey Rooney movies, ‘Let’s put on a show!’”

    We’d been working on this with our CSIRO partners Sarah King and Peter Kambouris for a while, but it still seemed like it had happened too fast, and we were too unprepared. For me, at least, scared was winning out over excited.

    This is normal when we do new stuff.

    I often forget getting out of my comfort zone requires actual discomfort…

    In running that first program, we learned (or re-learned) the lessons that we are trying to help others get as well.

    The first lesson is the importance of hypothesis testing. I often refer to that first one as our Minimum Viable Lean LaunchPad. We had no resources for it, so we ran as leanly as possible. And we were testing three hypotheses:

    1. Lean LaunchPad will be effective with deep technology research projects. Definitely supported, though it requires some modifications, which I’ll get to in another post soon.
    2. The scientists will see the value in customer development work. Verified, more enthusiastically than we expected.
    3. We can get the teams to do enough customer development interviews to have a big impact on their projects. We didn’t get there on the first try. This is what we’ve worked on the hardest in subsequent iterations.

    It was, of course, classic Build-Measure-Learn. We got the first version out into the world, and then improved each iteration subsequently. We’re still working hard to improve each new version.

    The second lesson is that we need to get our ideas out uncomfortably early.

    It turns out that all of those startup cliches keep getting repeated because they’re true:

    • “If you are not embarrassed by the first version of your product, you’ve launched too late.” – Reid Hoffman, LinkedIn founder
    • Move Fast and Break Things;
    • Done is Better Than Perfect. – Facebook slogans

    It was true for us, and it was true for the CSIRO researchers too. This gets at one of the biggest flaws in the way that we try to commercialise research.  The normal thing to do is to do all of the research first, and then hand off the technology to business development people to bring it to market.

    This model assumes that technical risk is the biggest problem we face. While technical risk is higher in deep technology research than it is if we’re building a normal startup, market risk is still the biggest risk that we face.

    This means that we can’t afford to engage with the market only after the research is done. Instead of moving up the Technology Readiness Levels first, then going up the Investment Readiness Levels second, we must work on both simultaneously.

    TRL to IRL

    The reason for this is that engaging with the people that will benefit from our research leads to better research.

    Here’s how I put it at the final session of that first Lean LaunchPad:

    LLP Workshop 01 – Introduction from Tim Kastelle on Vimeo.

    This was my fourth hypothesis going into this: doing Lean LaunchPad leads to better science. It’s turning out to be true, which has been the biggest surprise for the scientists – and it’s deeply counterintuitive.

    This comes up in larger organisations too. They are uncomfortable with the lean startup approach because they worry about eroding their brand through running experiments. This is why thinking about minimum viable products as learning objects is so important.

    We’re getting better with each Lean LaunchPad program that we run. And fortunately, I’ve been learning my lessons. I’m working right now with my colleagues Scott Thomas and Kate Morrison in launching another lean startup program that seems to fast, too soon. But we’ll get it out there, then make it better.

    If we wait for something to be perfect before we launch it, we’ll never launch.

    Note: Over the past year, I’ve been running (with help, of course!) a bunch of Lean LaunchPad programs with the Commonwealth Science and Industrial Research Organisation (CSIRO) aimed at increasing the impact of all the great research that they’re doing. This is part of a series reflecting on what we’ve learned through the course of six programs involving 40 research projects and more than 250 people. The other posts are:

  • A Minimum Viable Product is an Object for Learning

    A Minimum Viable Product is an Object for Learning

    The idea of a Minimum Viable Product (MVP) is frequently misunderstood. From the name, people often think of it as a prototype, but really, it’s a learning object.

    Here’s an example from our first Lean LaunchPad program. The ASPIRE team was in that group – and they’ve recently launched their platform. Here is how they describe it:

    ASPIRE is an online marketplace which intelligently matches your business with potential purchasers or recyclers of your waste by-products. It saves on your disposal costs and cuts the amount of waste going to landfill. ASPIRE is run by the CSIRO in collaboration with local councils and business networks.

    Register your outputs and inputs to find a match.

