Wednesday, 4 April 2018

Valuing platform businesses

A client of mine asked my views on how to value a platform business he is building. Since it's a fun blast from the past (remember when everyone wanted to be a platform?!), I thought I'd share the method we use.

In simple terms, there are two questions you need to answer:
  1. What’s the business model of the platforms that you’re considering rolling up?
  2. Where is value retained in the platform?

Question 1 is a weighting factor, which in combination with Question 2 gives you the standalone value for the platform. There are three predominant platform business models:
  1. Trade, in which the value is in identifying and rendering liquid otherwise underexploited assets
  2. Co-creation, in which the value is in identifying and rendering trustworthy and productivity collaborators who could have otherwise not have found each other
  3. Outcome, in which the platform enables the assembly of data or assets from multiple sources and thus enables the platform owner to monetize the combination by delivering an outcome that would have been otherwise impossible

Each model uses a number of different value mechanisms. A successful platform has all five of them, but will usually excel at only one or two. Success is dependent on the alignment of the mixture of mechanisms with the business model. Value mechanisms build value through:
  1. Participant equity, measured via the absolute and relative number of producers and consumers on the platform
  2. Network equity, measured via the number of connections on the platform, the quality of those connections (e.g. between producers and consumers > consumers and the platform) and the volume of ‘items’ available for purchase relative to the number of participants
  3. Trust equity, measured via the number of positively-rated connections on the platform
  4. Algorithm equity, measured via the success of the platform in creating new connections between participants
  5. Loyalty equity, measured via retention rates and proportion of consumer spend passing through the platform
So the value of the platform is the weighted sum of the equities, each of which carries a dollar value based on their relative competitive position in the market. Simple... if you like math :).

Monday, 18 December 2017

Blitzkrieg business

Like many people I found myself in a buying panic on Black Friday. I havered over some wireless headphones, or a VR headset for the PS4 I don't have time to play, or an Amazon Echo but somehow couldn't bring myself to make a decision. In the end my Amazon basket contained only one item: Heinz Guderian's Panzer Leader. So I bought that and was left with faintly unsatisfied with my Black Friday efforts.

I shouldn't have been. Although a little dry, this is a really superb book on disruptive leadership in a time of colossal social and technological change. Although perhaps not so well known (seems that great strategists tend to be less popular than charismatic characters, see Montgomery vs Slim, Patton vs Abrams), Guderian was a revolutionary. He invented Blitzkrieg, amongst other things. A couple of ideas have particularly caught my attention in the early chapters, which I thought worth sharing.

1. Organising disruptive capabilities

One of Guderian's key realizations during the 1920s and 1930s was that rather than supporting the infantry, tanks should be massed together as a breakthrough weapon. His main argument is that eventually a countermeasure will be found for any new invention so whilst that invention is in the ascendancy it shouldn't be frittered away to prop up the competitiveness of the previous generation (in this case, infantry and cavalry). He was to be proved right. Even though France and Britain had many times more tanks than Germany and their machines were also individually superior, they deployed them piecemeal and were defeated in detail. Their new technology was forced to act with the constraints of the old.

It made me realize that businesses do this all the time with new technology and cultures. Revolutionary capabilities like AI are frittered away in innovation programs sitting around the old business. Digital divisions are saddled with old skool Marketing and IT as soon as it's possible to do so, thus destroying their speed of operation and innovativeness. No doubt there needs to be a transition into the mainstream at some point, but while they are still differentiating, new technologies and ideas should be kept separate to maximize their effectiveness. The rest of the organization should adapt to support.

2. The 'operational' sphere

We're all familiar with the concept of tactics - ploys effected in near real time to iteratively impact the local situation. Strategies are definitely more blurred. Getting away from the semantics of 'strategy is what you do', the reality is that the term is used to define sweeping, long term positional changes as well as relatively short term decision making. I've lost count of the number of times I've been invited to 'strategy' sessions for the next quarter of business at an account or even for a competitive pitch.

The German Army apparently didn't think this way. They had a third domain of action, which roughly translates as 'operational'. These are theatre-level ventures that ultimately impact the realization of the strategy but are decided on in near real time, like a tactic. So they are reactive, but also supportive of strategy.

