Archive for the “The Equity Kicker” Category

Nic Brisbourne’s view from London on venture capital and exploiting change in technology and media.

I’ve been thinking a lot about authenticity recently.

The first thing to say is that it’s a woolly concept. As an individual I am clearly me, and therefore of undisputed origin and not a copy. In common parlance then to be authentic is to be genuine, to be truly what we say we are. The problem with this is that most of us are, in fact, many different people. The person we are with our kids might be different from the person we are with our partner, which might again be different to who we are at work, and we might even be a different person depending on which group of friends we are with.

There’s a tempting notion that the true person sits somehow at the middle of these different external facing people, but if you subscribe to the view, as I do, that we are no more than the sum of our actions, then it follows that we are really just a collection of different people. That’s born out for me by the tension we sometimes feel between our different personas. I often suffer from inner conflict because I genuinely want to do more at home and at Forward Partners, but there’s no time for both. If there was one inner person governing everything then it should be possible to resolve the issue, but I find that when I’m in family mode my desires are different from when I’m in work mode.

All that said I am a firm believer that, generally speaking, if we can be more authentic we will be happier and more effective in our lives. Firstly, from a selfish perspective, maintaining multiple personas is tiring. We constantly have to remember where we are in order to remember how to behave and there’s cross over between the different areas of our lives that threatens to expose the differences. For most people this is a low level stress that’s eminently manageable, but it’s there and it impacts performance. I was discussing authenticity with a friend recently who partly thought of it as being able to say what he genuinely thinks. Many of us censor what we say a lot of the time, and that becomes exhausting after a while.

Secondly, the more authentic we are the easier it is for other people to trust us, making us more effective as friends, partners and leaders. The more knowable we are, the easier it is for people to rely on us, which means they can spend less energy worrying about whether we will do what we say and whether we will look after them.

Bringing this all together, it follows for me that the first key to being authentic is achieving an alignment between our different personas. The more aligned we are the more we have one true self, which makes us more genuine by definition.

However, there is a caveat. If we are successful in achieving an inner alignment, but there’s is a lack of alignment between what I want and what my friends, family or colleagues want, then being true to what I think all the time might make me feel better, but can put a burden on others. In most of our relationships we find a shared space that works for both parties. That space defines what we talk about, the topics we avoid, what we expect of each other, and a whole host of other things. The process of getting to know somebody is in large part a process of defining that shared space. If we unthinkingly change our behaviour to be more authentic then we unthinkingly change that shared space with each of our friends, colleagues and partners. That can be a jarring experience for them and could well be a selfish thing to do. You might be able to think of someone you know who has achieved a good degree of internal alignment but comes across as selfish. I know I can.

Which brings us back to alignment. The journey to authenticity is ultimately a shared journey towards alignment with everyone we share our lives with. Writing that sentence really made the penny drop for me, so I’m going to repeat it. The journey to authenticity is a shared journey towards alignment with everyone who shares our lives. Full alignment with everybody will be out of reach for most people, but the more aligned we can get the more authentic we can be and the better everything will work.

That’s one of the reasons why great leaders place huge stress on aligning their people around a unifying company mission and why many successful couples are aligned that their relationship is the most important thing in their lives. As I think this through we have many tools for building alignment in the work place (vision, mission, company values and OKRs spring to mind) but we don’t have anything comparable for our personal lives. That feels like a gap to me.



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Growing from a founder to a scale-up CEO is challenging. Thinking up game changing ideas couldn’t be more different to running a large business. Many transitions are required along that journey but the one that I’ve been thinking about recently comes when the first product starts to take off. To simplify, before then success comes from trying lots of things, but after that success comes from making one thing work.

Creativity is the main skill required in the first phase. It’s all about coming up with lots of ideas and seeing which ones have merit. It’s a time when the options seem limitless and new ones are opening up all the time. Conversations with customers and other industry players frequently go off on tangents revealing new opportunities and adding to the sense of upside. It’s all about getting a few irons in the fire and founders often have a growing belief that at least one of the ideas will work out, even if they aren’t sure which one.

Then one of the ideas starts to work. Customers are buying and levels of excitement and optimism grow still further.

We are now into the second phase. The onus has moved from trying lots of things to making one thing work, and that requires a very different mindset.

The first thing that’s required is focus. It’s difficult moving from adding irons to the fire (and feeling good about the security that brings) to taking irons out of the fire to focus on something that is promising but still unproven. For many founders giving up on the optionality of having lots of horses in the race is hard, and that’s despite the fact that ideas put on the backburner at this point can be re-ignited later. The difficulty isn’t rational. Everyone understands the benefits of focus from an intellectual perspective, but in practice many find it very challenging emotionally. Buckets of self belief play a part here too – most great founders believe they are snowflake special, which is great, but that confidence can give them the excuse to think that whilst everyone else should focus, they are good enough to keep all the options alive without compromising on delivery. I’m here to say that’s rarely the right strategy.

The second thing that’s required is discipline. The fun creative process of dreaming up new user flows and product features gives way to disciplined experimentation. For an ecommerce company or marketplace that means analysing the whole funnel from marketing spend through to checkout, looking where people fall out, and experimenting with fixes. Many good companies run regimented experiment programmes with a weekly cadence. Every seven days they identify a metric they want to move, develop a hypothesis on how to move it, implement an experiment to test the hypothesis, and then kill or roll out depending on the result.

Moving from adding irons to the fire to taking them out and from creativity to discipline is quite a shift. Writing this post has got the transition clearer in my mind. I hope it’s helped you too.

 



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A common mistake founders make at the early stages of a company is to put too much detail into their business plan. Sometimes we see a level of detail which amounts to spurious accuracy given the stage the company is at and the attendant uncertainty. Two concerns follow:

  1. The founder doesn’t understand how much things change in startups (or, worse, are trying to project a greater level of certainty than they feel)
  2. They may not be flexible enough to ride with the punches

This happens most often with projections about how products will work and with financial models. I won’t name companies but one we spoke with recently was building a three sided marketplace. They were pre-launch but had developed a complicated six step transaction flow they thought their customers would go through which included commission splits and transaction timelines. They had taken users through the potential flow and got positive feedback but I was left thinking that the questions they asked those users wouldn’t have passed the Mom Test and that there was a high chance that when they launched the process would bamboozle even their early adopters. For me, it would have been much better if they had focused on describing the value participants would garner from using the service and either planned to manage transactions manually in the early days or documented a very simple transaction flow. That would have shown me that they understood the inherent uncertainty in building products and would have had the additional benefit of really hammering home the value proposition.

When it comes to financial models people sometimes take false comfort from the spurious detail they’ve built in, which results in relying on the model rather than on common sense. I’m thinking now of an ecommerce company that was in its first six months post launch. Pretty much all their traffic came from Google and in their plan they had projections for growth in organic traffic and for traffic from Facebook, referrals, and other new channels. That showed they were planning to diversify their sources of traffic and understand the different options available to them which isn’t a bad thing in and of itself. However, when we asked them to explain why they believed their customer acquisition costs would reach the levels they were projecting their answer was pretty much “because the model says so”. Models can, of course, be made to say anything and their answer left me feeling that they didn’t really understand the drivers of their unit economics. It would have been better to say “We believe the major levers for reducing customer acquisition costs will be increasing organic traffic and reducing our CPAs on Google. Based on [insert justification here] we believe that X and Y are achievable.” Modelling at that level would have been sufficient too, with commentary about plans to expand to other channels in the pitch deck.

Don’t let over detailed plans distract you from the bigger picture and the flexible thinking required to navigate the startup ecosystem successfully.

 



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Consider the three charts above. They are all representations of the same exponential function where the Y value is equal to two times it’s previous value. The first chart shows the data series for the first twenty values, the second chart shows the data series for values on through ten, and the third chart shows the data series for values eleven through twenty. Notice that all the charts look similar and that the second and third charts are virtually identical.

The takeaway: when you are on an exponential curve the trajectory looking forward is the same at any point on the curve.

For me, at least, this is highly counter-intuitive. I think that’s because the mind sees change in absolute rather than relative terms. I know that things like processing power, storage, bandwidth (fixed and wireless), solar, and genome sequencing have been improving exponentially for some time so I expect to feel the change to a much greater extent today than I used to, and by extension my natural inclination is to expect that change will get bewilderingly fast in the next decade or two.

However, when you think it through properly our experience of change will remain the same. There will be a doubling each year (or halving, or whatever the exponential function is).

This is all very abstract. Let me try and make it real. When I think about Moore’s law and the acceleration in computer power, it feels that the change should be faster than it was when I was a kid. I remember when I was 8 the Sinclair ZX81 was released and then the big news a year later was when the Sinclair ZX Spectrum came out. Memory went from 1k to 16k and there was colour! More importantly for me at the time, the games were much better :). That was a notable advance, however, for the next few years after that there were no really major steps forward. When I compare that to progress in computing over the last few years or so it seems to me we have seen a similar rate of change, although we have to look to the cloud services we use rather than our personal devices to see the change. I will call out Uber and the Amazon Echo as two new things that are changing the way we go about our lives in a way of similar significance to what those Sinclair computers did in the 1980s.

I should say at this point that in the real world exponential curves don’t continue for ever. We get S-curves which closely mimic exponential curves in the beginning, but then tail off after a while often as new technologies hit physical limits which prevent further progress. What seems to happen in practice is that some new technology emerges on its own S-curve which allows overall progress to stay on an something approximating an exponential curve.

The chart above shows interlocking S-curves for change in society over the last 6,000 years. That’s as macro as it gets, but if you break down each of those S-curves they will in turn be comprised of their own interlocking S-curves. The industrial age, for example, was kicked off by the spinning jenny and other simple machines to automate elements of the textile industry, but was then kicked on by canals, steam power, trains, the internal combustion engine, and electricity. Each of these had it’s own S-curve, starting slowly, accelerating fast and then slowing down again. And to the people at the time the change would have seemed as rapid as change seems to us now. It’s only from our perspective looking back that change seems to have been slower in the past. Once again, that’s only because we make the mistake of thinking in absolute rather than relative terms.

I’m writing this now because I only just created the charts at the top of the page. The mathematical side of my brain has known for some time now that when you are on an exponential curve the trajectory going forward is always the same, but there was some other part of my mind that didn’t quite believe it. If you’ve reached this far in the post you have seen my mind in action getting to the bottom of this piece of inner conflict. I think I see the world a little more clearly now. I hope you do too!

 

 



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I am in the Elon Musk fan club. It’s hard not to be in awe of what he’s achieved – four multi-billion dollar companies and he’s only in his forties. I’ve even read his biography, not something I’ve done for many people.

Lots has been written about why he is successful, mostly focused on his drive, vision, tenacity, resilience and intelligence, but I happened on a post morning which highlighted something that was new for me. Forbes columnist Michael Sims was seeking to understand how he has been successful across a wide range of very different industries – auto, space travel, energy and software.

The answer, he believes, is that Elon Musk is an expert-generalist:

Expert-generalists study widely in many different fields, understand deeper principles that connect those fields, and then apply the principles to their core specialty.

That struck a chord with me because that is what good venture capitalists do. In his book The Second Bounce of the Ball, Ronald Cohen, who has a good claim to being the first true VC here in the UK, wrote:

[investors] have to be financially trained and to have an understanding of management, but you also have to have a strategic brain while being sensitive to tactical and people issues

To that I would add empathy, patience, grounding, creativity and hustle. So we have to be generalists in that sense. Then on top of that we need to master multiple areas of investment – at least if you are to have a long career. In my seventeen years in this industry, I have invested in enterprise software, semiconductors, SaaS, social media, adtech, and ecommerce across multiple sectors. That has required a lot of reading! Then right now I am getting to grips with Bayesian Networks, Hidden Markov Models, Convolutional Neural Networks and back propagation as Forward Partners investigates whether to have a big push in what we are currently calling “Applied AI”. Further, all of this applies across multiple industries, from fintech to fashion to healthcare (one of my colleagues is up to his neck in microbiome research as we speak).

You can see the need to be an expert-generalist.

All this begs the question of how one becomes an expert-generalist, or if you are already an expert-generalist, how you become a better one.

The answer is to get good at learning. Fortunately Sims spells it out for us. Here is what he describes as Musk’s two stage process for learning:

  1. Grasp the fundamental principles
  2. Reconstruct those fundamental principles in new fields

There are no short cuts here. Musk used to read 60 books per month. But when, and only when, you understand the fundamentals you can more quickly learn and apply things in new areas. Returning to AI – Bayesian Networks are much easier to understand if you grasp the fundamentals of statistics, and once you grasp the fundamentals of Bayesian Networks (and all the other components of AI) it is much easier to understand where they can be successfully employed and where they can’t. Similarly with regard to human behaviour, a solid grasp of behavioural psychology makes it easier to predict how people will react to new products and services.

And getting good at learning isn’t just important for VCs. It’s important for everybody. The world is changing so fast now that one area of knowledge is most unlikely to be enough to build a career. A quick look at this Wikipedia article on the history of programming languages shows what developers have to deal with, but something similar is true for just about everyone else.

 



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As a keen observer of startups over the last 17 years, one of the most remarkable and welcome developments has been the application of scientific method to building startups. In 1999 when I started in venture capital there were no blogs and very few business books that were useful for entrepreneurs. All founders could do was accumulate wise advisors and rely on their wits and instinct.

If I was to pick a watershed moment in the emergence of ‘entrepreneurship as a science’ it would be the publication of Steve Blank’s Four Steps to the Epiphany in 2005. It’s not the easiest read, but for the first time founders had a playbook they could follow. However, it was also around that time that Brad Feld, Fred Wilson and a number of other wise souls started blogging and startup best practices started to be widely shared.

There were two great things about that. Firstly sharing leads to discussion and discussion leads to iteration, making everybody involved smarter. Thus it was that Eric Ries both extended Blank’s work and made it more accessible with the publication of The Lean Startup in 2011. Secondly, people outside of Silicon Valley were able to join in the conversation and get smarter to a much greater extent than they ever had been before which was a massive boon to other startup ecosystems around the world, including London.

Here at Forward Partners we have worked hard to contribute to this development by publishing The Path Forward – a playbook and set of practical guides for founders in their first year or two.

All this work has, I think, made it easier for founders to climb the learning curve and become masters at running their companies. It’s easier to know about and avoid common pitfalls (e.g. assuming you know what customers think) and to pick up tactics and best practices (e.g. OKRs for managing objectives). Of course, that doesn’t mean it’s now easy to be founder, far from it, but it is easier than it was.

However, building a startup can never be reduced to pure science. Some magic, art and wit is always required. I was talking to the chairman of one of our companies a year or so back (I won’t name him for reasons that are about to become obvious) as he was helping them through a rebuild of their product. The founder is a disciplined practitioner of lean startup principles who had achieved good growth through lots of experimentation and optimisation, but they had got stuck. They had hit a local maxima. The chairman explained how they had over indexed on startup science and ended up with a product that was boring. They needed more soul.

This story has a happy ending; they rebuilt the product and are now growing fast once again, but it is a reminder that there needs to be a balance between the disciplined application of startup best practice and inspiration.

I’m writing this today because whilst reading Are Liberals on the Wrong Side of History in The New Yorker I was struck by the similarity between the recent evolution in startup thinking and the way The Enlightenment impacted western thought in the eighteenth century. I don’t have the deepest grasp of the history of philosophy, but it was during The Enlightenment that thinkers like DescartesDavid Hume, Adam Smith, and Immanuel Kant had the great rationalist vs empiricist debate which developed the concept of the scientific method, introduced the idea that everything might be explainable through thought and rules, and then hotly debated the limitations of that approach to understanding the world.

As The New Yorker points out, you can, in fact,  trace this debate back to the ancient Greeks with Plato on one side and Aristotle on the other, so the rationalist vs empiricist debate has actually been running for millennia.

When I was an under graduate studying social science in the 1990s I had a good run synthesising the work of the leading thinkers of the time across sociology, political science, social psychology and social anthropology. It worked for me then and I find myself repeating the pattern here.  When there is a significant change in society then the pendulum almost always swings too far, whilst what we really need is to find the right balance. During the great debates of The Enlightenment in a sense both sides were right. It is beyond doubt that rationalist thought and the scientific method brought great advances to our understanding of the world and many great things flowed from that, including the liberal-capitalist system which has given us unprecedented individual freedom and prosperity. However, there are still many things that we don’t understand from first principles where all we can do is treat them like a black box developing predictions for what will happen next based on what we’ve seen in the past without understanding the underlying workings – the human brain is one example, and the workings of the economy being another (hence our difficulty understanding the impact of Brexit).

Returning to startups (and this is a bit of a stretch, but bear with me) – Steve Blank and Eric Ries can be likened to Descartes and other early enlightenment thinkers from the rationalist camp who achieved great advances by using scientific method to shine light into areas that had previously relied upon intuition and rules of thumb. The next step is to balance that thinking with the an approach that can be likened to the work of David Hume who pushed back on the rationalists noting that great insights can also be had by drawing on our experiences.

Throughout his career Steve Jobs famously eschewed market research and relied on his intuition to build amazing products. That’s an extreme position which worked for him, but doesn’t work for most of. The balance I’m talking about cultivates that sense of intuition but then finds ways to quickly and cheaply test the resulting ideas with customers. Now that we are in an era where our basic needs are sated MVPs need to be increasingly sophisticated before customers will engage. That means more investment in development before ideas can be tested than was the case ten years ago, increasing the cost of failure (hopefully not too much) and thus making it more important that only good ideas are tested (again, hopefully not too much). Hence the point of balance is shifting. At the margin the value of good intuition is increasing and the value of disciplined application of lean startup principles is decreasing.

The pendulum is starting to swing back the other way.



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Unless you’ve been hiding under a rock, you will have noticed there’s a lot of heat around AI as an investment theme right now. Octopus’s recent announcement of a £120m dedicated AI fund is one of many recent events I could cite as evidence.

In that same announcement Octopus mention that they have had three AI exits (Swiftkey, Magic Pony and Evi) so this is not a new investment trend.

It is, however, a trend that is changing. Up until this point AI exits have largely been driven by a desire to acquire talent. Even Deep Mind’s $400m sale to Google in 2014 is, I think, best understood as an acqui-hire.

Going forward two things will be different. Firstly, universities have responded to the demand for AI PhDs. Hence talent will be less scarce going forward and acqui-hires will be less necessary.

Second, and perhaps more interesting, is that it’s becoming much easier and much cheaper to build AI driven products and we are seeing an explosion in the number of AI startups with a clear path to delivering value to their customers and making profits. There were, of course, numerous companies in the previous generation of AI startups that were on this path, just nothing like as many as we are seeing now and expect to see in the years ahead.

AI startups are becoming cheaper and easier to build, because many of the underlying technologies are now mature enough to apply predictably, and because of the declining cost of cloud computing – including many AI as a service products on AWS and Google Cloud.

I liken this development to the time when cloud computing first emerged around ten years ago. Resources that were previously the preserve of cash rich companies became available to anyone who could pull together a few grand and a thousand flowers bloomed. I think we will see something similar again now.



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A couple of times recently I’ve found myself coaching people to stay positive. In both cases they very reasonably pushed back, saying great idea, but they didn’t want to be false and pretend to feel positive when inside they felt anything but. Two conversations about the art of being authentically positive ensued and I’ve been collecting my thoughts on the subject since then.

Let me start by taking a step back. This may be obvious to many of you, but we all like being around positive people. It’s more fun and it helps us keep our own energy up.

Positivity is doubly important in startups where the ups and downs will inevitably lead to periods where we question whether the whole endeavour is worth our time. Happiness is contagious and companies full of positive people climb out of the dark patches more quickly.

However, to really work, the positivity must be authentic. Saying or implying you feel good when you’re really not sure is better than giving into cynicism, but people can tell, and after a while it will chew you up inside.

One trick for staying authentically positive is to avoid dwelling on the big problems and focus on the little wins. When someone asks how you are doing, reflect on something that has gone well recently. If you made minor progress with a major client in the last 24 hours, say so. It’s genuine, and will make you and the person you are talking to feel better than a negative or neutral statement.

Underlying this is a really important point, which is that effective operators respond to feeling down by finding something positive to do. When we were still working out the details of our model here at Forward Partners we had a chap who started to get cynical about key aspects of his role. To his credit he responded by taking ownership of one of our content initiatives. It was a side project for him, but he had success there which kept him positive whilst we sorted out his bigger issues.

Other helpful tricks are getting enough sleep, exercising, eating well, meditating, and – simplest of all – remembering to smile. If you feel good in your body you will have more energy and find it easier to stay positive.

Like happiness, positivity is a function of mindset and behaviour. It can and should be cultivated.



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It’s fashionable in certain quarters now to slate some of the billion dollar startups that have been created recently and the investors that helped them get there. Zebras Fix What Unicorns Break is a good example. The piece makes three criticisms of the status quo:

  • Pursuit of extreme growth results in companies with unpleasant characteristics and a negative impact on society – e.g. Facebook (fake news) and Uber (where do I start…)
  • Companies with pure for-profit motives aren’t well equipped to solve many of society’s most pressing problems – e.g. homelessness in San Francisco, education, healthcare
  • Companies that aren’t chasing unicorn status find it hard to raise money

There’s some merit in these arguments, but they need to be put into context.

  • There is clearly dysfunction in chasing growth at all costs – inherently unprofitable companies grow to employ thousands of people before going bust, resulting in much personal anguish and not a little wasted capital. However, that’s a cyclical dysfunction which hit notable peaks in 2000 and 2015 and which needs to be understood as an unfortunate part of a larger system which overall has been an incredibly positive force for good. Five of the six largest companies in the world today were venture backed startups and just about all net new job creation comes from young companies.
  • It’s also true that many of society’s deepest problems aren’t likely to be solved by for-profit companies. That’s because there’s no money in solving them (otherwise the market would have been solved already). What we need here is government intervention.
  • The startup community has taken the ‘go big or go home’ mantra so much to heart that good mid-level outcomes – including exits in the hundreds of millions – aren’t seen as sufficiently ambitious. There are structural reasons why we’ve ended up here. As Fred Destin explained in his recent post Why VC’s are obsessed with large outcomes, investors with large funds have to chase unicorns to make their numbers work. Those large funds are often the ones everyone wants on their cap table and so almost everyone in the food chain, from smaller funds to angel investors and entrepreneurs alike, orientates themselves around giving those larger investors what they want, with the result that companies without unicorn potential find it disproportionately harder to raise money. That’s not a good thing.

So what should we do?

  1. Recognise that the system is imperfect, but not broken. We need massively successful companies like Facebook, and even Uber to generate growth, employment and the profits needed in the venture industry to finance the next generation of companies. Some unicorns are bad, but lots are good. Some investors back unsustainable growth in pursuit of short term profit (often unknowingly) but most are sensible.
  2. Celebrate mid-level outcomes as much as massive outcomes. Or at least almost as much. For me companies that exit for $200m are as noteworthy as many of the companies that raise money with a $1bn valuation, and often the lessons they’ve learned are more widely applicable than lessons from companies in the unicorn club. Talking about their stories more would help shift some of the dialogue and mindset in the startup community away from the needs of larger funds, towards the middle of the bell curve where most founders exist.

 



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It’s common for VCs to look at the market size for a potential investment from a top down and bottom up perspective. The top down perspective takes market research, often from an analyst firm or investment bank and the bottom up approach works by multiplying the number of customers by their likely spend – more detail in my old blog post here.

What I hadn’t thought of until recently is that it’s also helpful to take a top down and bottom up approach to assessing likely demand for a product.

The top down approach looks at how a startup fits with prevailing big picture trends. At the time of writing AI is the trend of the moment and it’s a good starting point to think that companies which intelligently apply AI techniques can create useful products. Moreover, it’s also true that raising money is easier for companies that are on trend (investors love a herd… or at least most of them do!).

However, the top down approach isn’t sufficient on it’s own. Even though it sometimes seems like companies doing AI for XYZ seem to be raising money almost as easily as companies doing Uber for ABC were a couple of years back, this strategy is unlikely to yield much success for either founders or investors.

To make good investments it’s important to combine the top down approach with a bottom up approach which looks at use cases. If it’s difficult to convincingly explain how someone will use a company’s product, it’s a fair bet that they will find it difficult to get customers. I’m consistently surprised how often entrepreneurs allow themselves to be satisfied with only a vague understanding of why they will make people excited.

When looking from the bottom up, a good first question to ask is ‘what behaviour potential customers are already exhibiting which shows that they will have demand?’ For young software companies a classic answer to this questions is that potential customers are building homegrown versions of the product they intend to build. If our young software company can build a software product that’s better and cheaper than the homegrown version then it’s a fair bet these companies will stop writing their own code and become paying customers.

A second technique is to employ Clayton Christensen’s ‘jobs to be done’ framework which starts from the insight that customers buy things because they have jobs they want to get done. Jobs can vary from the mundane (e.g. cutting the grass) to the exotic (e.g. become my better self) and companies that can articulate a good fit with a job that lots of us have to do or want to do are in with a good shout of selling lots of product. There’s more detail on the jobs to be done framework here.

For infrastructure companies the use cases are often not end user use cases. Rather the use cases are to help other companies build use case for the ultimate end user. For example a company that makes electric motors might sell to a lawnmower manufacturer who’s job to be done is to sell more lawnmowers. The electric motor opportunity can then be evaluated on the basis of whether it will allow the lawnmower manufacturer to help its customers (the end user) with their job of cutting the grass.

As with market size analysis the bottom up approach is harder to do well, but yields much richer insight.

 



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We have been thinking about how to evolve our investment strategy recently. I will write about the full process when we’re done and I’ve got a better sense of which bits have worked and which haven’t, but for now I want to highlight a post by another VC which highlights a lot of the methods we like to use when thinking about the attractiveness of potential focus areas.

The post was written by Bradford Cross, partner at Data Collective. Superficially it’s a listicle with Five AI Startup Predictions for 2017, but you don’t have to read very long before finding some good structured analysis and original thinking.

It turns out that four or Bradford’s five predictions are about things that won’t work and one about something that will work. Each of his points has generalisable lessons that can be applied to analysis of any potential investment sector.

  1. Bots go bust – main reasons: bot interactions are utilitarian and don’t meet our emotional needs, and for most use cases they are less efficient than other UI paradigms (e.g. apps and menus – note that Facebook has just added menu features to Messenger).
  2. Deep learning goes commodity – main reason: the number of grad students with deep learning skills has mushroomed and the premium paid for deep learning acqui-hires will fall because companies now other options for bringing in talent.
  3. AI is cleantech 2.0 for VCs – main reason: cleantech failed as an investment category because it’s a cross-cutting societal concern with a self important save-the-world mentality and not a market. AI has similarities, albeit the self-important element is about forming ethics committees and saving the world from the fruits of it’s own labour – super intelligences that destroy humanity and robots that take all our jobs.
  4. Machine-learning as-a-service dies a death – main reason: machine learning APIs are two dumb for AI experts and too difficult for AI novices. They don’t have a market.
  5. Full stack vertical AI startups actually work – main reason: low level task based AI gets commoditised quickly whereas vertical AI plays solve full-stack industry problems with subject matter expertise and unique data which make them defensible.

The generalisable lessons here are:

  • Use cases are paramount to good investing  (ref points 1, 3 and 4). Bots are failing because they don’t solve any new use cases and are worse at their job than other options. Horizontally focused investment themes are tough because they don’t start with use cases. Machine learning APIs aren’t solving a problem for anyone. Good candidates for investment focus areas have easy to understand use cases – e.g. I buy from ecommerce companies because it’s more convenient and the range is better.
  • Valuable businesses have strong barriers to entry (ref points 2, 3, 4 and 5). Deep learning, and AI more generally, got hot in part because talent was scarce. This reached the point where $m per PhD was talked about as an acquisition metric. However, talent is not a barrier to entry over the long term and neither is clever implementation of new algorithms. Proprietary data and uniquely trained models on the other hand, can provide a basis for high margins over the long term.
  • Hype is dangerous (points 1, 2, and 3). Hyped sectors draw in lots of VC dollars which drive valuations up, creating an illusion of success which brings in more VC dollars (sometimes spurred on by M&A). It is possible to make quick money from investing in startups in hyped markets but it’s a lottery. Moreover, all the mania often causes founders and investors to lose their focus on use cases. Unsexy is harder work, but it wins in the end.
  • Good focus areas allow for shared learning (point 3). One of the reasons that cleantech was a difficult place to make money is that there was little in common between different cleantech companies. Solar, wind, and biofuels, for example, all have very different technologies, different customers and different company building best practices. Mobile games, in contrast, has been a successful investment focus for many investors because key disciplines around game mechanics, monetisation and marketing are common across companies.

Many VCs are opportunity driven. Their primary strategy is to work on building their networks and then they invest in the best of what they see. Our belief is that focusing yields better results because deep understanding of a sector leads to better decision making and a greater ability to help entrepreneurs succeed. However, focusing is hard. It takes deep thought and hard work to find interesting areas and then it takes strong discipline to stick to your strategy. Focusing is also risky. If you choose a bad area to focus on at a minimum you will look stupid and if you don’t course correct in time you will have a bad fund. Still, if venture has taught me anything it’s that fortune favours the brave ”</p

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I’ve come across Kahneman’s Peak-End concept before but only just grasped it’s significance. As with much of his work Kahneman is highlighting an area where our minds don’t work rationally. In this case it’s how we remember experiences.

If we were rational we would remember experiences as some kind of average of how they felt at the time, adjusted for their duration. However, it turns out we remember them as a function of two moments – the peak moment (best or worst) and the last moment. Duration and average are less important.

Kahneman makes his point by citing research into how patients undergoing conscious surgery rated their experiences. Their post-surgery rating of the overall experience correlated with the peak moment of pain and how the surgery ended rather than the average of their minute by minute scores for how much pain they were suffering. Indeed, changes in the final moments of their operation dramatically skewed their overall perception of how well it had gone, in both directions.

This difference between how patients experienced their operations and how they remembered them exists with all experiences, both pleasant and unpleasant.

That has big implications for how startups should build products. Customers come back or tell their friends because of how they remember the experience of a product or a service, not because of how they experienced it at the time. Accordingly products should be engineered to deliver moments of delight and happy endings rather than maximum overall utility.

At Forward Partners we always look for those moments of delight, which we call “eyes-light-up moments”. Going forward I will be pushing us to pay equal attention to the closing moments of a customer experience.



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I’ve just read Atul Gawande’s Being Mortal, a book lots of people seem to be talking about all of a sudden. It’s a great book, and one I highly recommend. Mostly it is about making better choices for ourselves and our loved ones as we grow old, which is a far cry from startups.

One passage is different though. It’s about courage, which is required in spades if we are to make the most of our old age and if we want to make the most of our startups.

Gawande starts by turning to Plato who wrote “Courage is strength in the knowledge of what is to be feared or hoped” and “Wisdom is prudent strength”.

Which brings me to startups. It takes great courage to found and build a company, to maintain conviction in the face of naysayers, to get back up again when you get knocked down and to persevere when things look helpless. But it is also important to be wise. To know when to carry on or when to change course, or even give up. It was only after Ev and Biz Stone gave up on Odio that we got Twitter.

Gawande goes to on say that at least two kinds of courage are required in ageing and sickness. The courage to seek out the truth of what might happen both on the upside and the downside, and then the courage to act upon it. Again that’s super important at startups – the best founders tirelessly look for ways to expand the upside and ways their companies can go wrong. Other founders put their heads in the sand.

In a further parallel with startups, Gawande notes that ageing and sickness are highly complicated and uncertain, and that it is very hard to build an accurate picture of what’s going on or of the implications of any particular course of action.

It is in this uncertain environment that we must find the courage to act. Very often that choice means deciding which is more important, our hopes or our fears. Do we want the chance of a much better life, or do we simply want to stay alive?

That last question is as relevant in startup boardrooms as it is in the Emergency Room, but it is rare to see the difficult topic of potential permanent decline and failure addressed well enough that the the following course of action gives the best chance of happiness to all concerned. More common is to raise more money if it’s available, keep going with more or less the same plan and only make radical changes when the writing is well and truly on the wall. By this point there is much less cash left, the options for remedial action have become limited and the chances of achieving even a half decent outcome are much reduced.

The starting point for most founders is that they can overcome all the obstacles in their way and overcome what, to many, look like impossible odds. Possessing the self-confidence and resilience to maintain this perspective is an amazing gift that has enabled many entrepreneurs to achieve amazing things and it is something we look for in the founders we back. My closing point in this post though, is that in some situations the right thing to do is to stop trying to achieve the impossible, reduce our level of ambition and start building the best possible future within the constraints that face us.

Contemplating anything other than success is tough for most people involved with startups. Nobody likes a naysayer and fear of being thought of as unambitious or lacking tenacity made me think twice about writing this post. I decided to press ahead because the truth is that only a small fraction of startups achieve unfettered success (we will be happy if one in three of our portfolio are that lucky, and even those will face difficult periods). For the rest finding the courage to face the truth and make difficult decisions early will make them happier in the long run.



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This chart, from First Round Capital’s recent post Leslie’s Compass: A Framework for Go-To-Market Strategy is super interesting. It’s first use is for founders to work out whether they should have a sales intensive or marketing intensive go-to-market strategy. That’s the point of the post and the summary is that if your business has the characteristics on the left hand side then your strategy should be marketing intensive and if you’re more like the right hand side you should be sales intensive. If you’re thinking this problem through at all I would highly recommend reading the whole post.

The second use, which they don’t cover, is assessing whether a business idea is likely to be successful. It’s an obvious thing to say, but unless a business can find a successful go-to-market strategy, sales will be limited and it won’t succeed. The power of this framework is that it can expose fundamental challenges to the viability of a plan even when it is only a concept, and then it can suggest ways to address those challenges.

Simple plans are easiest to execute and in this case the simple plans are ones that are either marketing intensive, or sales intensive. Plans that sit somewhere in the middle are ok, but products that have some marketing intensive characteristics and some sales intensive characteristics have an inherent contradiction that if left un-addressed will undermine success.

The most common and obvious contradiction that we see is complicated and high touch products that are inexpensive (or have low margins). Even if the product is a bullseye hit with what the customer needs, it won’t be possible to persuade them of that fact without an expensive sales effort, which won’t be covered by the value of the sale.

Other contradictions to watch out for include B2C : complex products and many customers : low fit, but the most important one is definitely cheap products that require a sales lead approach.

Business plans with contradictions like this aren’t necessarily fatally flawed, they are just more difficult to execute, and that brings us to the third and final use of this framework, which is to inform product strategy. If there is a contradiction then one solution is to resolve it through product innovation – if the contradiction is between low price and complexity/high touch then either find a way to either to take the complexity out or to charge more.

Usually those product innovations will be to enable a more marketing led approach, and to generalise, companies that move product categories from being more sales intensive to being more marketing intensive make promising bets. The shifts don’t have to be big either – convenience is a winning proposition. Examples are legion, but Slack is a great one. Last May they became the fastest company to reach a $2bn valuation in large part because they succeeded in making a product that works with a go-to-market strategy that is close to 100% marketing led. Looked at through this lens, their genius was in taking all the complexity out and enabling low touch adoption.



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For about three weeks I’ve been meaning to write about the amazing success that is Amazon. Back then it was when I read they were planning to create 100k US jobs in the next 18 months, are worth more than the next eight biggest US retailers combined (see chart above), and that they now employ 45k robots, up 50% year-on-year. I finally put pen to paper today (so to speak) because I saw the additional news that they are building a $1.5bn hub for their own cargo airline (sic), have 0.7% of the UK grocery market a mere six months after launching here, and had a record holiday season in 2016 shipping over 1 billion items.

That’s quite a list, and unsurprisingly their share price has been tracking an exponential curve over the last few years.

When I think about what’s got them there, the list of characteristics are exactly the sort of things we love to see here in startups here at Forward Partners:

  • Execution oriented
  • Determined
  • Disciplined
  • Relentless
  • Logical
  • Keep it simple
  • Independent thinkers
  • Experimental
  • Not afraid to make mistakes
  • Values driven

That list makes them sound a bit like The Borg and whilst I love Amazon I will admit there’s some validity in that comparison, also noting that The Borg were hugely successful. That said, new startups trying to emulate Amazon would be well advised to make sure they also have a good dose of creativity and brand story.

Finally, there’s a great entrepreneur at the heart of every great business and Jeff Bezos is perhaps the greatest out there at the moment. I still love this video I posted back in 2010 where he shares some stories about Amazon’s first days and then tells us “everything he knows” in about five minutes. He has an incredible ability to distill complex concepts into simple insights.

There’s no denying that Amazon have had and still have their critics and naysayers, but from an entrepreneurial perspective, we could all aspire to be a little more Amazon.



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