    That’s a pretty clear statement of their value proposition. How did they test it? Their first thought was to build the website, but they had neither the time nor the money to do that during the LLP program.

    Here’s what they did instead:

    ASPIRE Concierge MVP

    The hypothesis that they needed to test was:

    We believe that small manufacturers in Victoria will value a service that helps them recycle waste streams.

    To test that, they got 58 people in the room from 32 small manufacturers. Then they did on paper what they proposed to do with their website – list the industrial waste streams they produce, along with the process inputs that they need. Then they looked to see if there were any matches between firms. And there were – 62 of them, in fact, nearly two per firm!

    Now, obviously, this is a very primitive version of the platform that they ended up building. The launch version has a lot of smarts built into it to do that matching automatically.

    But this is a great example of an MVP. They didn’t build a small version of their final product, they built an experience that validated whether or not people had a genuine need for it. The results were an overwhelming “Yes!”

    This is a low-fidelity MVP. The Investment Readiness Level scale includes steps for both a low-fidelity MVP (IRL 5) and a high-fidelity MVP (IRL 7). A high-fidelity MVP is something like a prototype.

    I think that the best way to think about a low-fidelity MVP is a pretotype – and pretotype.org has a bunch of great resources to help you test and validate your ideas quickly and cheaply. That Resources page includes free .pdfs of two books on the topic, and they are both excellent.

    The Pretotyping Manifesto includes a great set of principles to help you build your low-fidelity MVP:

    Pretotyping Manifesto

    Here are some more resources to help you build and test an MVP:

    The Lean Startup approach is based on the Build-Measure-Learn loop. When you combine hypothesis testing with low-fidelity MVPs, you get into the Build-Measure-Learn loop very early in the process.

    It’s a great way to learn if you’re really delivering value for your customers. The best way to think of an MVP is an object for learning.

    Note: Over the past year, I’ve been running (with help, of course!) a bunch of Lean LaunchPad programs with the Commonwealth Science and Industrial Research Organisation (CSIRO) aimed at increasing the impact of all the great research that they’re doing. This is part of a series reflecting on what we’ve learned through the course of six programs involving 40 research projects and more than 250 people. The other posts are:

  • What Assumptions Underlie Your Business?

    What Assumptions Underlie Your Business?

    Every social activity that we undertake is subject to underlying assumptions.

    Think about music – here is a great section from the book Art Worlds by Howard Becker:

    Consider what changing from the conventional Western chromatic musical scale of twelve tones to one including forty-two tones between the octaves entails. Such a change characterized the compositions of Harry Partch. Western musical instruments cannot produce these microtones easily, and some cannot produce them at all, so conventional instruments must be reconstructed or new instruments must be invented and built. Since the instruments are new, no one knows how to play them, and players must train themselves. Conventional Western notation is inadequate to score forty-two-tone music, so a new notation must be devised, and players must learn to read it. (Comparable resources can be taken for granted by anyone who writes for the conventional twelve chromatic tones.) Consequently, while music scored for twelve tones can be performed adequately after relatively few hours of rehearsal, forty-two-tone music requires much more work, time, effort and resources. Partch’s music was often performed in the following way: a university would invite him to spend a year. In the fall, he could recruit a group of interested students, who would build the instruments (which he had already invented) under his direction. In the winter, they would learn to play the instruments and read the notation he had devised. In the spring, they would rehearse several works and finally would give a performance. Seven or eight months of work finally would result in two hours of music, hours which could have been filled with more conventional music after eight or ten hours of rehearsal by trained symphonic musicians playing the standard repertoire. The difference in the resources required measures the strength of the constraint imposed by the conventional system.

    The difference in the resources required also measures the amount of assumptions in the conventional system. Here’s another example: money has no inherent value. How long would it take to navigate daily life if we had to negotiate trading terms for every single thing that we needed? It would require all kinds of extra resources. We have many other sets of conventions like this that allow us to interact – things like time, every single language, standards of dress, and so on.