I think this is interesting. We often think of 'operations' as the processes that combine forces and materials to create products and services. They are the opposite of reactive, being planned, well understood and regimented. In the business world, the equivalent of Guderian's operational domain would be reacting to acquire a smaller competitor as its economics begin to fail, or doubling down on a new product or market mid-cycle. My experience is that most corporates are fairly bad at this kind of thing as there's a disconnect between the intent of their strategy and the actions of their corporate development/ acquisitions group. Furthermore, most organizations lack a significant strategic reserve. Budgeting is a process where everyone asks for the maximum and then the business argues until every penny is allocated. The ability to react to opportunities at a corporate (as opposed to a BU) level is generally limited.

Maybe that needs to change. Everyone seems to accept that the pace of change is unique (reading Panzer Leader suggests that we know nothing about pace of change!), and yet we set our organizations up to react on an annual cycle. In hindsight that seems crazy.

So there we are. Two interesting things to come out of Black Friday... 

"Alexa: what's the German for 'operational'?"

Thursday, 30 November 2017

Prototyping and the Analysis Crusade

I feel like I've recently been on a crusade against business analysis. As some of you will know I really, really like to prove things so I am most certainly in favour of analysis, my problem is the amount of it that people feel that they need to do in order to make a decision. And worse still, having asked for lots of data and analysis it then goes on a shelf without being read. I've been trying to change that situation within our practice, project by project.

Part one of my crusade is to persistently remind people that analysis must ultimately help someone make a decision and then enact that decision in the world. That may seem banal, but my observation is that it's very easy for analysis to become the task, subsuming the intention of the strategy process. This leads to intellectually pure answers, but not decisions.

Part two is more recent. It's the culmination of something that's been nagging at me since we started to build ventures for clients, initially using Lean Startup, more recently our own proprietary method that extends and improves that idea. In venture building one is always creating new tests to increase certainty about the business in three domains: do customers want it, can we do it and can we make money out of it.

Working with my colleagues with a more design-related background reminds me constantly that each of these tests is a prototype of a component of the overall business (as opposed to the prototypical products or solutions that they typically focus on). This makes me think about a reframing of business analysis: if we considered every analysis as a prototype of a decision would we reduce wasted effort and increase the likelihood of realizing a better decision in practice?

My train of thought starts with the domains of strategy. Oddly, this isn't something that I've given much more than idle consideration over my career: I've just tried to solve problems (note that I didn't say 'help leaders make decisions' - I'm very guilty of everything I've said above). A simple construct for the development of strategic intention would be:
  1. Describing the environment in which a decision will be made (a.k.a. scaring everyone/ creating a burning platform)
  2. Making strategic choices that define how the organization will react to, and attempt to shape the environment to its advantage
  3. Translating corporate strategy into commercial or business-unit level strategies
In my experience (1) and (2) are typically bundled together. I'm not really happy with this anymore as I very rarely arrive in an organization in which the leadership have a similar view of the nature of the market in which they play, the likely future of that market and the internal issues that impact the business. Spending some proper time setting the scene probably makes sense, before moving on to corporate-level strategic choices.

I typically see two types of analysis used to underpin these three domains: anecdotal and research-based. As stated in the previous paragraph, anecdotal tends to be the dominant method in describing the environment, I suspect because its easiest to abstract the subject of the nature and future of the environment into "it'll change and it could be BAD". Since there will almost certainly be a business case produced in the formal strategy processes, research-based analysis dominates (2) and (3). I personally don't like business cases very much as they are justifications of the overall decision, rather than honest analysis used to make the decision. It's therefore both easy and desirable to make the business case look good, hence destroying any real chance of the strategy being realized. Anyhow, the research-based analysis involves lots of people sitting at desks looking at Google, making models and occasionally trying to find 'experts' to interview. It's all a bit ridiculous when you consider how much it costs.

Getting back to the point, could there be a third way? Could we use prototypes to make better decisions by more accurately expressing the nature of the market and judging the likely validity of ideas? I think yes... I mean, seriously, would I have written all of the above if I thought 'no'? :)

The main question is how far we're willing to go with prototypes. Great strategies make deliberate, stretching choices that create the conditions for success and that we ultimately hope will enable us to win. That means being ambitious - not 'playing to play'. To really improve our understanding of the behavior of a market, one has to be quite active and deliberately set out to make a change that one can then observe propagating.

Because of this a good strategic prototype probably needs to go beyond the most basic formats: a mock-up of a value proposition that is introduced to customers to gauge their response or an adcept launched on Facebook or Linkin to test sign up rates. These are far superior ways of derisking a strategy than analysing what other companies are doing and thus inferring that we can do the same, particularly if the prototype's branding is carefully thought through. Responses to a startup are very different to responses to a mature enterprise. Not better or worse, just different. Likewise, we can experiment on the capabilities of an enterprise or the reaction of the channel by changing incentives for sales staff, tweaking real operational processes to see what happens to productivity. All of this is prior to concluding on a strategic option, remember. It makes strategy a more activist process.

Although this type of thing can show us about reactions to a value proposition, it doesn't show us anything much about our employees' response to our new aspiration, or about competitor response or our capabilities to scale the proposition or our ability to govern the business. Given the ultimate impact of a corporate strategy and the value at stake, it seems to me that larger scale prototypes could be tried.

For example, rather than analyzing a market endlessly and performing scenario analysis to try and understand how it will respond to new products, why not launch a new business specifically to test how it responds in reality, or buy something that does the same? In scaling such a business, you'd also be prototyping the capabilities and management systems that would be needed to run a similar business at scale, hence derisking the build of large scale enterprise productivity systems (which have a tendency to be iterative rather than transformative, despite the regular presence of "Transformation Directors" in organizations). One could even use incubators and accelerators to do the same through X-Prize competitions of funds in very targeted domains.

In this way, strategy becomes a continuous process based on connecting hypotheses about the world to metrics that describe the behavior of that environment and prototypes that impact it. It probably looks something like this:
If this were feasible (and I believe that Digital Economy sensing and data processing technology makes it so) then it would revolutionize the practice of strategy, which is for the most part a periodic project-based exercise, realized with waterfall delivery that generally fails to have significant impact. Productivity increases. Everyone wins! A victory for theory...

...but, there's a problem, and it's a big one. Corporate governance means that anything that involves an acquisition or significant capital outlay needs to be reported to the market in some way or another. This makes it quite difficult to launch prototypes of sufficient scale without making market-facing announcements that may bias the validity of the test. It's a massive problem, and one that I'm going to try and tackle in the next couple of posts.

Tuesday, 28 February 2017

MWC 2017 - Sony, agents, projectors and Lean Startup

I’ve recently become fascinated with the potential of domestic voice assistants, such as Amazon’s Echo. Although they have their flaws of interpretation, they enable access to content from the internet for people who are unused to, or unable to use smartphone interfaces. It’s easy to forget that although a great many people pick up their iPhone as soon as they wake up and rarely put it down until they close their eyes at night, there’s a whole generation my parent’s age and above who don’t habitually carry their phone around or really understand how to use it. Devices like Echo/ Alexa get around this by making it easy to get facts about things from the web or play some music just by speaking clearly, bringing some of the benefits of ubiquitous connectivity without the learning curve.
Xperia Agent expresses its frustration at its long gestation
I was therefore excited to see how Sony were getting along with their Xperia Agent, AI assistant product, which they showed at Mobile World Congress last year. And the answer is… they’re in exactly the same place. The product looks nice. Its friendly eyes and cute head still look at you when you talk to it. It has a little touch of empathetic response that makes it more engaging to talk to than Echo. But it still isn’t a product. One of its minders was quite stern with me when I mentioned that it didn’t seem much different to last year. “We need to get it perfect”, he told me.

Another product that seems good on the surface but is probably about to disappoint is the Xperia Touch. This is a great concept: a projector with integrated computer that turns any surface into a touch screen. It works brilliantly too, creating sharp images and responding rapidly to interaction. Why do I think it’ll fail? The price, at EUR 1,500 is way too high for the modest consumer need it serves. I can see it being useful in some retail or business situations; however the Sony people on site were firmly of the view that it’s a consumer product.

What’s occurring here are two common issue in corporate innovation. First, Sony seem to be totally focused on technology invention and product innovation, rather than thinking across all three parts of the Lean Startup cycle. They are favouring ‘can we do it’ over ‘will customers want it’ and ‘can we make money out of it’. Considering all three parts together is vital in order to launch successful products rather than mere trinkets.

Second, they are too focused on perfection. In the quest to launch a 100% product, Xperia Agent has gone from something novel that may even have been able to compete in the AI assistant market, to an irrelevance. And this from the organisation that was willing to take a risk on two generations of Aibo the robot dog! Launch and get customer feedback, fixing issues along the way, rather than trying for perfection in a market that you don’t understand yet (because no one does!).

I’ll look forward to seeing Xperia Agent again next year. 

Tuesday, 7 February 2017

Unicorns and Dinosaurs

Last week I was lucky enough to present the keynote at Oracle Modern Business conference in London. I used it to test some thinking that's emerged from client work over the last few years. I've been gathering data around it and applying the ideas in real businesses... needless to say that there's some promise there.

The central thought is that dinosaurs were once unicorns. Rather than the common rhetoric that the latter are intrinsically better than the former, I wonder whether unicorns should actually aspire to be dinosaurs and seek to learn from them in the same way that big organisations seem desperate to sip some unicorn-flavour Koolaid in the Valley. Anyhow, here's the transcript of my speech. Hope you find it interesting.

Disruption was an exciting new concept when I started my career. It was much like Lean Startup is today: if you felt that you understood it, you were in an exclusive club possessing of knowledge that could topple empires.
Disruption is daring, a tale of David and Goliath in which an innovation creates a new market and value network, destroying the existing market and value network, displacing established market leading firms, products and alliances, typically with terminal results for those incumbents.
Those incumbents are the dinosaurs, great lumbering beasts just waiting for the asteroid.
Today we’d see a business that disrupts really well as a unicorn: a mythical value creature that investors scramble over to get a piece of. Unicorns are faster and leaner than dinosaurs. Their culture of flatness and risk taking makes them deadly competitors, even if they only have one horn.
I’ve long believed that there is truth in this, that the cultural norms of the Digital Economy are more effective than the management norms of the Industrial Economy, which are based on hierarchy and control. So I set out to prove this in a two year study with London Business School.

Our research used the banking sector – incumbents, challengers and potential disruptors – as a base. It wasn’t conclusive, but it does suggest mathematically that digital native businesses are substantially more productive than incumbents.
The question is whether being more productive is enough. Many people seem convinced. The common rhetoric I encounter in our own organisation is that the rate of change is accelerating, unicorns will triumph and we are entering a post-dinosaur world.
As with everything these days, there are facts and there are alternative facts. I have come to believe that the common rhetoric is a lazy rhetoric. My research suggests that unicorns and dinosaurs will largely peacefully co-exist and that both have much to learn from each other.
Let’s talk about the life of dinosaurs. As we learnt in school, these are majestic beasts with huge teeth and claws. None of my friends were interested in the little guys, so we’ll continue with that bias today.
Companies have been around since the 16th Century, but their ascendancy in economic terms began with the widespread growth of public companies in the late 19th Century. It’s not actually all that easy to get data on these early organisations. Much of it is buried in ledgers in public libraries or in books with gripping titles like ‘Scale and Scope, the dynamics of industrial capitalism’.
But if you’re persistent then you can find it and with it a treasure trove of information about the way in which both unicorns and dinosaurs evolve.

The figure above is a distribution that shows the age of large organisations at the point that they went extinct. By extinct I mean bankrupt, dissolved, eaten by a competitor or deceased in some other way. And these are important institutions – all of were part of the Dow Jones Industrial Average or were members of the S&P 500 or FTSE 100 from the 1880’s to the present day. Importantly, this is a data set that covers the last economic system change: electrification.
Looking at the peaks tells you something interesting about the points of danger in an organisation’s life. And the first danger is in their unicorn-dom.
The data suggests that large organisations are about 35% more likely to fail in their first 10 years than the average. To put that into perspective, at the time of writing there are 185 Unicorns – privately funded companies with a valuation of more than a billion dollars. 75% of them are less than ten years old.

So what's causing this spike in the death rate? It’s important to recognise when considering the death of unicorns that dinosaurs were also once unicorns. Although a very small number of organisations gradually build over time into mega-corporations, the vast majority of big businesses started out causing or riding a wave. They grew explosively fast by creating new industries like automobiles, aeroplanes, electrical factory equipment, typewriters, software and so on.
That rush is a really dangerous period. Yes, it’s exciting, but in a rush to grab newly emerging demand those that fail over-invest in areas that do not build lasting competitive advantage and as their market inevitably begins to slow they are left with a cost base too great to be sustainable. In the old days we’d have said that they were too ambitious. Post lean-startup we’d probably say that they’d failed to validate whether they could make money as they scaled.
Let’s go back to the chart.

There’s two big spikes in the middle, between 80-90 and 100-110. Companies at this age are in real peril – 140% more likely to go extinct than the average. The ‘why’ is two-fold and strongly related to the time period covered by the data.
That time period covers the Industrial Economy and about ten years of the early Digital Economy, in which the introduction of digital computing as a new general purpose technology led to another surge in innovation output.
What you’re looking at here is the gradual decline of Industrial Economy business models over a long cycle. When you look in detail you see that the demise of these dinosaurs was generally a legacy of doubling down on non-differentiating infrastructure by Managers who’d grown up on the scale economics of the preceding hundred years.
It's important to realise that this generally isn’t an asteroid. Disruption is a small part of the story, but by no means all of it. The economist Paul Ormerod has done fascinating work on the effect of disruptive shocks on businesses. This chart shows one of his many analyses, demonstrating that throughout history shocks have led to extinction, whether there is disruptive change or not.

Doubling down on an existing strategy at a time when the world is changing is much the same as the failure mode of unicorns.
It turns out that it’s not at all easy to innovate and grow fast. And it’s not easy to change tack and change modes into optimisation. Survival is a process of building layers of complementary physical and intangible infrastructure that maintains the efficiency of scale while providing a springboard for investigating possible future infrastructure layers.
There’s a way of thinking about it that I’ve found effective with the businesses I work with. It's not a silver bullet, but it is a useful framework for transforming the way they think about their market and their operations.

This model entails rethinking the organisation as an interconnected set of engines, each managed by different metrics and with distinctly different functions. These are not unitary structures – organisations can have many of each and crucially they partner for many more.
Let’s start from the top.
Commissioning engines are the quarterback for the system. They maintain a world view that enables everyone in an organisation to go broadly in the same direction and trigger migration from one type of engine to another based on, amongst other things, the growth rate of their key metrics.
Invention engines come up with new ideas and technology in isolation of application. These are often pure research organisations and thus are more than likely to be outside the direct control of your business.
Innovation engines systematically take ideas from invention engines and apply them to business problems dictated by commissioning. They demonstrate problem solution fit and then pass to…
Commercialisation engines, which operate a lean startup process of build, measure, learn cycles that establishes product-market fit for an innovation, be that a process change or a new venture.
Scaling engines begin the process of migrating a startup venture onto a more sustainable footing. They balance growth rate with putting in place infrastructure – remember that means working practice as well as physical and tangible - that will work at 10X size.
As growth begins to attenuate you need optimisation engines. 80% or more of the typical business’s resources are in optimisation engines, which are set up for slow or even negative top line growth and thus focused on systematising innovations that enable productivity to remain high.
Underpinning everything is a decommissioning engine. This doesn’t just sense when something is no longer performant and needs to be removed from the portfolio. It also triggers invention and innovation by detecting business problems that are emerging from operating. If you like, it looks inwards while the commissioning engine looks outwards.
This all sounds fairly hypothetical and even a recipe for chaos. It actually works really well operationally because it does two things. First it requires the use of data around every process in an organisation. Processes drive outcomes. Outcomes need to be meaningful in order to be pursued.
Secondly it disconnects outcomes from domains. The people, process and technology of an organisation should be directed at outcomes at a life stage, not consolidated by nominal domain.
Finally it focuses the mind on the engines that an organisation needs as it evolves rather than the engines that are making it successful right now.
In other words the future of business as I see it is a hybrid: the scale and solidity of a dinosaur and the magical growth powers of a unicorn.

I suppose we could call that a rhino.