    Conventions like this save us tons of time and hassle every single day. My brother Gabriel is a fantastic violinist, arranger, composer and generally excellent music guy. Here is his response to the Becker quote:

    I was very lucky a year ago to be part of an “audience” of four squeezed in to the (big!) room at Montclair State where Harry Partch’s own instruments are housed, on the occasion of the final dress rehearsal of several of his landmark works before their reprise at Lincoln Center later in the week…pretty freakin’ amazing music!!

    IF (and it’s a huge ‘if’) one really has something so different and valuable to create musically than the millions before, then MAYBE eschewing centuries if not millenia of tradition for the sake of incredible INefficiency and frustrations and utter lack of infrastructure may be worth it… Maybe, yes, for example, in the very very rare case of Harry Partch…

    Becker is making the point that constraints enable creativity, as you can also see in that statement from Gabriel.

    This is very circuitous route to a very important point: every time we build a business model, there are a TON of assumptions that lie underneath it, and some are more obvious than others.

    Here is what it means for lean startups: every assumption is an innovation opportunity.

    You start with your first proposed business model, which consists of a bunch of guesses.

    Business model guesses

    The next step is to convert these guesses into testable hypotheses – this is why the National Science Foundation in the US has called the Lean LaunchPad approach “the scientific method for startups.”

    There are plenty of tools for doing this – we’ve used Strategyzer. Here is what one of their hypothesis testing cards looks like:

    Hypothesis testing

    The structure for a business hypothesis and test is:

    • We believe that…
    • To validate this belief, we will…
    • And we will measure…
    • We were right if…

    Once you’ve done this, then you have to go do customer development interviews, and record your results. Was the hypothesis right? If so, great! Move on to test the next one. If not, you need to build another hypothesis to test.

    At its simplest level (simple, but not easy), business hypothesis testing lets you test out the hypotheses that must be true for your business model to work.

    At a deeper level, you can be like Harry Partch and think about the fundamental assumptions that underlie the area you’re working in.

    I talked about money – there are all kinds of assumptions with it, like:

    • The value of money is guaranteed by government fiat. Challenging that assumption led to Bitcoin.
    • Money requires currency. Challenging that assumption led to mobile money platforms in Africa like Sente, where money is stored in phone accounts.
    • People keep money in banks. African mobile money platforms also challenge this assumption, where a huge percentage of the population has skipped banks entirely.

    Assumptions aren’t automatically bad. For day-to-day things like language and money, assumptions save us a great deal of time and effort.  Nevertheless, when we are doing new things, it is useful to articulate the assumptions that underlie the work. Challenging these assumptions can lead to potential innovation opportunities. At the least, clarifying assumptions gives us the hypotheses that we must test to improve our chances of success.

    Note: Over the past year, I’ve been running (with help, of course!) a bunch of Lean LaunchPad programs with the Commonwealth Science and Industrial Research Organisation (CSIRO) aimed at increasing the impact of all the great research that they’re doing. This is part of a series reflecting on what we’ve learned through the course of six programs involving 40 research projects and more than 250 people. The other posts are:

    Another note: if you’re interested in Harry Partch, here’s a good documentary about him and his music.

  • How Big is Your Market and Where Will You Start?

    How Big is Your Market and Where Will You Start?

    Facebook currently has 1.55 billion monthly active users. 1.55 billion!

    To get to that size, they must have been building for a huge market right from the start, right? Well, no.

    Here is what the first homepage for TheFacebook looked like when it launched:

    TheFacebook

    It was a digital version of the paper student directory that Harvard students were given when they enrolled. As you can see, its functions were very limited. The website was aiming at Harvard undergrads – about 8000 people. And within a couple months of launching, nearly everyone in that group was using the site.

    After a while, they dropped the “The” from “TheFacebook” and expanded to all of the Ivy League – around 40,000 people. Once again, the adoption rate became very large, very quickly. They slowly added features, like The Wall – this happened when they expanded their market to everyone that had a .edu email address – 20 million people or so.

    About five years after it launched, Facebook finally opened up to everyone.

    Their markets looked like this:

    Facebook Markets

    When you launch something new, you need to have a handle on a couple of things: what the overall potential market is, and who your first niche will be.

    The problem that we often have when we launch something new is that we often only think about the big number. So we hear things like “our target market is everyone” or “analysts say that total value of our field in 2025 will be $25 billion!”

    Unless you’re Facebook, your customer is never “everyone.” You have to find that first niche – you have to focus. You need to figure out what your version of Harvard for Facebook will be.

    In one of our Lean LaunchPad programs, we got Mick Liubinskas from Pollenizer and Muru-D in to talk about focus. The Muru-D link has a video of one of his talks, which you should check out. You can also find a .pdf of the book he wrote with Phil Morle on this here.

    Mick made a bunch of great points, but his big one is that you win through focus – and you focus by identifying the customers with the most pain that you can remove. He drew a graph that looked like this:

    Customer Pain

    What it shows is that in most markets, there are a small number of people whose needs aren’t being met at all – their pain level is high, while the majority of people are basically satisfied with what they’re getting.

    Here’s an example. Do you need a driverless car today? Even if you’re excited about the potential of the technology, the pain of not having one probably isn’t too high. There are plenty of other options now – for the majority of people the pain of driving themselves isn’t too high.

    So if you’re doing research on autonomous vehicles today, where do you go to maximise the impact of your work? Despite this week’s deal between GM and Lyft, driverless cars for everyone are still years away. Does that mean there’s no opportunity?

    No. Many people don’t realise it, but autonomous vehicles are already at work – in the coal mines of Western Australia.

    Why is this the first niche for autonomous vehicles? Because the pain there was really high. The trucks are enormously expensive, so adding the expense of the driverless technologies is relatively trivial. More importantly, the mining companies out there have really struggled to find truck drivers. During the mining boom, there were very few people available, so the miners ended up paying astronomically high salaries to people for driving trucks. By introducing autonomous vehicles, the mining companies are able to eliminate the cost of drivers (and also reduce industrial relations issues in that area as well), while also improving safety on their mine sites. In time, the firms involved with this project will learn, get better, and expand into bigger markets.

    This is the way that new technologies go – you start in the niche with the highest level of pain, which enables you to learn about the technologies, about peoples’ needs, and about what your business model should be.

    These ideas raise some important points:

    • Find your first segment through customer development. The way to find this first niche is through customer development interviews. They help you understand what people are trying to achieve, and what their problems are. If you do them correctly, you can also find the people and firms that are currently experiencing the highest levels of pain – they are the ones that are actively looking for solutions.
    • You need to talk about both the high-end potential and your first niche. If Mark Zuckerberg had asked for a bunch of money to build a website for 8000 Harvard students, he would’ve been laughed out of the room. But if he had gone to investors and said “I’m building a website targeting every person in the world,” he also would have been laughed out of the room. You need both parts – the long-term upside, but also your first wedge into the market.
    • Niches with pain are what leave openings for disruptive innovations. It’s no coincidence that Uber started in San Francisco, where the taxis are notoriously bad, rather than in London, where the taxis are outstanding. Two of the big innovation research areas recently have been user-led innovation, and disruptive innovation. Both of them depend on niches experiencing pain as the drivers of innovation opportunity. In an established market, the dominant players focus on reach the majority, which is the group with relatively low levels of dissatisfaction with the current state-of-play. That creates opportunity around the edges.

    If you’re doing a lean startup, you need to do two things. Identify the potential users in your market that are currently experiencing the most pain, and start with them. Once you’ve identified this first niche, then you can plan your expansion route.

    To succeed, you need to know both things.

    Note: Over the past year, I’ve been running (with help, of course!) a bunch of Lean LaunchPad programs with the Commonwealth Science and Industrial Research Organisation (CSIRO) aimed at increasing the impact of all the great research that they’re doing. This is part of a series reflecting on what we’ve learned through the course of six programs involving 40 research projects and more than 250 people. The other posts are:

  • Is Our Business Model Ready to Launch?

    Is Our Business Model Ready to Launch?

    Why do new ventures fail? There are three areas of risk when we launch something new:

    1. Technical (or product) risk: can we build it?
    2. Market(or customer) risk: does anyone want it?
    3. Cash (or business model) risk: if people want it and we build it, can we make enough money from it?

    When most people try to launch something, they often focus too much on technical risk. This tendency is amplified when the projects are based on scientific research. This is a mistake – market risk is by far the biggest.

    Here is what the Lean Startup team says:

    If you’re wondering which kind of risk you face, let me help you out: It’s customer risk. Nearly always, it’s the biggest question, because you simply don’t know the value, if any, your new product has for potential customers. When I say, “Nearly always,” I mean: this is so often the case, you should assume it’s true every time.

    The tricky part is that commonly, product risk looks more urgent. After all, if you’ve hit on an exciting new idea that you’re pursuing, you’re doing so because you believe other people will be interested in it, too. And if you assume the demand will exist, you’ll be tempted to make sure you can build the product before you offer it to people.

    The normal process in research-based organisations like universities, or the CSIRO, who I’ve been working with recently is to do the research first, which reduces technical risk. Then give the new technology to a business development team to bring it to market, which requires reducing market and cash risk. But if the biggest risk is market (customer) risk, is this smart?

    One of the key reasons for using a Lean Startup approach like Lean LaunchPad is that it helps you address all three types of risk simultaneously. Doing customer development interviews help you build a validated business model, which reduces market and cash risk.

    Steve Blank developed a tool that he calls the Investment Readiness Level (IRL).  It is designed to track your progress in building a validate business model, and it looks like this:

    investment readiness level

    It’s a great tool. Here are the advantages to using the IRL:

    • The Investment Readiness Level provides a “how are we doing” set of metrics
    • It also creates a common language and metrics that investors, corporate innovation groups and entrepreneurs can share
    • It’s flexible enough to be modified for industry-specific business models
    • It’s part of a much larger suite of tools for those who manage corporate innovation, accelerators and incubators

    When we started using the IRL in our CSIRO Lean LaunchPad programs, I noticed a problem – the teams often overestimated how far they had progressed. To help with this, I made a modified version of the IRL with criteria to assess for each level:

    Investment Readiness Levels
    Click on the picture to see a bigger version of it.

    I also included information on how many customer development interviews you need to be confident in your assessment.

    We used this in our most recent programs, and here are some observations:

    • It’s great for planning your learning. This gives you a pretty clear idea about what things need to be validated before you move to the next level.
    • While the levels are important, ultimately you need to check off all the boxes. All of the things included here help you build and validate your business model.
    • Teams tend to progress linearly over Levels 1-5, and then they start doing bits from several levels at once. The critical step in all of this is getting to Product/Market fit at Level 5, and the process for getting there is relatively straightforward. Once you do this, the sequence of actions is less important.
    • It’s possible to end up back at Level 0. Level 0 is “We have an idea.” We’ve had a handful of teams do 90+ customer development interviews which led to the conclusion that there currently isn’t a good use for the technology they’re working on. This isn’t a fun outcome, but it’s an important one – it allows them to move on other research.

    Lean LaunchPad flips research commercialisation on its head. One thing that we’ve learned in doing this is that good customer development actually changes the science back in the lab. This is critically important since research projects can run for five years or more  – it’s important to be working on ideas that will have impact.

    Science-based research will always have higher levels of technical risk than other ventures. However, market risk is still incredibly dangerous. It’s important to use tools like Lean LaunchPad and the IRL to reduce all of the risks we face when we’re trying to change the world.

    Note: Over the past year, I’ve been running (with help, of course!) a bunch of Lean LaunchPad programs with the Commonwealth Science and Industrial Research Organisation (CSIRO) aimed at increasing the impact of all the great research that they’re doing. This is part of a series reflecting on what we’ve learned through the course of six programs involving 40 research projects and more than 250 people. The other posts are:

  • The How and Why of Customer Development

    The How and Why of Customer Development

    Henry Ford famously didn’t say:

    If I had asked people what they wanted, they would have said faster horses.

    People often use this quote to justify not talking to potential customers, because people don’t know what they want.

    It’s true, people don’t know what they want. But you still need to talk to them. Why? Because they do know what their problems are.

    Over the past year, I’ve been running (with help, of course!) a bunch of Lean LaunchPad programs with the Commonwealth Science and Industrial Research Organisation (CSIRO) aimed at increasing the impact of all the great research that they’re doing. This is the first post in a series reflecting on what we’ve learned through the course of six programs involving 40 research projects and more than 250 people.

    The first thing that we’ve learned is that talking to people is essential.

    Here’s the issue: when we develop a new piece of technology, there are an infinite number of business models that you can build on top of that tech – here’s one good case study. Lean startup tools are a great way to discover the right business model for your new thing.

    Talking to people, or customer development, is the central tool in the Lean LaunchPad. It works really for building software startups, but it also works for commercialising research-based technology as well.

    When we think about our new technology, we have a business model for it in our heads. For example, when Alexander Graham Bell invented the telephone, his belief was that it would be too inefficient for person-to-person use. Instead, he thought that people would use the telephone to listen to musical performances when they didn’t have an orchestra in their town.

    In other words, the first business model in our head is made up of a bunch of guesses.

    Business model guesses

    Bell found out his business model was wrong through trial and error. But this is dangerous, and risky. Lean LaunchPad is the tool for converting those guesses into knowledge about what actually creates value for people. To do this, we must talk to about 100 people.

    This challenges most people, especially scientists. The process looks like this:

    Interviews

    Before you talk to anyone, our level of uncertainty about our business model is usually zero – we’re like Bell and his concerts. Then when we start talking to people, our level of uncertainty about our business model shoots up, and we feel incredibly confused about what to do.

    This confusion often makes people give up on talking to people, which is disastrous. The way to break through the confusion it to talk to more people. As we do, eventually we learn about what’s going on, and our level of confusion drops. In time it gets close to zero again – although if we’re smart, we always retain a bit of doubt.

    Here are some common questions about customer development interviews:

    • How do we know if it’s working? We use Steve Blank’s Investment Readiness Level scale as a measure of how far we have advanced in building our business models (see Part 2 in this series). In our programs so far, progress on the IRL has been pretty close to directly proportional to interview numbers.
    • Why do we need so many interviews? There are a few reasons. One is that when we start out, we aren’t very good at interviewing – it’s a skill we need to build. The second is  an idea in qualitative research called interview saturation. It is a state that you reach once you’ve talked to enough people that you stop hearing new ideas. The number of people you need to reach saturation will vary, but on average it’s around 25. To build a complete business model, we need to reach saturation on several different issues. You can see why we need 100 interviews to feel confident.
    • We’ll just focus on quality interviews, won’t that be better? No. First, you don’t know in advance which interviews will be the high-quality ones – especially when you’re starting out. The more people you talk to, the easier it is to figure this out in advance, but the only way to do it is to talk to a lot of people. Second, after you talk to a lot of people, you end up having a very high percentage of high-quality interviews from #60 on. The people that talk to me about quality over quantity have usually done less than 20 interviews, with maybe a handful of those being high-quality. The teams that have gotten up above 60 interviews usually have more than 20 high-quality interviews, which adds a lot of new data. In customer development, quality is an emergent property of quantity.
    • Steve Jobs didn’t do customer development, why should I? You’re not Steve Jobs. Also, the thing that Jobs did have was a very deep understanding of what problems people are trying to solve – customer development is the tool you use to gain that understanding yourself. The main idea is that we want to talk to people about what problems they have right now, and how they are currently solving them. As we learn about their problems, then we can figure out how to build a business model around our technology that will create value for them.

    There’s one last big question: what questions should we ask? Fortunately, there are a lot of great resources for this:

    If you use these links, you can build a good set of interview questions for your own customer development work.

    And you should do that, because it’s the best way to make sure that your great idea has the greatest impact on the world.

    Note: Over the past year, I’ve been running (with help, of course!) a bunch of Lean LaunchPad programs with the Commonwealth Science and Industrial Research Organisation (CSIRO) aimed at increasing the impact of all the great research that they’re doing. This is part of a series reflecting on what we’ve learned through the course of six programs involving 40 research projects and more than 250 people. The other posts are: