Strategy in the Post-fixed Costs Economy

Strategy in the Post fixed costs economy

Strategy in the Post-fixed Costs Economy

Evaluating strategic options in a world where businesses have never been easier to start, but never been harder to scale

by Ben Robinson, October 2020 | 15 minute read

We’ve talked often about the diminishing importance of supply-side economies of scale. In its simplest expression, digitization flips the industrial age equation. What was scarce in the industrial age was supply; what is scarce in the digital age is demand (attention).

In the industrial age, to scale supply meant mass production to spread the fixed cost of large capital investments over large volumes. And the industrial age was an age of mass produced, relatively standardized goods. This applied to goods and services provided by the private sector, but also to state-provided services, such as education and public services.

Since the advent of the internet, this is changing. We first noticed the shift in industries where both supply and distribution could be digitized (e.g. media) because supply became abundant faster and this highlighted our limited attention sooner.  But it’s becoming increasingly apparent that all industries are being disrupted as software has eaten the world. More and more physical goods have software components to them, making supply more digitized. Where supply cannot be digitized, distribution nearly always can. And where supply-side economies of scale remain important, they can be borrowed.

Renting scale and the end of fixed costs

AWS was not originally intended to be a platform on which third-parties would run their businesses. But, thanks to Amazon’s business model, it was possible to open up that infrastructure service to others. In doing so, Amazon created a massive and highly profitable business which contributes 65% of group operating profits.

But, as big as the impact has been on Amazon, the broader societal impact has been truly dramatic.

Before AWS, companies had to make large upfront investments in computing hardware – a significant barrier to entry. Even buying hardware was a major source of risk: buying too much could bankrupt a company while buying too little could cause a major bottleneck to growth. And so, AWS removed both risk and cost for new businesses. As a direct consequence, it also contributed to the creation and success of hundreds of thousands of new businesses.

This boost to global GDP over and above the value captured by Amazon itself is difficult to calculate, but it’s certainly very significant. It’s probably not an overstatement to suggest, as Charlie Songhurst does, that AWS has been the single biggest factor in the rise of angel investing.

But AWS is not the only internet era platform. From Shopify to Stripe, examples abound of platforms that share their scale economies to remove the cost and complexity of doing ecommerce – allowing companies to form and start trading faster, with reduced risk and at lower volumes than would have ever been possible.

And not all internet-era platforms are providing digital services. As Rita McGrath, Columbia Business School Professor, discussed on a recent Structural Shifts podcast, by helping establish prices and create trust, digitization is making more and more non-digital assets tradeable on marketplaces. As she put it:

“What we’re seeing with the advent of the digital economy is that more and more transactions can be conducted in markets that used to require a firm.”P

Uber was a pioneer in this regard, but we now see this “uber of x” phenomenon everywhere – even in the enterprise market. At aperture, we are very much an embodiment of this, providing strategy and go-to-market as-a-service that enables companies to avoid the fixed costs and risk of underutilization and underperformance in these functions.

In effect, it’s becoming easier to rent all services, physical and digital. All become liquid and on-demand. Capex gives way to opex or, as Younes Rharbaoui says: we have entered the post-fixed costs economy.

“Signs of a post-fixed costs economy are all around us: companies switching to full remote, increased reliance on independent workers & freelancers, on-demand software where cost matches usage, are all creating lean financial structures for growth.”

And, of course, like many other secular trends, the impact of the pandemic has been to accelerate it. If COVID-19 drew a binary distinction between online and offline services, lifting the former and sinking the latter, then it was disproportionately brutal in its treatment of those offline businesses with high fixed costs – oil companies, airlines, hotel chains and so on. From now on, all fixed cost investments will be more heavily scrutinized and, where they exist, variable costs alternatives will be more actively considered. Even Warren Buffett, a regular character in aperture blogs, is starting to consider the wisdom of some high fixed-cost business models.

The fact is, if fixed costs were already becoming passé, they definitely will be in the post-pandemic world – ushering in a faster transition to a new, internet-era economic structure.

Platforms, aggregators and the long tail

For the best explanation of the difference between platforms and aggregators, we recommend this classic essay from Ben Thompson. In it, he uses the Bill Gates platform definition, namely that: “A platform is when the economic value of everybody that uses it exceeds the value of the company that creates it. Then it’s a platform.”

Bill Gates quote platform vs. aggregators

On this definition, the business we have already mentioned – AWS, Stripe, Shopify – are all platforms. They make the large investments in fixed costs – datacenters, fulfilment centers, payment networks, etc. – that mean their clients don’t have to. They make it cheaper and simpler to do business.

It’s this commoditization of supply and associated end of fixed costs that’s now starting to give rise to a long tail of providers. Thanks to lower and variable input costs, it’s possible to make money at lower volumes than in the past, which in turn means a higher number of providers can co-exist.

Take newspaper publishing, for example. The massive costs of producing and distributing physical newspapers gave rise to significant economies of scale and produced an oligarchical market structure. Compare that with today, when a platform like Substack allows independent writers easily and cheaply to publish, distribute, and monetize paid newsletters. These writers can make a living with only a small audience, allowing potentially tens of thousands of them to co-exist – and giving rise to a broader phenomenon, the “Passion Economy”, where more of us can pursue our craft or our talent and make a living from it.

However, the difference between the long tail as it’s conceived now and the original theory, is that supply is abundant, not demand. The constraint on all digital-era businesses is demand and the gatekeepers of demand – the most profitable actors in the digital ecosystem – are aggregators.

In a world of abundant supply, aggregators help match buyers and sellers. They are in a position to do so because they provide interactive content that rises above the noise to command our attention. When in possession of our attention, they can monetize it by charging advertisers to reach us. This has become the biggest cost for many online companies, accounting for 40% or 50% of the investments they make in growing their business.

As Clayton Christensen predicted in the Law of Conservation of Attractive Profits, as one part of the value chain commoditizes, the value is captured elsewhere. As platforms helped generate an economic surplus, aggregators increasingly captured that value – especially Google and Facebook.

While it has become cheaper to start a business, a sharp increase in customer acquisition costs more than offset these savings.

More precarious

But it’s not just high customer acquisition costs that prevent long tail companies from rising to a size where they exploit scale effects. There are other factors at play.

First, the falling costs of starting a business is a double-edge sword. If one online retailer can set up on Shopify, so can any other. Platform companies are lowering the barriers to entry for everyone, making it harder to defend a business than in the past.

Second, if a business model has network effects and a company can grow large enough to exploit them, this market leader becomes more powerful than an industrial-age leader. This is because, unlike supply-side economies of scale, demand-side economies of scale are subject to increasing returns to scale; the more they exist, the stronger they become. And so, where the industrial age gave way to oligopolies with clearly defined industry boundaries, the internet age gives way to winner-takes-most aggregators, large-scale platforms, and a long tail of suppliers operating across the economy in general.

Third, there’s also one important supply-side economy of scale which makes size important even in the absence of network effects – and reinforces them where they exist. This is the ad score. Basically, the bigger a company gets, the cheaper its relative cost of customer acquisition becomes because it pays a lower cost per lead thanks to a higher ad score (the algorithm that advertising platforms like Facebook and Google use to calculate the likelihood of a customer clicking on an ad).

Effectively, the post-fixed cost digital economy is one where is it simultaneously cheaper than ever to start a business, harder than ever to defend and scale it, and where the returns to scale have never been more important.

If it was hard to cross the chasm from startup to large, scaled business in the industrial era, in the digital age it is harder still.

So where to next?

Let’s look at strategic options for new entrants, from where to play to how to scale.

Where to play

Marc Gruber, a professor at EPFL and former podcast guest, wrote a book on “Where to Play”. At the risk of grossly oversimplifying the narrative, it argues that companies spend too little time thinking about which market opportunity to pursue – assuming a good product eventually finds a market – and instead provides a framework to select the right market before commencing activities.

Like Marc, we believe it makes sense to invest the time upfront to consider carefully where to play, even more so in the post-fixed cost economy. Marc’s book provides the methodologies for this choice, so we limit ourselves here to explaining the rationale.

In a digital world, where returns to scale are bigger, incumbents will be harder to displace. Therefore, it follows that any startup should focus either on creating a new market or, more likely, on market blind spots: the niches where consumers are underserved or overserved.

Underserved and overserved markets

In the digital age, underserved markets are likely bigger opportunities than in the past because geographical limitations are removed. A micro market in one country might be a big market when addressing all countries collectively.

B2B marketplaces are a classic example of this phenomenon.

While there are B2C marketplaces for seemingly everything, many entrepreneurs overlook B2B marketplaces because they seem less scalable. They think that it will be difficult to build a big business if your buyers and sellers are very specialized; that there won’t be a generalized pull effect. Buyers of fish are unlikely to be drawn to a specialist marketplace for, say, cement and building materials. But when you are dealing with a global B2B vertical, a two-sided network is more than sufficient to build a massive business: the wholesale fish market, for example, is worth USD150bn!

Another reason B2B marketplaces are sometimes overlooked is because it can be a long game. Many of the businesses we work with are patiently helping incumbents digitize their offering as a precursor to enabling one-to-many transactions. But their ultimate goal is to become a platform for enabling a many-to-many marketplace.

Trade Ledger Business Finance Lending Platform

There are also plenty of opportunities to target overserved customers. As Gary Pisano discussed on another episode of Structural Shifts, companies often push so far with innovation with an existing product or within an existing business model that they overshoot customer demands and leave themselves open to disruption from new entrants providing more user-friendly products or offering more convenience. He gives the example of subscription-based razor blade services like Dollar Shave Club disrupting the overengineered and expensive Gillette razors (“I can only shave so close before it’s scary!”). But this concept of overshooting is especially prevalent in B2B software where, in order to meet the enterprise buyer’s demands, traditional companies have overshot the demands of the end user creating the opportunity for disruptive innovation. This idea is brilliantly expressed in the following excerpt from an a16z article:

“Since effective top-down sales require a highly choreographed (and costly!) dance between pre-sales and the customer, product teams are incented to add more nobs to the product so these teams can sell more value and extract more dollars. Vendors get crossed off the list in vendor discovery if their product doesn’t check all the right boxes for the enterprise buyer, even if many of the boxes don’t actually deliver any value. This often creates a vicious cycle where more complex products give rise to longer sales cycles for more dollars, which then incentivizes even more complex products. For any user of legacy enterprise software, it doesn’t take long to realize that designing a seamless user experience is by no means a top priority for the vendor.”

Direct to consumer

Targeting underserved and overserved customers is what Clayton Christensen refers to as “disruptive innovation” and, as he tells us in the Innovator’s Dilemma, disruptive innovation is about simpler and cheaper products, but it’s also about marketing:

“disruptive technology should be framed as a marketing challenge, not a technological one”

This is even truer today than when Christensen wrote it because digitization opens up new routes to customer. As a result, product, monetization and customer acquisition have to align seamlessly around these new distribution opportunities.

The big trend is direct-to-consumer.

In the retail space, this mostly refers to the phenomenon of avoiding any intermediation – retailers or wholesalers or even any physical retail footprint – to sell directly to the consumer.

In the enterprise space, direct-to-consumer is different. Historically, it was not worth going directly to end users because they didn’t have much influence – or budget. Therefore, it was necessary to go through procurement teams and the choreographed dance of RFI, RFPs and workshops mentioned above.

But what is changing now is that technology products are not just sold directly, but are consumed directly. This makes software-as-a-service a much more disruptive phenomenon than people think: it’s more than a cheaper deployment method, it is a way to circumvent the central buying function and reach the end user.

In this context, it’s clear SaaS companies should have products marketed to end users, simple enough for them to consume without heavy configuration, and priced so they won’t appear on the central procurement team’s radar (e.g. freemium models to test and deliver value ahead of the paywall).

Slack is the example many cite. It markets directly to end users, who can try it for free. Once it has taken hold in an enterprise, it spreads virally thanks to strong network effects (even across enterprises). All the while Slack reaps the benefits by having a pricing structure that reflects usage.

Some argue that it’s harder to make this bottoms-up, direct-to-consumer approach work in areas like fintech, where regulation is important and IT security teams have more muscle. But we see it happening everywhere.

One fintech example that we came across recently, in the context of our upcoming wealth management report, is Hydrogen.

Hydrogen offers a classic bottoms-up approach: a user can provision for themselves a free sandbox environment from the Hydrogen website, pre-integrated with the services they’ll need (like Plaid). The customer only begins paying once they cross certain thresholds, such as API calls. In addition, that also take a jobs-to-be-done approach to solving end user problems by offering discrete services as no-code plug-ins to existing applications, meaning an end user can add a service like tax optimization in minutes.

Blake Bartlett OpenView partners quote

Avoiding the aggregator tax

Going direct to underserved or overserved consumers is the new playbook for disruptive innovation, but it doesn’t mean companies can avoid the aggregator tax. In fact, costs just get reallocated: in retail, CAC is the new rent, while in enterprise, CAC is becoming the new senior sales rep.

Nonetheless, while unavoidable, businesses can minimize the aggregator tax.

One way is to invest in brand.

A lot of startups seem to believe investing in brand is a luxury. We don’t agree. Marketing is like a pump: first, you have to fill it, if you want to draw it down. Sure, you can generate some leads from well-targeted paid campaigns, but it’s not a sustainable endeavor and you’ll end up paying more to aggregators over time. Instead, you want to run paid campaigns into a customer demographic that’s heard of your company and thinks positively about it, which will improve your ad score and lower your ad cost.

You should also invest in data. Going direct to consumers means you know more about your customers than your industrial age predecessors ever did. It’s critical to capture this information in order to:

– build proprietary routes to customers

– learn more about your users to better target and to aid self-discovery through recommendations

– learn how consumers use the solution so you can constantly improve the utility of the product, everything from ease of purchase to completeness of solving user’s problems.

You must leverage the power of networked buyers.  At the most basic level, your product should be differentiated enough that customers will want to advertise it on your behalf. That customer advocacy might come in the form of a positive review or post, but as former podcast guest Julian Lehr highlights, it may also come in the form of signaling.

In addition to advertising, the networked customer can be an acquisition channel.

Wherever possible, you should try to build into your solution the viral features that make the product better when it’s used together with others (e.g. messaging), that lead to customers acquiring other customers. And, even when it’s not possible to build these viral features into the product, it’s possible to build them around the product. A Nike running shoe has no inherently viral features, but the community it has built around its products, the millions who share fitness information, definitely does. And it’s not just consumer brands that can create communities. Consider Salesforce, for instance, which has a community of over 2 million organizing events and sharing content. This attracts others, but also binds together the users in a way which makes it hard to leave the community (by choosing a different product).

Don’t just rent commodities, rent luxuries

If you’ve read this far, it probably won’t come as a surprise that we advocate for renting commodities. Don’t buy your own servers, for example, or write your own accounting software. This will keep operating costs low and variable.

However, we also advocate for renting specialist skills. It allows more cost flexibility and avoids underutilization, but it also reflects a structural shift.

The best people increasingly don’t want to work for a single company. They like the variety and the speed of learning that comes from working across multiple companies and projects. And, platforms are emerging that go further than just matching companies with freelancers – platforms that put together, manage and take responsibility for (the output of) interdisciplinary teams. Effectively, they give businesses greater flexibility and quality at scale and specialists the security and freedom to keep learning.

Achieving internet escape velocity

Brett Bivens, a venture capitalist at TechNexus, came on the Structural Shifts podcast earlier this year to talk about his theory of “Internet Escape Velocity”.

Essentially, internet escape velocity is what happens when a company successfully executes the strategy playbook described above. That is:

– it identifies an underserved or overserved niche

– it leverages internet distribution to reach those customers directly

– it unleashes a growth loop by combining the reinforcing properties of product, distribution and monetization, and

– it uses data and marketing to avoid the aggregator tax

At that point, it hits internet escape velocity, becoming capable of crossing the chasm of precarious long tail supplier to become an aggregator itself, using the pull of its loyal customer base to pull in more suppliers or to launch new own-label services. Brett uses the example of Spotify, which he describes as follows:

“over time, as they expand and gain leverage, podcasts are a higher margin business, social products are a higher margin business, marketplace products are a higher margin business for them. And so, by owning the consumer demand via the lower margin streaming business, they have the opportunity to expand into those areas.”

When a company hits internet escape velocity, it also has the option to invest in fixed assets. As we showed in a paper we wrote with Dave Galbraith in 2016, internet-era companies tend to become asset heavier over time as they seek to entrench their position and deliver better customer fulfillment. But the critical point is that they invest after they achieve a sustainable route to customers. Assets grow like the roots of a tree, downwards from distribution rather than upwards from production.

If, however, a company that hits internet escape velocity doesn’t want to invest in fixed assets, the good news is that it’s never been easier to rent supply in the post fixed-cost economy.

To turn Adversity into Advantage, Banks need to Renovate in Winter

Renovate in Winter

To turn Adversity into Advantage, Banks need to Renovate in Winter

by Ben Robinson | March 30, 2020 | 8 minutes read

Crisis is not the time to stop all IT projects, but to double down on the ones that really matter.

Don’t pull up the drawbridge

Beware risk and opportunity cost

Bag some quick wins

Enterprise Software Stack Systems of Intelligence
How the Banking Enterprise Software stack is splitting

Consider Impact on the future

The Future of Banking
The Future of Banking and the Strategic Imperative

This a data play. It requires understanding customer context (interaction preferences, financial situation, needs) and be able to match to the right offering. In the first instance, financial services companies will do this for their own labelled services, but increasingly — to maximize utility and convenience — they’ll need to do it for third-parties services as well (requiring an extensible product catalog) and intermediating and bundling if necessary (which necessitates managing real-time risk). As a third phase, these same institutions can then orchestrate value between the different parties on the platform, stepping back from intermediating and becoming a system of collective intelligence.

Don’t waste a crisis

Articulate the change narrative

Use stop/go triggers

In summary

The new moat in financial services (and why P. Thiel, not W. Buffett,…

The new moat in financial services

The new moat in financial services (and why Peter Thiel, not Warren Buffett, is the new investment wizard)

by Ben Robinson | July 26, 2019 | 11 minutes read

In the networked age, scale of production is no longer a moat. Instead, network effects are the new moat. Peter Thiel gets this; Buffet doesn’t.

I look for economic castles protected by unbreachable ‘moats’ –Warren Buffett

The quote above from Warren Buffet, a statement he first made in a 1996 investor letter, is one of his most famous. It neatly encapsulates his investment approach: invest in giant companies that can achieve a “moat” by operating at a scale that others can’t reach.

By spreading the fixed costs of expensive, non-transferable assets like machinery or a banking licence, as well as highly-geared operating expenses like brand marketing and regulatory compliance, over a larger revenue base than competitors, these companies could be better known and cheaper. And, if you look at Buffet’s portfolio, it’s full of companies operating in industries with high fixed costs and high operational gearing: capital goods companies like BYD, consumer goods like Coca Cola and, above all, financial services companies like Wells Fargo, Amex and Bank of America.

The investment approach was massively successful — until it wasn’t. In the period 1979 to 2008, Warren Buffet outperformed the S&P 500 by 12.6% a year on average, cementing his reputation as the Wizard of Omaha, the most successful investor of all time. But — a less known fact — since the financial crisis, Warren Buffett has underperformed the S&P. One might be tempted to attribute this relative under-performance to the heavy financial services weighting in the Berkshire Hathaway portfolio. However, while a factor, deeper structural changes are at play.

Problem number one with the Buffett investment philosophy is that, in the digital age, critical mass is within most companies’ reach. Critical mass — or minimum efficient level of scale — is the scale of production a company needs to reach where it won’t have a major unit cost disadvantage compared to its competitors. After this point, diminishing returns to scale kick in, which means that even if a competitor has greater volume it won’t translate into the same order of magnitude differential in unit costs.

However, as we’ve written before, companies can now plug into the scale economies of third-parties like AWS, which spread fixed costs over the volumes of all customers, to get to scale faster. In banking, you see the emergence of banking-as-a-servce providers, like Railsbank or SolarisBank, levelling the field for new entrants. All in all, this means that scale does not represent the barrier to entry it used to.

Scale can become a hindrance

The second problem with Buffet’s investment philosophy is that diseconomies of scale, or negative returns to scale, manifest themselves more frequently and earlier.

In the industrial age, the trick to achieving an unbreachable moat was to produce standardized goods at mass scale and then invest in marketing to create sufficient demand to sell all of these goods. The challenge now is two-fold. Firstly, the broadcast channels that companies used to advertise are being eroded at the same time as there are many more demands on the consumer’s attention, making it harder to engage in the same type of mass-marketing.

The second issue is that, since consumers are now online, we can know much more about them, as well as have a direct relationship with them. This means that at the same time as it’s become possible to operate profitably at smaller scales of production, it’s become possible to produce goods which cater to smaller customer demographics, and to reach these customers directly — which explains the rise of artisanal goods and direct-to-consumer brands.

But, for digital goods, it goes further, artificial intelligence increasingly allows platforms to match services to customers as well as personalize services to each customer.

To put it another way, in the digital age, the mass consumer is dead.

The new moat

This begs the question, is it still possible to create a moat in the digital age? One answer could be that the idea of a moat is obsolete, a relic of the industrial age, sort of what Elon Musk said when he challenged Warren Buffett recently. But, the reality is a new moat is possible and it’s the diametric opposite of what came before.

Scale isn’t the barrier to keep out new entrants, scale is what attracts new entrants to work with you. Scale doesn’t allow you to push a mass produced product to the mass consumer, scale is what enables you to tailor an individualized product to every consumer.

“I think moats are lame. If your only defense against invading armies is a moat, you will not last long” – Elon Musk

This definition of scale is one that accepts and capitalizes on the new realities of the digital age. Maximizing production scale by itself is less of a competitive advantage and, increasingly, a competitive disadvantage. But the fact that consumers and business are connected means that a new competitive advantage can be achieved by maximizing network size.

Where a network has strong social engagement, like Facebook, adding more users increases the value of the network for everyone. Where a network matches buyers and sellers, like Amazon, increasing the network size increases choice and, by extension, value. Where a platform analyzes data to serve up the best results, like Google, the more data that comes from adding users, the better the results become. And most platforms are a combination of these social, two-sided and data network effects.

What is more, the new moat is a superior moat. Supply-side economies of scale, while a formidable barrier to entry in the industrial age, always suffered from diminishing returns.

Demand-side economies of scale, however, are subject to increasing returns to scale since more users create more value for other users in a self-reinforcing positive cycle. This is why in markets where network effects are strongest, there are winner-takes-all dynamics.

Does this mean that supply-side economies of scale are irrelevant? Not at all, as we wrote a few years ago, these platforms based on demand-side economies of scale (network effects) often become asset heavy as a way to reinforce the strength of these network effects and maximize profitability. But the difference is that maximizing scale economies was not the goal in itself. Instead, these companies found a route to mass adoption and, from there, put in place the assets to sustain the network. In other words, a business grows its assets top down like the roots of a tree.

If the new moat is to achieve network effects, how can these be achieved in banking? In our mind, this is probably asking the wrong question. Banking is inherently a transaction-based activity. This makes it unsuitable to most types of network effects.

For example, most companies that have tried to build social network effects into banking, either as part or whole of their USP, have failed. We don’t want to chat with our friends specifically about money, we don’t want to share all of the information on our assets and liabilities. Which means that, although the new banks sprouting up might be cheaper and more convenient than what came before, they aren’t able to arrive at meaningfully and sustainably lower costs of customer acquisition numbers once they’ve gone beyond the early adopter audience.

It is possible to create marketplaces for financial services, but because banking is transaction-based (and fundamentally not a social activity), the surface area around which to create a marketplace is limited. Basically, we don’t spend much time on banking apps, which makes it difficult to introduce us to other products and services, which we then don’t purchase frequently anyway.

This podcast was recorded at FinTECHTalents’19 Festival: we’re exploring the potential of unleashing network effects in financial services. Ben Robinson is joined into the conversation by: Evgenia Plotnikova (Partner @ Dawn Capital); Martin McCann (CEO at Trade Ledger); Oliver Prill (CEO at Tide Business Banking).

Some banks and fintech providers get round this by targeting specific demographics and then giving them the tools they need to run their business/life, such as Tide, which understands that freelancers and small businesses will send invoices and submit expenses more frequently than they’ll apply for a loan. But, these business are niche.

When this is attempted on a bigger scale, it comes back to the same problem of unit economies: high CAC in the absence of social network effects and low lifetime value in the absence of the engagement.

The mistake we think many people make when they think about banking and network effects is to apply the following logic: banking is a massive market, therefore we must target it and find a way to generate network effects. We believe it is smarter to turn the logic on its head and think about how to put banking into channels and services that have high engagement and strong network effects, what Anthemis calls “Embedded Finance”.

As Amazon is showing, the goal isn’t picking off a few high value revenue lines, but making value flow ever more easily within the Amazon ecosystem, removing friction and making it easier for buyers and sellers to trade. Similarly, the Alibaba and WeChat models both serve a higher purpose: to embed financial services into people’s lifestyles.

The direction of travel can go in the other direction too: that is, starting with banking and seeking to embed it in other services with higher engagement. This is what Moneo is trying to do and what TinkOff Bank in Russia has done so successfully. Through partnerships as well as launching its own products, Tinkoff has created a super app akin to WeChat in China where consumer can do everything from booking theatre tickets to giving their kids chores.

But, in general, it seems more probable that banking will get embedded into other services than vice versa for the reasons already stated: it’s a high CAC and low engagement starting point from which to build out an ecosystem or Super App.

That doesn’t mean that there won’t be plenty of opportunities to build big businesses in banking, that enjoy strong network effects. But, to our mind, these are unlikely to be directly client-facing.

Earlier this year, we wrote a piece about systems of intelligence in finance. The piece looked at these systems mostly from a supply-side and architectural standpoint, arguing that solution architecture needed to change in response to the split of distribution and manufacturing and to capitalize on open banking. It concluded that systems of intelligence would emerge as the most valuable parts of the Enterprise IT value chain.

Here we make the same argument, but from more of a market standpoint. If we accept that banking will become increasingly embedded in third-party services and channels, it doesn’t necessarily follow that, as many people argue, banking will become completely commoditized.

As markets digitize, two types of intermediaries tend to emerge: those that seek to internalize network effects by commoditizing supply, aggregators like Amazon or Facebook, and platforms that externalize network effects by empowering suppliers, like the Apple AppStore or Shopify (Ben Thompson sets out this distinction very well in this much recommended post).

In financial services, then, the same pattern will play out: aggregators like Amazon will commoditize financial services suppliers, while platforms will emerge to intermediate between suppliers and distributors in a non-zero-sum, value accretive way. These platforms will be systems of intelligence.

Systems of intelligence are evolving. Today, most systems of intelligence are deployed for individual clients and with the end of digitizing services. But this is just the first step. Digitizing services makes them consumable through non-proprietary bank channels, but it also generates a new stream of data that can be used to make the services better fit consumer needs. So the next step will be that systems of intelligence will then use that data to help providers more intelligently price and package financial services.

But once that has been achieved, the opportunity will exist to then serve up the right service to the consumer at the moment of need, which mean systems of intelligence become systems of network intelligence, matching the needs of consumers with the inventory of suppliers in the smartest way.

This is the evolution we observe happening at companies like additiv, Assure Hedge and Trade Ledger. Trade Ledger is digitizing the origination of credit services so that lenders can supply credit at the right price and with the speed needed by fast-growing SMEs. But beyond that, it is able to use data to give lenders a real-time picture of asset quality, even for intangibles assets, allowing lenders to offer new types of services better matched to changing customer needs. But, ultimately, the opportunity exists to then link lenders with the different players in the ecosystem, helping embed banking into whatever is the right channel to serve the customer at the point of need. Martin McCann, Trade Ledger CEO, puts it well in this excellent blog:

“Within business finance, the opportunity exists not just to connect banks with their customers, but banks with banks, corporates with corporates, corporates with complementary third-party services providers and so on.”

If Warren Buffett has missed the shift from supply- to demand-side economies of scale, there is one investor who most certainly hasn’t. That is Peter Thiel. His investment in Facebook, a business underpinned by massive network effects, made him a billionaire. Conversely, Buffett passed on Facebook, like Google and Amazon, because he couldn’t get comfortable with the valuation, saying “I didn’t understand the power of the model as I went along.”

And the performance of the two investors also couldn’t be more divergent. Whereas Buffett has underperformed the S&P since 2009, Thiel’s Founders Fund has more than two-fold outperformed the VC fund industry since 2011 (the only figures we could find in the public domain). Since 2011, the Founders Fund is up by $4.6 for every $1 invested.

And where is Peter Thiel investing now? If you look at his holdings, there are many B2C companies there for sure. But, more than anything, there are systems of intelligence — across many industries, but especially in financial services. This leaves Peter Thiel well-placed to capitalize on what Matthew Harris, another venture capitalist, sees as the fourth major wave of digitization after internet, cloud and mobile; one that, in his view, will create more value— $3.6 trillion — that its three predecessors combined.

So, you don’t need to believe us that systems of intelligence are the next big thing. Just look to Peter Thiel, the new investment wizard.

Digital Era Banking Systems

Digital Era Banking Systems of Intelligence

Digital Era Banking Systems

by Ben Robinson | Dec 17, 2019 | 11 minutes read

The banking software market is reconfiguring around the demands of the digital economy — and value is accruing to new systems of intelligence

In Clayton Christensen’s Law of Conservation of Attractive Profits, he talks about the “reciprocal processes of commoditization and de-commoditization” that occur in technology value chains when product architectures change:

“The law states that when modularity and commoditization cause attractive profits to disappear at one stage in the value chain, the opportunity to earn attractive profits will usually emerge at an adjacent stage.”

Our view is that this same process of commoditization and de-commoditization is playing out in the market for banking software. Changes in technology (cloud and AI) as well as changes in regulation (real-time payments and open banking) are causing a formerly integrated system to become modularized and new players are emerging to exploit this shift— new core banking systems but also new systems of intelligence that, akin to operating systems, orchestrate value across their networks.

A brief history of banking software systems

When we look at the technology debt in the banking industry, we might forget that banks were once IT pioneers. Banks were among the first industries to use software, adopting branch accounting systems to keep records of customer bank balances as well as to calculate interest, fees and tax.

But, because banks were such early adopters, they wrote their own applications — there was no software industry at that time from which to buy applications. This might not have been a problem except that 1/banks didn’t stop writing applications when commercial software arrived and 2/they have kept and extended those same branch accounting systems ever since — producing the kind of unwieldy system architecture depicted below.

A typical universal bank system architecture (source BCG)
A typical universal bank system architecture (source BCG)

Smaller and newer banks (from the 1980s onwards) skipped the branch accounting system and instead moved to packaged software, integrated core banking systems. These systems had many advantages: they could run on much cheaper hardware (and software) than S/360 mainframes; they could keep separate records based on parties and products (so that it was possible to have the same customer across branches and products and to provide consolidated views of customer holdings); and, they were integrated front-to-back — from the teller to the general ledger — meaning that changes could be applied across the whole system, reducing significantly both the run-the-bank and change-the-bank costs. And so banks running integrated core banking systems were in a position to achieve scale economies as well as to cross-sell effectively and, when product builders were added, to launch new products to market quickly.

System S/360

Bank systems in the internet era

With the arrival of the internet, banks opened up proprietary channels (apps and internet portals) which allowed customers to query their own bank records and set up payment instructions. But that was the extent of the upgrade: neither branch-based accounting systems nor integrated core banking systems were significantly re-architected in response to internet banking. In fairness, some core banking systems were already real-time and most have been scalable enough to cope with the rise in customer interactions. But the situation is changing.

Integrated to Internet Banking

The open banking era

In most industries, product manufacturers have a choice about whether or not they sell through distributors. In banking, in Europe and an expanding number of other places, this agency is being lost. Open Banking legislation is forcing banks to put their inventory online by obliging them to share customer transactional data with third parties (where customers give consent). In effect, banks face a stark choice: become aggregators of own-labelled and third-party products or risk being disintermediated by other aggregators, whether from inside the industry or outside (e.g Amazon or Alibaba).

The Open Banking era
CB Insights showing the spread of Open Banking legislation across the globe

In addition to open banking regulations, most jurisdictions have enacted — or are enacting — legislation related to real-time payments. This will likely have a profound impact on value chains outside of just banking — for payment schemes, for instance — but in banking it will usher in an era of not just higher volumes, but lower fees per transaction, requiring a step change in scalability if banks are to be able to keep up — and to do so profitably.

In response to these two changes, the integrated nature of most banking systems is unsustainable. If banks are to distribute third-party as well as own-labelled products, they will need a separate system for distribution. If banks are to cope with the demands of ever-increasing payment volume as well as continually rising interactions, they will need to separate channels from manufacturing to boost straight-through processing (STP). To put this last point in context, if a bank moves from 99% STP to 99.9% STP, this would likely translate not to a 1% reduction in costs but more likely a 10x reduction in costs.

The future model for banking systems could be the retail industry where the major players have all created distribution systems independently of accounting systems. But there is precedent that is much closer to home: when regulators pushed for higher STP in capital markets in the early 2000s, the industry very quickly split between front office (the buy side) and middle office (the sell side) and systems were re-architected accordingly. And, whereas in capital markets there was a push for faster transactions, in banking there is both a push for faster transactions and a push to open up the industry to new competitors. As such, this split seems all but certain.

Systems of intelligence

At the moment, there is a tendency to try to put more and more logic into banking channels, but this is flawed. Proprietary banking channels are likely to disappear as banking becomes more “embedded” in other products and services (such as WeChat), making these investments increasingly pointless.

Instead, this logic needs to sit somewhere else, where it can be used to produce a high level of engagement across multiple channels, where it can be combined with data from multiple other parties and systems, and where it can handle inquiries independently of orders and order entry asynchronously from order execution. This somewhere else is a system of intelligence.

Systems of Intelligence Basic.png

We borrow the term “system of intelligence” from this seminal article from Jerry Chen, a Partner at Greylock. In his article, Jerry describes how application software is splitting into three layers: systems of engagement, systems of intelligence and systems of record. If we apply the same taxonomy here, customer channels are the system of engagement (although we prefer to use the term system of interaction because we see these as thin clients, integrated using REST principles); core banking systems are the principal system of record; and distribution systems are the systems of intelligence.

In Jerry’s article, he highlights the importance of technology changes in creating the opening for new systems of intelligence. One is cloud in that it adds a new level of scalability on which to build these systems, but the more important is AI, which fundamentally changes the amount of data we can process and the insights we can draw from it. Echoing Clayton Christensen, Jerry Chen says that, because of AI, 

Systems of intelligence in banking

In Jerry Chen’s article, he makes the point that providers of systems of record often have an advantage in creating systems of intelligence because they have privileged access to their own data. This is true for banking also, although open banking removes part of this advantage (for transactional information). A bigger advantage for incumbent banking software comes by dint of serving hundreds or, in some cases, thousands of banks; creating the pull to attract other data sources to mash up with data from their own system of record.

The playbook for incumbents, regardless of industry, remains Salesforce. A lot of people get excited about the Salesforce AppExchange, a marketplace for complementary applications, since it created a platform business model with two-sided network effects. But at least as important in amassing the data to become a system of intelligence are (now the Lightning Platform), its platform-as-a-service on which third-parties build native applications, and Mulesoft, its API integration platform, which allows third-parties to integrate their existing applications and datasets. Lightning and MuleSoft don’t just provide a route to data but lock-in and switching costs around that data. And then, working on this data and giving an additional incentive to share the data is Einstein, the Salesforce system for artificial intelligence, deriving insights for Salesforce and its customers. We would argue that it is ensemble — MuleSoft, Lightning, AppExchange and Einstein — that makes up the system of intelligence.

Salesforce’s system of intelligence

And so in banking it is unlikely that creating an AppExchange equivalent will be sufficient to create a system of intelligence.

It is likely to need all of the above components: an API platform, PaaS, AI and an app store. And let’s not forget that because of open banking, the distribution play for a banking system of intelligence goes further than distributing apps — to helping banks distribute third-party banking services.

This extends the list of necessary capabilities to include, for example, order management and an extensible product catalogue, as well as customer engagement tools that, among other things, would help identify the right content and services to offer up to customers at the right time and over the right channel.

In addition, we believe a key component of successful systems of intelligence will be to share intelligence across their ecosystems.

The idea, very well articulated in this blog by Peter Zhegin, is that the source of competitive advantage (the moat) is constantly shifting. Processes— and software — are declining in importance relative to data. And within data, Peter argues that the moat is moving away from data collection — amassing the largest possible data set with which to train a model that benefits the company’s product — to improving the collective intelligence of the network.

In banking software, therefore, advantage is moving from having the best application to having the most value-added ecosystem around that application (app store) to helping customers make smarter decisions (system of intelligence) to helping the whole ecosystem perform better (a system of network intelligence).

As a practical example, this could mean moving from providing independent banks with the best credit scoring model to facilitating an open banking network.

Commoditization and de-commoditization — the emerging vendor landscape

As in any market where the value chain is being broken up, there is likely to be a significant shake-up in the competitive landscape for banking software. The keenest fight will be to dominate the market for systems of intelligence, since this is where value will accumulate. But we are also seeing new entrants into the core banking market.

Since the system of intelligence aggregates logic away from the system of record, the system of record is required to do less. Effectively, the most important characteristics of the system of record will increasingly become speed and cost.

As a result, these systems will be re-architected for speed (into microservices) and they will be deployed in the public cloud. And it is no surprise, therefore, that we are seeing the arrival of new cloud-native core banking systems such as Mambu, one of the first and the most successful so far.

Furthermore, as the need for scalability increases, we predict that we may even see these systems further fragment, with the accounting capabilities (fees, limits, etc) splitting from the manufacturing capabilities, which, incidentally, seems to be how Thought Machine is architected.

Digital Banking to Real-time Banking

As regards the systems of intelligence, we foresee a three-player race.

The first players are horizontal systems of intelligence. Insofar as the system of intelligence is like an operating system (nCino actually calls itself “a revolutionary bank operating system”) — providing a consistent set of interfaces, mashing up and running analytics on multiple data sets — these systems do not need to be as domain-specific as systems of record.

Accordingly, there is the potential for horizontal players to make bigger inroads into banking software — such as Salesforce, which already has good traction in wealth management and is pushing aggressively into retail banking.

The second players are the incumbent providers of systems of record. Many are well-positioned — having the pull of large customer bases and investing in the tech infrastructure. Finastra, for instance, has assembled many of the underlying components of a system of intelligence — an app store (Fusion Store), a PaaS (Fusion Operate) and an API platform (Fusion Create). The bigger question is likely to be whether management at these companies will place enough importance on a platform strategy to be able to overcome the immune system challenges.

The third set of players are the new entrants. With a couple of exceptions, such as nCino, these are chiefly vertically focused: for example, Additiv is focused on wealth management (and increasingly credit), The Glue is focused on retail banking, and Trade Ledger on lending. While there are likely many more shared than vertically-specific components in banking systems of intelligence, which makes a cross-banking strategy possible, an initial vertical focus makes sense to build a network quicker (the micromarket strategy to overcoming the chicken-and-egg challenge) and conforms with the pattern of disruptive innovations, which are typically commercialized first in smaller and emerging segments.

To sum up…

In response to regulatory and technology changes, the banking market is undergoing a digital upgrade with new networked business models emerging.

The most successful banking technology companies will be those that align themselves with — and enable — this change.

Our bet is on those that can create the best systems of intelligence.

What is a Challenger Bank for?

What is a Challenger Bank for?
The battle is on to see which firms will dominate the internet-era banking market

What is a Challenger Bank for?

by Ben Robinson | Sep 18 2019 | 13 minutes read

The last couple of months have seen JP Morgan close its digital bank Finn, as well as BPCE close the UK arm of its digital bank, Fidor. This has led to a lot of speculation about whether it’s possible to run a disruptive business within an incumbent organisation. But, it also raises a simpler point. When does it make sense to launch a challenger bank?

Challenger banks are definitely in vogue. In the aftermath of the financial crisis, regulators sought to introduce more competition into their domestic banking markets. One of the most progressive was the UK regulator, which lowered capital requirements for start-up banks as well shortened the application process, leading to a influx of new competitors. But many jurisdictions, including the UK, also introduce Open Banking legislation, which obliges banks to share customer data with third-party providers, also increasing competition.

The Open Banking era
This graphic from CB Insights shows the spread of Open Banking legislation across the globe

In addition to the regulators’ efforts, technology has also lowered barriers to entry. Infrastructure services like AWS have reduced start-up costs while smartphones have opened up distribution at the same time as making possible new digital features, such as remote, paperless customer onboarding.

The result has been an explosion in the number of new companies offering banking services — 17% of all companies having been created since 2005, according to Accenture.

Accenture Beyond North Star Gazing Open Banking
Source: Accenture, Beyond North Star Gazing
Challenger Banks a Global Phenomenon

But digital banks are not just banks without branches. They are offering something different. Part of this is about customer experience — they have built their offerings from scratch, taking advantage of new technology to mould their services around people’s lives. But part is also around business model.

A look at the latest Monzo annual report illustrates this well. It has over 2m customers, but it makes significant losses. It has over 2m customers, but makes net operating income of only £9.2m (£5.7/customer). The majority of its money comes from fee income, not interest income. And, despite heavy losses, it is ramping up marketing expenses (up 700%) and pushing forward with international expansion.

Note: Monzo reported 2m customers as of May 2019. However, most of the metrics above are calculated based on the total number of customers (1.6m) at the end of fiscal year, Feb 2019

In this regard, Monzo — like the other challenger banks — is operating a classic digital-era business model. In contrast to traditional banking models, it recognizes that distribution is not the primary point of differentiation in the value chain; but, instead, customers are.

Number of Customers per Challenger Bank

It is this desire to maximize customer numbers that explains why, despite a historical customer acquisition cost of less than £3, Monzo is engaging in TV ad campaigns. This explains why its investors are prepared to cover its losses and fund its international expansion. And lastly it explains why most income comes through fees, not interest income. Because this is a business where, with low customer acquisition costs, low churn (NPS is +80%) and low cost to serve (GBP30 per account at present and falling fast as scale economies kick in), the incentive is to maximize lifetime value.

Valuation per customer Challenger Banks

Monzo, like other challengers, will seek some direct customer monetization for sure — Monzo is now offering loans — but this is likely to be levied on those who can most afford it, in the shape of premium subscriptions, ensuring that most services remain free (current account, foreign ATM fees) to attract as many new customers as possible. Instead, most of its monetization will be indirect, using the pull of its large customer base to bring in third-party fees. At present, most of its fee income comes from interchange fees as its customers spend money using their Monzo debit card, but over time other routes will make more meaningful contributions. Monzo has said that it wishes to become its customers’ “financial control centre” by introducing them to the best possible third-party financial services and, although the resulting commissions from these introductions are small at present (just £85k), this will grow as two-sided network effects materialize.

Monzo customer contribution margin

For incumbent financial institutions, it is difficult to match the challenger bank model. Their businesses were created for a different age, where distribution was the choke point in the value chain. The need for a costly and difficult-to-achieve banking licence plus a network of physical branches kept out new entrants, meaning banks could push undifferentiated, expensive products to captive clients. And now it is extremely difficult for banks to change course and match challenger banks like-for-like — they don’t have the cost base, the financial incentives or innovation capabilities to do so. And so many banks are starting to launch challenger banks themselves.

Incumbent Banks Offering
The now defunct universal business model where banks were able to mass produce undifferentiated products

However, the bank-within-a-bank model is also difficult to pull off.

Firstly, as Aperture-subscriber John Hagel so eloquently describes in this piece, you have the problem of the immune system fighting off anything that threatens the business model and revenue streams of the body corporate (what in more successful companies might be described as the Innovator’s Dilemma). This likely contributed to closure of Fidor UK and explains why the rest of the business is up for sale. But the challenge to incumbents is very real. As the following table from Citi shows, banks’ RoE is much more sensitive to falls in revenue than reduction in costs, meaning that with stubbornly high costs — and in the absence of business model change (see below) — it will be difficult for banks to countenance a strategy that cannibalizes existing revenue streams.

Source: Citi GPS Research

Assuming that the immune system can be countered — by creating a completely separate organization, with different people, processes, tech, brand, incentives and reporting directly into the CEO — then you have the problem that innovation is hard. This seems to have been more of the root issue at Finn. Built on the bank’s existing IT infrastructure, its objective was more around putting a new UX on traditional products than using the virtues of digital to create a unique offering. As a result, it didn’t manage to attract large numbers, let alone introduce viral features that leverage the power of networked consumers, as Revolut has successfully done.


The last problem is one of strategic intent. If a bank launches a digital bank because its strategy is a) to defend itself against challenger banks; b) to lower cost to serve by using digital channels; c) to improve User Experience; d) to capitalize on Blockchain/AI/IoT/Cloud or e) to change its perception among younger customers, it’s probably going to fail.

Launching a digital bank is about launching a digital era business model, which goes way beyond changing brand perception, user experience or moving customers onto cheaper-to-serve channels. As noted above, it is about maximizing customer numbers and engagement to activate demand side economies of scale. This requires clear strategic intent because, in turn, it requires organizational transformation. Launching a challenger bank can be a (faster and less disruptive) route to digitization, but it is neither an easy option nor a panacea.

Our view is that a challenger bank strategy has a higher likelihood of success if is underpinned by one (or more) of the following six objectives.

It is interesting to see Goldman Sachs’ digital bank Marcus referenced in so many of the articles on Finn. For us, it is very different. For instance, it is built on a new technology stack. But most importantly, it moves Goldman into a completely new space, consumer finance, where it does not have the cannibalization concerns that trigger the corporate immune system. This allows it to operate under very different constraints and, like other new entrants, challenge the status quo with a proposition that includes market-beating interest rates, no origination or late fees as well as customizable payment dates and payments. And it’s working: Marcus had 4 million customers and $46bn in deposits at the end of March 2019, two and half years after launch.

In the same way as entering new markets allows the new business to operate more freely, so does entering new countries. It is also a less risky strategy than M&A, which made sense at a time when distribution was the barrier to entry, but now encumbers the acquirer with all of the legacy issues they inherit. And this is why the challenger-led strategy is being pursued by many banks, including DBS, which has launched digibank (“a bank in a smartphone”) in India and Indonesia with already over 3m customers — and why it is looking to do the same in Vietnam.

Launching a challenger bank with the purpose of bringing banking to the unbanked is by definition the antithesis of cannibalization — because no one was providing these services in the first place. And, as banks like CBA in Kenya have shown with M-Shwari and now Stawi, when you combine mobile distribution with low costs and intuitive user experience, you can succeed in bringing financial services to millions of people. But, as demonstrated below, while countries like Kenya and China have very successfully leveraged digitization to tackle financial inclusion, there still exists massive scope to do the same in populous counties like Egypt, Indonesia or Pakistan.

The Financial Inclusion Opportunity

Another reason for launching Marcus was that Goldman Sachs can use retail deposits to lower its group cost of capital. But a business where this is more transparently the objective is EQ Bank in Canada. It is a subsidiary of Equitable Bank, which provides residential and commercial real estate lending services, and the bank uses EQ customers’ savings to fund its lending, allowing it to start to increase its net interest margin (now at 1.6%) in a low interest environment. Structuring the group like this not only creates complementarity between the bank and its challenger brand, as opposed to a cannibalization threat, but also reinforces incumbency advantages. EQ Bank can sustain its above-market deposit rates thanks to its parent’s large lending book.

Equitable EQ Bank Funding Mix

Another reason to launch a challenger bank is to attempt lower-risk and faster-to-value technology renovation. Banks sit on decades of technology debt, batch-based legacy systems built around products not people that have been continually added to over time, resulting in massive cost and massive complexity. If banks are to compete on price and on user experience with digitally-native challenger banks, then they will have to address this technology debt. But doing so is expensive and risky, which why it is tempting for many banks to start again — create a new bank with new technology.

A typical universal bank system architecture (source BCG)
This BCG image shows the mass of interdependent systems and interfaces within a typical universal bank

This “build and migrate” strategy is still somewhat unproven , even though it looks like some banks like Santander, with Openbank, may be going down this route (its annual report states that Openbank is “the testing ground for our future technology platform”).

For banks considering this strategy, they should be mindful that they will have to run two IT platforms in parallel for a good while (it is unlikely that regulators would let incumbents close all branches — or indeed the bank itself — for a long time). They should also be aware that there will be customer attrition in the base business as they divert investment into the new bank and also likely attrition when they try to move across customers to the new bank. In addition, they should start small, that is, with a single product offering like savings, which will enable them to test the market proposition before committing big expenditure, get fast RoI on the initial capital expenditure and minimize the risk of rejection from the corporate immune system — at the same time as probably lowering cost of funding (or, in the case of one challenger bank we consulted, whose first product will be to lend boomer savings to millennials, increase asset yield). Furthermore, if the technological renovation is successful and the bank creates a great platform, then, as Starling, OakNorth and Ant Financial have done, it can sell this to other banks — the “make yourself the first customer” model of creating an exponential software business.

Oak North, a unicorn SME challenger bank, sells its lending analytics platform to other banks

The counterargument to the “build and migrate” strategy is two-fold. Firstly, modern core banking systems are modular, meaning that progressive renovation is possible — replacing systems one by one — to combat risk and speed up time to value. In our experience, however, these projects tend to more complex than they seem and subject to the same issues as all in situ transformations, such a scope creep. A better argument for not executing a build and migrate strategy is that it is increasingly possible to achieve what banks want — improve customer user experience, launch digitally-native products, run advanced analytics and open up to third-parties — without replacing all of their back-office systems, as vendors like Additiv and The Glue are helping institutions to do.

In a blog we wrote last year, we set out what we thought were five viable banking business models for the digital age. However, at least three of these new business models were off limit to banks given their organizational constraints. Launching a challenger bank removes those constraints and allows banks to unbundle themselves by launching a narrowly-focused digital proposition (in terms of product offering as well as demographics) and then to rebundle themselves around this proposition. This is what fintech companies like Transferwise, Robin Hood and Zopa have done successfully and we are starting to see banks do the same — like Imagine Bank from Caixa which successfully attracted a new customer demographic with a convenient and high margin savings product and has since rebundled a whole set of own label and third-party services.

Unbundle to Rebundle

There is also the possibility to do this unbundling to rebundling via a holding company model. This represents the digital equivalent of the traditional universal banking model but where each product offering is run by a separate subsidiary. Doing this keeps each unit nimble enough to compete to respond to market shifts, permits partial customer acquisition, but also allows the overall group to achieve economies of scale (both supply-side, like IT, and demand-side, like customer data insights). In banking, the best example of this seems to be Pepper from Bank Leumi, which is building up a set of discrete product propositions.

If an incumbent bank chooses not to launch a challenger bank, what are its options?

It could choose to do nothing, essentially pursuing a mix of tactical options like cutting discretionary spend, shrinking risk-weighted assets and lobbying the regulator to slow, or reverse course on, new legislation. But, even though this might get management through to its next stock option vest, this isn’t a long-term remedy.

Another option could be to go upstream. We see this a lot in wealth management. Since HNWIs want a somewhat bespoke service and interaction with a relationship manager, then a lot of banks are moving to serve exclusively these HNWIs and UHNWIs where they think they can earn good fees for the foreseeable future. However, since many banks — as well as independent asset managers and family-offices-as-a-service focus on the same market — fees are gradually eroding. But more importantly, it leaves the banks open to classic disruptive innovation as the providers who now serve retail and mass affluent customers with digitally native services start themselves to move upstream.

In our view, for the banks that don’t want to launch challenger banks, there are only really two options. One is to become a bank-as-a-service, offering their back office and compliance to other banks and fintech providers, as banks like Bancorp in the US have done. But, this is a low-margin business. A better option is what we call the thin, vertically-integrated bank, where a bank starts to offer third-party services alongside some of its own products, capitalizing on its advantages —a bank licence, trust, the pull of a large customer base — to give its customers more choice. The challenge here is, of course, that this is a radically different business model which is likely to activate the corporate immune system.

Vertically integrated digital bank

So, the conclusion seems to be: if a company can dismantle the corporate immune system for long enough to adapt its existing business, then a challenger bank might not be the right option. Otherwise, it probably is.

Is Spotify the new MoviePass?

Is Spotify the New MoviePass
Are US subscription-based business models an innovation, or a compromise?

Is Spotify the new MoviePass?

by Ben Robinson | Jun 7, 2019 | 9 minutes read

Have you ever wondered why the social features on Spotify aren’t better? It’s not difficult to imagine a more engaging experience if, say, it was easy to co-create playlists or you got updates on what friends and artists were listening to. One explanation could be that it is hard to build siloed social interaction — outside of horizontal apps like Facebook or WhatsApp. But my hunch is that, while Spotify wants to maximize users, it doesn’t want to maximize usage. Why? Because unlike most aggregation platforms, its marginal cost grows with usage, meaning that — like MoviePass — its best customers are actually its worst customers. That’s why it’s struggling to generate profits, why it’s struggling to protect itself from rival streaming services and why it needs to become the pre-eminent platform for podcasts.

The move to subscriptions

Source: Zuora

Is a subscription model right for Spotify?

Spotify vs Netflix

Netflix vs. Spotify
Netflix business model vs. Spotify's Business Model

Spotify as Netflix

Spotify vs Amazon and Uber

Uber creating consumer surplus

Spotify vs Tencent Music

A new way?

Is Blockchain the internet of finance?

Whether the solution comes through crypto or an infrastructure upgrade, not being able to execute on micropayments-based business models is bad for business and bad for consumers. Furthermore, it is likely to give a growing advantage to Chinese vs American internet giants as they battle to colonize the rest of the world.

The Internetworked Nation

The Internetworked Nation

The Internetworked Nation

by Ben Robinson | May 23, 2019 | 9 minutes read

In one the most notorious scenes from cinema history, Harry Lime, a racketeer played by Orson Welles in the The Third Man, compares the cultural achievements of Italy and Switzerland. He says,

“In Italy for thirty years under the Borgias, they had warfare, terror, murder, bloodshed. They produced Michaelangelo, da Vinci, and the Renaissance. In Switzerland, they had brotherly love, five hundred years of democracy and peace, and what did they produce? The cuckoo clock.”

Still image from Carol Reed’s The Third Man
Still image from Carol Reed’s The Third Man

It’s probably unfair to suggest that this stereotype of Switzerland as safe and sterile originates directly from the film, but it has certainly helped to perpetuate it. According to its purveyors, the stereotype holds that Switzerland is too rich to take risks, too boring to be creative, and officiously obsessed with petty rules governing everything from rubbish collection to toilet flushing — in short, the antithesis of innovation.

However, not only is this stereotype misplaced — Switzerland invented the internet, not the cuckoo clock— but Switzerland’s networked, outward-looking economic model sets it apart from other nations and provides a blueprint for how to succeed in the digital economy.

While it is high time to debunk some myths about Switzerland, Switzerland’s success should still be seen on its own terms. If we look at the start-up scene, for example, it bears little resemblance to Silicon Valley. Tech companies here tend to ramp up slowly. According to startupticker, Swiss tech start-ups take about 10 years to reach scale and grow almost twice as fast in their second decade as in their first. Without a large domestic (consumer) market to sell into, they look to sell to corporations and they seek to internationalize early, with the majority exporting within the first three years of their life. And, they tend to take comparatively little external capital with most businesses becoming stable, moderately growing SMEs rather than blow-out exits or IPOs.

Still image from Carol Reed’s The Third Man

Looked at on these terms, we could cast Switzerland as small-scale and parochial. It is a long way from blitzscaling and moving fast and breaking things. Instead, it is very much the slow and steady model of measuring twice and cutting once that leads to fewer unicorns and billionaire celebrity founders, but which also produces fewer negative externalities and inequality.

Should Switzerland be aiming to become the Silicon Alps?

The fact is that drawing a comparison with the US tech scene is wrong and Switzerland should resist portraying itself as “Silicon Alps” or the “Silicon Valley of [insert term here]”. Like Operation Libero, the Swiss political movement that punctured the rise of the far-right Swiss People’s Party by reframing the political debate, Switzerland should not define its credentials in terms set by others. Instead, it should present the uniqueness of its model: a complex, adaptive system that combines lasting quality with cutting edge innovation and that marries economic growth with inclusiveness.

Flavia Kleiner, of Operation Libero, on the importance of narratives

A complex adaptive system is one made up of a network of interdependent actors that collectively adapt quickly to changing environmental factors. For Switzerland, this system is made of a series of layers – from government to brand – each resilient and adaptive, which, as a whole, both reinforce the strength of the overall system and leave it well positioned to capitalize on the future.

Switzerland is a complex adaptive system
Switzerland is a complex adaptive system

A distributed political system. In Switzerland, political power is distributed between the federal, cantonal and communal governments. This allows for a high degree of adaptiveness by keeping government nimble and localized enough to respond to diverse needs. In addition, it encourages competition between the different cantons and communes that promotes good governance and which, in practice, results in policies which are both pro-business, such as low tax and low employment regulations, and pro-citizen, such as a strong safety net, and which overall create the conditions for entrepreneurial risk-taking and shared prosperity.

An inclusive culture. It is testament to its inclusiveness that Switzerland is able to seamlessly accommodate four different language groups and a population with 25% foreign-born residents. But, it is in its ability to meld the high-tech and the rural, the modern and the traditional into a coherent, confident whole that it really excels. In much of the world, digitization is leaving behind rural areas, but not so in Switzerland.

Switzerland Rural Modernist
Switzerland Rural Modernist

A robust industrial set-up. Switzerland has a unique ecosystem, spread across multiple cities as opposed to one single dominant center. In place of unicorns it has lions, top-of-the-food-chain organizations that act as key nodes in the network. These include multinationals (Switzerland has more per capita than any other nation), top class universities (one in the top 10, two in the top 25), NGOs (including the UN) and scientific research hubs, such as CERN.

The Rolex building at The École polytechnique fédérale de Lausanne

These organizations support a much larger network of SMEs, which form the backbone of the economy and middle-class employment, and attract key flows of capital and information. This last point is critically important since globalization is entering a new phase, one centered on virtual not physical flows. In practice, this means that big is not necessarily best, that the future probably doesn’t look like our popular imaginings and that small countries, like Switzerland, can have outsized impacts.

Switzerland, a landlocked country, is the second biggest shipping power in the world
Switzerland, a landlocked country, is the second biggest shipping power in the world

Cutting-edge technology. Switzerland didn’t invent the cuckoo clock, but it did invent acid. In a whole range of fields, from micromechanics and hardware to life sciences and pharma, Switzerland is world-leading. Its high-tech economy is supported at a foundational/high-risk level by strong government spending on R&D (as a % of GDP it is behind only Korea and Israel, the latter a big military spender) and then a combination of top universities, research centres and high-spending corporations take it from there: Switzerland tops the Global Innovation Index.

Climeworks, an ETH spin-off, commercialized the world’s first carbon capture technology

International connections. Its outward focus is symbolized through its international organizations as well as the annual Davos gathering, but it is supported bottom up through over 40 free trade agreements. International trade accounts for over 120% of GDP and the 2019 Freedom Index ranks Switzerland the fourth most open economy in the world, the most open in Europe. But its internationalism is also down to geography: if you draw a circle around Zurich into neighboring southern Germany and northern Italy, you realize it stands as the pinnacle of the global center for high-end, family-run design and manufacturing — everything from machine tools to Ferraris.

Which other country in the could attract all of the world’s leaders to a small mountain village

A world-renowned brand. Switzerland wraps this adaptive system with a timeless, global reputation for quality epitomized by precision engineering and intricate design; more Zenith watch than cuckoo clock.

Swiss Innovation
On the left, the precise mechanics of a Zenith luxury watch; on the right, Solar Impluse, a solar-powered plane attempting to circumvent the globe

One paradox of Switzerland is that it’s in the heart of Europe, yet not in Europe (the European Union, that is). But a greater paradox is that, though not in the EU, it represents the best model of how the EU should work.

Just as Switzerland should not compare itself to the US, nor should Europe. North America embodies a libertarian model where winners take all and corporations rule supreme.

Similarly, the Chinese model is not the European model, either. The Chinese model is one of planned economic activity. It can deliver fast growth, but at the expense of individual freedoms. Here corporate surveillance gives way to state surveillance, but the surveillance persists.

Technology makes it possible to design complex societies following series of recipes, not inorganic blueprints
Technology makes it possible to design complex societies following series of recipes, not inorganic blueprints

Europe seeks to forge a different path. A Goldilocks model of balanced freedoms and inclusive growth. The challenge is that Europe hasn’t yet found the recipe to make this happen. However, that recipe might well be the Swiss one: business friendly to promote growth, but with the welfare state to lift all boats; a federal model to allow for self-government and pooled sovereignty; a focus on science and lasting value that creates a prosperous middle class; and, an open model attracting the flows on which the next phase of globalization will be built.

While objectively a global success story, there could still be room to improve. A recent report from McKinsey highlighted, for example, that Switzerland is losing attractiveness as a home for MNCs, a key constituent of the ecosystem. Here are three measures that could help.

Unleash the capital. As should be clear, we would not advocate turning Switzerland into some sort plastic replica of Silicon Valley. However, that does not mean that more risk capital wouldn’t be helpful.

Last year, Swiss companies raised over USD1bn in venture financing, a 32% annual growth and the highest amount ever. Moreover, the money went to fund more companies in a more diverse range of sectors than ever before. But USD1bn is low relative to most other developed countries and very low relative to, say, over USD1 trillion that sits in the national Swiss pension pot.

Analog Flows vs Digital Flows
From “Swiss Made” to “Swiss Designed” | Pictured left — daily commuters from France to Geneva (c. 85,000 per day) | Pictured right — next level of globalisation is about digital flows

Furthermore, not only does venture capital have significant economic multiplier effect, but it is also filling the vacuum left by traditional lending which is not adapting to the increasingly intangible nature of modern economies. As such, it would highly beneficial to get dormant Swiss capital moving. This might come naturally from tokenizing illiquid assets like real estate, but it might also require the hand of the state by, for example, creating the conditions for a new financing vehicle to emerge —one that promotes long-term, sustainable investments.

Foster the holding company model. Our view is that this networked organization of the future looks a lot like Ant Financial or Amazon, operating what we call the “holding company model” . This model allows group companies to remain small enough to cater to specific customer demographics and agile enough to respond to market changes. But, the holding company structure leverages growing financial muscle, shares supply side economies of scale, like IT infrastructure, and shares data network effects via APIs. Given the structure of the Swiss economy — as the world capital par excellence of holding companies — as well as the importance of holding companies to attracting data flows, it is vital that Switzerland makes this model as viable as possible before Singapore and others usurp it. Part of this relates regulations on data, but at least as important are rules on tax, especially around how capital flows between entities get treated.

The future is invisible
Which view of the future looks more likely?

Scale the brand. The future is small and invisible. It won’t be the future of flying cars and Fritz Lang’s Metropolis. And for Switzerland, nor could it be. A country of 8 million people, its future always had to be true to brand: high-end and high-quality. But there is a way to make this brand do more and we believe it is by pivoting from “Swiss Made” to “Swiss Designed”, accepting its increasingly intangible asset future, placing manufacturing closer to consumers and scaling its addressable market in the process.

When you transit between terminals at Zurich airport, you are presented with a hologram of Heidi set against a mountain backdrop and accompanied by sounds of nature, yodeling and the Swiss horn. This is the manifestation of a society at ease with itself, the confident projection of a multi-faceted nation that can be at once modern and traditional, high-tech and rural.

The nature of our economies is changing. Our future will be increasingly networked. Switzerland as a complex adaptive system looks set to re-calibrate for this new phase of globalization.

While it might not have invented the cuckoo clock, it’s probably in for another five hundred years of peace and sisterly love.

An immersive image of Heidi in-between train stops at Zurich terminal

The Rise of the Growth Platform

The Rise of the Growth Platform
In pursuit of faster growth, firms must choose what not to do

The Rise of the Growth Platform

by Ben Robinson | May 9, 2019 | 9 minutes read

As Michael Porter once said, 

“The essence of strategy is choosing what not to do.” 

Strategy, as an exercise in weighing up opportunity cost and allocating resources between competing priorities, has always been about making choices and this is truer than ever.

Bruce Henderson, BCG Founder, standing in front of the ubiquitous growth/share matrix

However, traditional strategic theory has not kept up with the digital age. It presents a static view of the world at a time when technology is fundamentally changing the nature of scale, the nature of work and the nature of the firm. Where sustainable competitive advantage used to come from maximizing the scale of production, it now comes via the network effects of connecting consumers with external producers. This move to networked business models raises fresh strategic questions about what not to do, both in terms of what a firm produces, but also in terms of what skills and resources it needs to employ itself. In future, the most successful companies won’t just be those with networked supply chains, but those with networked workforces.

There are many routes to creating a highly profitable business, but the most dependable and sustainable is to seek scale.

For the most successful industrial age companies like Ford or General Electric, this meant systematizing production to maximize output. If a firm could produce a good, say a Model T car, at greater scale than competitors, it could spread its fixed costs over larger volumes, allowing it to charge less while spending more on distribution and marketing. Achieving mass scale was a formidable barrier to entry leading to very high levels of return on capital — without necessarily having high-quality products.

In our networked world, what is inside and outside a company becomes increasingly fungible and it is clear that the bedrock of competitive advantage moves from how to scale internal organizations to how to orchestrate the most valuable ecosystem.

Today, while scale remains critical to achieving sustainable advantage, the nature of that scale is changing.

As platform companies like Uber and Amazon have demonstrated, in the age of networks and ubiquitous computing, achieving scale no longer requires firms to manufacture all of their products and services. Instead, they can source some or all of these products and services from third parties. This allows them to operate at higher scale since supply is elastic and to achieve greater quality since they offer consumers more personalized choice.

The Changing Nature of Scale

These platform models are underpinned by network effects, or demand-side economies of scale, as opposed to supply-side economies of scale. These arise not from maximizing production, but facilitating the interactions that make the platform more valuable for every participant, such as making it quicker and cheaper to get a taxi. And since network effects are not subject to diminishing, but instead increasing, returns to scale, markets move from having a few dominant players to winner-takes-most.

However, strategic theories have not kept up with this change. They continue to draw a sharp distinction between what is internal and external and ask how firms can maximize profits with existing resources or with the resources they could hire, build or buy. But, in our networked world, what is inside and outside a company becomes increasingly fungible and it is clear that the bedrock of competitive advantage moves from how to scale internal organizations to how to orchestrate the most valuable ecosystem.

This shift clearly calls for new strategic tools, but it also calls for a new approach to resourcing, one that reflects the inevitable move away from hierarchies to networks.

When competitive advantage meant maximizing output from a given set of resources, it was important to organize work into standard, repeatable tasks. These tasks were governed by strict processes and overseen by formal hierarchies. The role of senior management was to formulate strategy, the role of the rest of the organization was execute it with little autonomy to think or act for themselves.

Industrial Work Henry Ford Museum Pic

Today, the nature of work is dramatically different, almost diametrically opposed. Ours is an information age. Networked technology has connected us and broken down the siloed and hierarchical flows of information. We have become knowledge workers whose work, where once routine and predictable, is now varied and exceptional. This requires simultaneously new ways of organizing work and also new ways of planning work.

Networked technologies have also dissolved geographical boundaries and unpicked the fixed form of work. We don’t just do different work, we can work from where we want, at the hours we choose and on the tasks we select. In other words, work has become more global, more varied and more liquid.

In its new form, it makes much less sense for work to be organised in hierarchies and we could argue whether it needs to be managed under the umbrella of companies at all. More and more people are choosing to become freelancers and, when you look at their motivation, it owes a lot to self-empowerment. They want to be free from the constraints of companies which not only set their hours, but in their pursuit of productivity, put limits on their learning and self-actualization.

The big shifts in the nature of work and the nature of scale are taking place against a backdrop of accelerated change which has seen the longevity of S&P 500 firms shrink by a factor of 5 since the 1930s.

In response, firms have started to shorten strategic planning horizons, but what is really needed is to split strategy in two. Long term considerations like value proposition, business model and purpose seldom change and should only do so in response to major structural shifts. However, for shorter term considerations, it is important for strategy to become more closely aligned with innovation. That is to say, strategy should pivot from a function that makes well-informed yes/no decisions to one that creates the conditions for maximum agility so to impose as few of these constraints as possible. This means devolving autonomy. This means embracing a culture of experimentation. But, above all, it means embracing the power of networks.

Traditional resourcing decisions were anchored in an old paradigm. They assumed that the best people wanted to work for companies and that companies were the best place to manage them.

Some might argue that firms have already capitalized on the changing nature of the workforce. But outsourcing, nearshoring and offshoring were aimed at exploiting wage arbitrage — doing the same work, but with cheaper workers. There was no attempt to use the networked properties of the internet to revolutionize the way work was done.

Furthermore, the scope of any outsourcing or offshoring decisions was constrained by management theory that said companies’ internal resources must be focused on “core” areas, those with the highest strategic and operational importance. But what constitutes a core activity will change over time. When a firm is starting out, everything is core. It needs to establish its culture, its product/market fit, its business model. But, once these are established, its focus should shift to how to scale as quickly as possible and this means looking to external networks to alleviate the constraints and lower risk. It is only once a firm crosses the chasm and becomes its market’s winner-takes-most that it should it think about bringing back in house some of these activities — either to deepen its moat (like Amazon having its own logistics network) or to reduce its reliance on third-parties (like Dropbox choosing to move off AWS).

And so a new approach is needed. Traditional resourcing decisions were anchored in an old paradigm. They assumed that the best people wanted to work for companies and that companies were the best place to manage them. They were conceived for a world of slower change when the opportunity cost of either being too slow to hire or hiring the wrong people was less material. They assumed all core activities had to be performed by internal resources. And they didn’t take account of the power of network effects.

In the knowledge economy, having the best people matters more than ever and establishing flows of information between independent yet interdependent individuals is critical to ensuring that they grow. As such, management place artificial and damaging impediments to fast growth when they seek to hire all of these people within their company.

Some digitally native platforms have emerged to let companies tap a broader talent pool on an on-demand basis, but these have limitations. In the case of platforms like Uber, they work against the self-empowerment of workers by subjugating them to management by algorithm, making them interchangeable commodities and internalizing all of the network effects. Platforms like Upwork are much better in that they allow for differentiation, empowering workers to be more selective about what they do as well as earn a better return on that effort. But in not sharing risk with the end client, these platforms add much less value than they otherwise could, creating an opening for a new type of business model, what we call the Growth Platform.

Growth Platforms are more capital intensive and take longer to scale than aggregation platforms, but they add much more value and are critical to the next phase of the digital economy. AWS is a growth platform, using the strength of its balance sheet to give firms access to cheap and liquid computing power and sharing network effects through the AWS Marketplace. WeWork is another example, taking the upfront risk on large real estate investment that it parcels out and makes it affordable to smaller firms, while plugging them into its network effects around community and optimized design.

However, up until now, we had not seen a growth platform for creative people and skills. Platforms that pair companies with the right teams of go-to-market people and creatives, to form a partnership that maximizes value and transfers risk from both sides. Companies get immediate access to the best go-to-market talent, but also the infrastructure to make these people perform at their best. The people don’t just get to work with fast-growing companies, but also job security with optionality as well as the ability to constantly learn.

The platform should then supercharge its value-add through network effects. The teams are external to the companies they work for, but in substance they’re very much part of it, adapting to the culture and working towards the same goals. They’re external only as condition of achieving network effects. By operating at increasing scale, these platforms accrue the network effects that enable it to better match not just requirements with people, but people with people, assembling the fluid and interdisciplinary teams to let companies adapt to greater complexity and pace of change. Furthermore, it accumulates the domain expertise, market knowledge and the ecosystem of partners that allows for continual and compounding rates of success.

The Rise of the Growth Platform

Until now, firms had a binary choice: hire all of their own people or outsource. One allowed for control, the other speed. Similarly, the best people had to choose between working for one company or becoming a freelancer. One gave security, the other freedom.

Now, owing to network effects and the changing nature of work, these constraints are disappearing. By plugging into growth platforms, firms achieve control, speed and quality — at scale. And people gain freedom, security and the opportunity to keep learning.

We’re now in a new paradigm, then, where mutual success depends on firms choosing what not to do and workers choosing what to do.

How Marketing Adapts to the Networked Age

How Marketing Adapts to the Networked Age (spoiler: it’s not adtech)

by Ben Robinson | Sep 12, 2018 | 13 minutes read

For father’s day this year, HP released a specially commissioned advert. It depicts a father telling his baby son there’s someone he’d like him to meet. He then introduces the baby to his grandfather for the first time. As the baby seemingly touches his grandfather’s face, the camera zooms out to reveal he’s just looking at a photo. The father says, “I really wish you could have met him.” The ad finishes with the tagline, “Keep memories alive”.

This is a moving ad for sure, but prevailing wisdom tells us it’s a total waste of time. Platforms like Facebook have changed the marketing game, they say. Rather than spending on big-budget, brand-building ads shown to wide audiences, companies need to embrace the new paradigm of perfectly targeted, perfectly measurable micro-messaging. This is, after all, what won Donald Trump the US election, delivered Brexit and launched Warby Parker.

But is that really true? There can be no doubt that digitization is changing marketing. Marketers can gather more data on their customers, which can help them to target more effectively. But, in the digital era, attention is increasingly scarce, making it harder for marketers to land those targeted messages, let alone build engagement. And, some aspects of marketing remain stubbornly true, like customers will pay more for brands with which they feel an emotional connection.

The truth is, in the digital age, marketing craft and strategy matter more, not less. And, as if to underline this point, Facebook — the virtual embodiment of the micro-targeting phenomenon — has just hired Antonio Lucio, the man behind this father’s day ad, to be its global CMO.


In his superb Long Now talk, Paul Saffo eloquently explains how our economy has moved from one centred on maximizing production (to meet a shortage of “needs”) to one centred on financialization and mass marketing (creating and fulfilling “wants”) to one centred on engagement (to hold people’s finite “attention”).

In this context, it is clear that firms can no longer rely on supply-side economies of scale to mass-produce commoditized goods at the lowest possible price nor enter into undifferentiated mass marketing to boost demand. Instead, in the digital age, demand-side economies of scale (network effects) are the golden source of competitive advantage. And while network effects rely on personalization, it does not necessarily follow that the corrective to mass marketing is personalized marketing — or, at least, not in the form the adtech world purports.

Even if you can microtarget the right message to the right person, there is no certainty the ad will get through (as ads have become more prevalent, so have ad blockers); if the message isn’t compelling it probably won’t be viewed; and even it is viewed, it probably won’t be enough to change customer buying behavior (which is essential to grow market share).

The fact is that to trigger network effects and overcome attention scarcity, marketing needs to be highly strategic and highly crafted. It needs to focus on the macro and the micro, the long term and the short term. Practically, this means customer activation needs to take place under the umbrella of brand-building. The reason is that marketing has to do more than getting one customer to buy something, it needs to leverage the power of networked buyers to improve the product and induce others to buy.

Virality & Big ideas

In his autobiography, Confessions of an Advertising Man, David Ogilvy talks of the importance of big ideas. He says,

“Unless your advertising contains a BIG IDEA, it will pass like a ship in the night…Once you decide on the direction of your campaign, play it loud and clear. Don’t compromise. Be strong. Don’t beat around the bush. GO THE WHOLE HOG”

He wrote this in 1963. But is remains truer now than ever. Where once spending enough on TV commercials and newspaper ads could get the blandest advertising onto the collective consumer radar, now the name of the game is rising above the attention deficit and activating the power of network effects. And big ideas are core to this.

Donald Trump Big Idea

To illustrate this, compare the two 2016 presidential campaigns. Donald Trump’s campaign was built on a big idea — that globalization had left millions of Americans feeling left behind. And he went the whole hog — grabbing people’s attention with polarizing messages delivered directly through social media, producing strong emotional responses that mobilized his network of supporters. In contrast, Hilary Clinton led a much more conventional campaign with safer, focus group-driven policies, an anodyne slogan “Better Together” — all of which relied on heavy, TV-based mass marketing for reach. And, as much as we like to point the finger at micro-targeting, it was a big idea that won Trump the election.

The best brand campaigns are also built on big ideas. Nike is exactly the kind of brand that adtech should have theoretically seen off, as micro-targeted new shoe brands picked off its heterogeneous consumer base. But it continues to flourish in large part thanks to its willingness to take risks and embrace big ideas (look at its decision to hire Colin Kaepernick). With Nike+ Running, a groundbreaking campaign kicked off in 2006, its BIG IDEA was to realize that sport is more fun with others. But it went beyond just an advertising campaign. It used technology — allowing people to record, track and share their runs — and wrapped it with the experiential layer of taking part in races all of the world — to unlock the power of connected consumers and create an engaged global community.

Nike’s latest “Just Do It” campaign shows it is not afraid of stirring controversy or going the whole hog

And we shouldn’t underestimate the impact of content marketing to build brand and create an engaged community — all the more so when based on big ideas. Andreessen Horowitz, the VC firm, is a great example. Its content isn’t just informative, but frequently leads and shapes opinion on major new trends – just consider Marc Andreesen’s seminal article “Why Software is Eating the World”. As such, the content isn’t just consumed and valued, but sees people actively engage with and share it, turning them into the distribution engine. In turn, this generates massive long term brand equity, which while not directly or immediately convertible into revenue, creates the bedrock for mobilization and monetization (in AH’s case, raising money, attracting the best people and finding the best portfolio companies).

False precision and displaced spend

Adtech is to the marketing profession what mathematics is to the economics profession — a bogus attempt to make something precise that inherently isn’t.

For a thorough and entertaining debunking of many of the adtech industry claims, I recommend following Mark Ritson on twitter and reading his weekly column. Suffice to say here that, beyond the issues of reaching the audience, there are problems in measuring the impact, not least separating causation and correlation. For example, if someone clicks on an ad for a Porsche and then buys one, the data would indicate causation. But the likelihood is that the person was already intending to buy one since, for most, this is not an impulse buy induced by an online ad. And so we must be cautious not to overstate the effectiveness of online targeted ads — noting that many big companies have seen little impact on sales from reducing their spend. As Marc Pritchard, P&G’s chief brand officer, said in a WSJ interview,

“As we all chased the Holy Grail of digital, self-included, we were relinquishing too much control — blinded by shiny objects, overwhelmed by big data, and ceding power to algorithms.”

Another inconvenient fact of paid advertising is that while it may have once been a relatively inexpensive customer acquisition channel, this is no longer the case. This excellent Inc article on the rise of Direct-To-Consumer (DTC) startups makes the point that the price of advertising on Facebook is increasing fast — by nearly 200% in the first six months of 2017 alone. To quote the article:

“ Comcast’s Gulati has a phrase for this phenomenon: “CAC is the new rent.” In other words, for companies reliant on paid marketing, their digital customer acquisition cost (CAC) is a lot like paying for brick-and-mortar stores in the old model, or selling wholesale. Essentially, this undermines one of the most basic precepts of the DTC movement, that these companies are cutting out the middleman and therefore can afford to charge much less for higher-quality goods.”

The analogy with the wholesale sales model is especially interesting since, if social networks become the shopfronts for many companies, these companies lose the direct customer relationship. This not only locks them out of many demand side network effects, but also — like their indirect-to-consumer forbears — means that brand-building becomes critical as a means of creating loyalty and repeat purchases.

Wastage works

If you fly into Geneva airport, you’re likely to see one of several giant billboard advertisements for Nespresso featuring George Clooney.

According to proponents of adtech, these ads are wasteful — they’re expensive, there’s no way to track their effectiveness and they’re reaching a much broader audience than the narrow segment that a brand should be targeting.

This is wrong, for several reasons.

First, there is a signalling effect. George Clooney is expensive to hire. George Clooney is well known and respected. Thus, we infer Nespresso is a high quality product.

Second, there is a value in broad targeting. As Byron Sharp illustrates in his book, “How Brands Grow”, buying behaviors are much more skewed than we may think between heavy buyers and the long tail of light buyers, such that there is more scope to get a large number of light buyers to buy a bit more than to get heavy buyers to increase spend. As he puts it, “sales growth won’t come from relentlessly targeting a particular segment of a brand’s buyers. Yet this targeting fantasy continues to appear in marketing plans.”

Lastly, brand-building through above-the-line promotion remains important to create an emotional connection. This emotional connection is born of common social and cultural associations which, by extension, need to be created at a collective, not solely individual, level. It is triggered through distinct imagery and carefully crafted copy — since making us think, laugh or cry is key to making a brand memorable. And it is this collective emotional connection which translates into higher loyalty and the ability to charge more.

Emotions and pricing

Indeed, one could make the case that brands as a navigational device are even more important in the attention economy, since we are now so overloaded with choices and content. However, that said, the ability of advertising alone to create a reliable signal of quality, to change buying behaviors and to create an emotional connection is not what it was. This is a great quote from Jeff Bezos in an interview with Charlie Rose,

“Before you could win with a mediocre product, if you were a good enough marketer. That is getting harder to do. The balance of power is shifting toward consumers and away from companies…the individual is empowered… The right way to respond to this if you are a company is to put the vast majority of your energy, attention and dollars into building a great product or service…If I build a great product or service, my customers will tell each other.”

It’s difficult to argue that product isn’t more important than in the past, nor that a great product with mediocre marketing wouldn’t win most of the time over a mediocre product with great marketing. However, even if we accept the power of advertising might not be as strong as before, this doesn’t necessarily change the importance of marketing overall. Instead, it places more importance on doing marketing well — what you want is a great product and great marketing — and it changes the relative importance of promotion vis-à-vis the rest of the marketing mix.

Hidden marketing

Such emphasis is given to promotion that we sometimes forget that marketing has three other P’s — price, product and place. And such is the emphasis given to promotion that companies that market in other ways — like Tesla — often claim not to spend anything on marketing.

But all companies spend money on marketing, even if it isn’t recorded in the sales and marketing expense line. Every time Elon Musk tweets about Tesla, this is marketing but isn’t recorded as such. And Tesla — like Zara — does most of its marketing by dint of its stores. Strategically located in the most prestigious parts of town with giant windows showing off their prized products, why would either company invest in billboards? Even Brandless, the SoftBank-backed retailer which as the name suggests has jettisoned branding (but which is in itself a conspicuous act of branding), is engaging in lots of marketing — from price promotions to product curation.

Zara store at 666 Fifth Avenue in NYC: a building which was once the most expensive ever sold in Manhattan

The fact is that so much of marketing gets overlooked because it is practically invisible.

The customer feedback loops it provides get translated ever more seamlessly into product design, development and placement — think of Amazon’s personalized recommendation engine.

But it is in customer acquisition, notably pricing, that marketing is maybe most underappreciated. Since customers today represent not just consumers, but an essential part of the product itself, customer acquisition and retention is both critically important and highly strategic. Many of the tools marketers use today, traditional ones like email campaigns or new-fangled ones like paid online ads, don’t cut it. Instead, new levers are needed to get the flywheel of network effects started. Here are some examples:

Giving the product for free. One obvious way to this is to charge advertisers instead. But, not only have advertisers been oversold, but this model creates a conflict of interest between customer and advertiser. There are better alternatives, such as freemium pricing. If you have a great product, then letting customers experience it for free is an excellent way to spread the word. And once a customer is hooked on the product (and since the relationship is direct), the firm can upsell them premium features (e.g. Dropbox) or give them access to a premium service (e.g Spotify).

A Facebook advertisement
A Facebook advertisement in a non-digital medium apologizing for its use of customer data (illustrating the conflict between advertiser and consumer)

Shared-value transactions. Perhaps a better model still than freemium might be what Eric Feng calls shared-value transactions. This a pricing model wherein a firm gets almost all revenues from the heaviest users of its service and uses this revenue to improve the product for all. This in turn attracts new users, most of whom will pay little or nothing, but some of whom will become heavy, very profitable customers. It is superior to ad-based marketing, where increasing revenues involves hitting all consumers with more advertising (i.e. making no differentiation between customer types) and gives an advantage over many subscription-based models, where all users — again regardless of level of usage — tend to pay the same amount.

Spotify is the king of Freemium — its conversion rate is almost 27%

Giving money away. Cruder, but still effective are referral fees. Paypal built its network of customers in part thanks to giving money to people for referring their friends (which were paid into the recipient’s PayPal account). Dropbox does the same — only giving referrers extra storage as compensation

Making customers investors. One way to build initial excitement for a product launch and acquire an early group of engaged customers is to give them to chance to invest through crowdfunding platforms like Seedrs. This is what challenger banks like Revolut have done. Another way, still nascent, will be to issue utility tokens to simultaneously raise money and build a customer base. By meshing together investing and consumption, tokens provide a way to reward financially the users on which network effects are built.

The future

It may be that one day, we have surrendered enough data about ourselves for a bot to be able to make better choices than we can. But up until that happens, marketing remains critical to attracting and engaging consumers.

Marketing has become harder. Many of the marketer’s tools have been blunted. The media over which to reach customers have both changed and proliferated, putting attention at a premium. But this calls for greater craft and more strategy, not a search for snake oil.

Marketing has also become more important. Marketing today is more than just about finding a buyer for a service. It is about activating the power of networked consumers, which form a central part of the service creation, promotion and distribution.

To be effective, marketing needs to do more than translate consumer insights into more accurate targeting. It needs to work above and below the line -building brand equity and mobilizing consumers – as well as making full use of the marketing mix. And over time it is likely to rely more on behavioral science, a move from objective analytics to a rational understanding of irrationality and what drives an emotive response to capture attention — building on Tony Schwartz’s idea of “the Responsive Chord”.

But, for now, marketing is definitely not dead.

Firms need Business Model change, not Blockchain

Firms need business model change, not blockchain

Firms need Business Model Change, not Blockchain

by Ben Robinson | Jun 1, 2018 | 13 minutes read

When Jimmy Song, a venture partner at Blockchain Capital, took to the stage at Consensus two weeks ago (wearing a black cowboy hat), he launched an attack on the blockchain-is-the-answer-to-everything mentality. He said,

“When you have a technology in search of a use, you end up with the crap that we see out there in the enterprise today.”

Jimmy Song
Jimmy Song

Jimmy was clearly trying to be provocative and burst the bubble of blockchain fanatics, but he has a point. It’s not so much about blockchain per se (although this may be where the worst offences are committed) but about the focus on technology in general. Every day we are bombarded with articles about the need to digitize or about how [Blockchain/AI/APIs/Cloud/Mobile/IoT] will transform or disrupt such and such an industry. But we forget that technology in the absence of new business models never changed anything.

UBER Business Model, by Tim O'Reilly
UBER Business Model, by Tim O'Reilly

It wasn’t the internet that transformed retail or music. It wasn’t the smartphone that created Uber. Instead, it took business model change which exploited new technologies. In retail, it was the Amazon business model of one-click checkout, marketplace and next-day delivery. In music, it was the iTunes model of unbundling music to let us buy individual songs and then the Spotify rebundling model of all-you-can-listen streaming subscription service. And Uber didn’t just use the smartphone to let people order cabs (as many of the incumbents did), but instead uses the power of GPS to allow anyone with a car to become a taxi driver, transforming supply in the course of transforming user convenience and experience.

And so in banking we can safely predict that it won’t be blockchain or APIs or AI that transform the industry. Instead, it will be new business models empowered by those technologies.

Implementing technology without a clear plan risks making matters worse

In fact, we could probably go further and say that implementing new technologies without a clear idea of the future business model may make matters worse because it could well entrench existing practices.

The reason for this is that these new technologies will be implemented in support of existing business models rather than in pursuit of new ones. This means — as we have seen so often in banking — that digital technologies are used to digitize analogue products, rather than reinventing them for the digital age. But, it means more importantly that these technologies will be used to double-down on scale.

Economies of Scale Illustrated

The industrial economy was all about scale. Once a company had come up with a winning product, the challenge was to exploit economies of scale as fully as possible. This allowed unit costs to be minimized and allowed firms to undercut rivals, seeing them gain more market share and scale and thereby locking in their leadership position. So all investments were aimed at maximizing scale — mass marketing, mass production, mass distribution — and business were organized into centralized, hierarchical structures to make this possible.

But these investments in scale in the digital age are quickly moving from sources of competitive advantage to sources of competitive disadvantage.

Technology and platforms have neutralized scale advantages

In their recent book, , Hemant Taneja and Kevin Maney talk about how the technologies of cloud and AI have turned scale economies on their head.

In the world of cloud computing, IT resources are available cheaply to everyone meaning that — other than for the platform providers like Microsoft— scale doesn’t matter. A business can rent as little or as much IT as it needs, conferring little scale advantage in running massive operations.

But it’s not just IT resources, the same model is being applied everywhere. Take human resources, it is becoming increasingly easy to contract the people a company needs at the time they need them through platforms such as Malt and through a new breed of companies like  and .

In economic terms, technology has lowered the minimum efficient scale of production to a point that is within the reach of most SMEs. And with a monolithic business structure, diseconomies of scale kick in sooner and are more material.

Artificial Intelligence is also having a profound impact on scale. If new technologies and platforms make it possible to manufacture profitably without scale, AI is making it possible to know what each and every customer wants — so that product and service can be tailored to everyone.

While the slight flaw in the unscaled argument is that more scale leads to more data and more data leads to better AI, it is nonetheless the case that any company offering undifferentiated products at scale will soon lose market share and scale. And so we see white space for new kinds of business models, where firms — or platforms — are able to take advantage of these new technologies to offer mass customization at scale.

The incumbents’ challenge

The incumbents challenge is, therefore, how to move away from this heritage of scale. This is likely a bigger challenge than it seems. Many companies in the industrial age missed shifts in consumer trends, but because of scale they could in many cases afford to catch up — copying rivals, buying rivals, etc.

In this digital age, the scaled business model is likely to lead to the double whammy of failing to spot new trends and the impossibility of catching up. Moreover, scale is so deeply embedded — across company structures, performance metrics, remuneration, processes, employee skillsets, cultures — that it will be so difficult for incumbents to make the transition.

Number of investments in tech companies by country — source Atomico
Number of investments in tech companies by country — source Atomico

And it’s not just an issue facing companies. Take Germany, for instance. For so long, its industrial sector has been admired all over the world for consistently high quality engineering. But, the German economy is struggling to make the transition to the unscaled, digital world. It doesn’t (yet) have a  from which the new unscaled models are emerging and the .

But there is hope. We do see many incumbent companies, including in the banking industry, adopting new, unscaled business models for the digital age.

New banking business models for the digital age

In many ways, the following section is an update  ago looking at how technology and new regulations, particularly PSD 2, were likely to lead to new business models. Where back then we identified 4 business models, now we identify 5 (but now fully discount the universal banking model as a relic of the industrial age). And where back then we framed the choices around asset intensity and profitability, we now frame the choices around the size of the demographic a firm wishes to serve and the number of products it offers to this demographic (although profitability is likely to improve in correlation with these factors).

Let us consider each in turn.

The unbundled start-up

This is the model that most B2C fintech companies have pursued until now. They spot a niche, which could one of: a product that wasn’t previously offered (e.g Coinbase), a demographic that is un- or underserved (e.g Lending Club), a much better experience (with likely cheaper pricing), combining tech and design thinking (e.g Transferwise) or all of the above(e.g. WealthFront).

It is very much the embodiment of an unscaled model: using cloud infrastructure to operate at low volumes and using AI to serve small segments of the market. However, given it is both targeting a niche and targeting the consumer directly, it is often difficult to make this model profitable. The low infrastructure costs are more than offset by high customer acquisition costs which, because these tend to be single product companies, cannot be spread over many revenue lines. There are exceptions, of course, where the regulatory costs are low and the market is large (e.g WorldRemit), where there is a virality in the product design that lowers acquisition costs (e.g Revolut) or where the product solves a big issue in a big market such that it becomes a very large company (PayPal, M-Pesa, Stripe).

The unbundled startup
The unbundled startup

But the much more likely outcome is that successful unbundled start-ups start to bundle multiple products under the same brand.

The rebundled start-up

Once a start-up has found a strong product/market fit, it is logical for it to offer multiple products in order to boost its return on capital by cross-selling and upselling to its existing clients. It effectively moves from a single, unbundled product offering to rebundling a full banking service over time. However, it is different from a traditional universal banking model in a number of ways, such as the fact that it is digitally native but more importantly because it remains focused on serving the same demographic. In that sense, it doesn’t engage in mass marketing and production, but sticks to a narrow target market. Were it to begin to offer all products to all customers, it then risks becoming the victim of unbundling itself.

Unbundle to Rebundle
Unbundle to re-bundle strategy

Examples of successful unblundled to rebundled start-ups include Zopa, Transferwise and Revolut.

The platform model

The platform model is somewhat of an anomaly in this list since it is essentially a scale play. However, it is likely to be an enduring model since:

1/ it is underpinned by strong network effects in a way that the universal banking model isn’t;

2/ it is often executed as part of an unscaled holding company strategy (see later); and,

3/ it is offered in the service of (and helps to make sustainable) the model of unbundled start-ups.

The platform model is simple. Banks rent out their back office as a service to others. For the unbundled start-ups who would be clients, it offers the advantage of not having to undertake a bunch of low value-added regulation and IT activities and it helps them to go beyond just renting IT infrastructure to renting IT applications and compliance. For the banks, it helps them to spread the largely fixed costs of IT and compliance over much larger volumes, improving economics.

Infrastructure Play
Infrastructure Play

The challenge, as pointed out in the last blog, is that this is a difficult model to scale across borders, limiting its profitability potential and meaning that there will be likely only one or two platform players per country/geo.

Examples of this model we have seen so far include Wirecard, Railsbank, Solaris and Bancorp. And it is no surprise that they are cropping up in the largest banking markets first where potential for scale economies is greatest.

The aggregator model

The aggregator model is where a firm uses its grip over distribution to introduce the consumer to the right unbundled services. Effectively it uses AI and machine learning to understand the customer’s financial affairs and preferences and to anticipate their needs so that it can make the right service recommendations at the right time. With the introduction of PSD 2 — and similar regulation across the world — this model becomes easier to operate since it forces banks to share customer data. And, theoretically, it becomes possible to operate this model without engaging in any product manufacturing or without having a banking licence or any compliance team — as firms like Centralway Numbrs are trying to implement.

The Aggregator Model
The Aggregator Model

Nonetheless, our view — consistent with the blog from two years ago — is that this model will be thin, open but vertically integrated. By this we mean that aggregators will work with many different unbundled start-ups, but because of the nature of banking, they will likely manufacture some products — like current accounts that require a banking licence. And because of the need to deliver exceptional customer experience, they will end up having to become more vertically integrated. We , such as with Amazon and Netflix, and now we observe the same thing happening in banking. When unbundled fintech start-ups rebundle, they tend to become more vertically integrated — witness Transferwise’s move off the Currency Cloud platform or N26’s move off Wirecard.

Vertical Bank Business Model
Vertically-integrated, thin digital bank

And so it is not a surprise that the aggregator models we are starting to observe in banking are thin and vertically integrated, such as M-Shwari and Starling Bank.

However, there are a couple of potential issues with the aggregator model. The first might arise from regulation. Will regulators allow banks that offer own-labelled services to aggregate services from third-parties and trust them to do so completely impartially? Especially given the marked tendency for aggregators to move from . Moreover, there may be a business model challenge in that, as , models like Starling’s rely on third-parties while seeking to internalize the network effects, especially around data.

Aggregators vs Platforms
Aggregators vs Platforms adaption of Ben Thompson's diagram

So, while we continue to believe that this is a sound model, aggregators of this type will need to look to empower the ecosystem by externalizing network effects and may seek to use arms-length intermediaries, like Bud, to avoid potential pitfalls around regulation.

And, where these potential issues are not addressed, aggregators leave themselves open to the threat from rebundled start-ups and from holding company models.

The Holding Company Model

The holding company model attempts to replicate the universal banking model — or conglomerate model in other industries — for the unscaled world and in a way that confers competitive advantage on the subsidiaries, especially by dint of network effects.

There is probably no “standard” for the holding company model. Berkshire Hathway is a great example of how a holding company structure can create competitive advantage across the group companies, in its case by using the cashflows and very low cost of capital of its insurance business to provide the cheap cash for investing.

Amazon is another great model to study and probably more relevant for banking. Jeff Bezos made a decision in 2002 to standardize the way information is shared across Amazon using APIs. It was a brilliant move in how to achieve control at scale. Essentially, the inputs and the outputs of every team were measurable in real time, such that their performance was instantly calculable and all other teams would get the information needed to conduct their work without bottlenecks, but it still allowed the teams autonomy in how to execute. The upside of this API model, so well documented in this , was manifold:

  • it allowed the different teams to operate autonomously so that that those business could be opened up to work with third-parties (as happened with AWS)
  • It allowed each unit to be kept focused on its own KPIs, which essentially means that they are forced to remain close to customer trends. As , the genius of Amazon’s customer obsession is that it forces every part of the business to innovate at the same time as making it practically impossible to overshoot consumer demands.
  • It critically allows every business unit to stay focused on its niche (essentially an unscaled model) but allowing for scale at a group level (e.g IT resources, distribution and brand), positive working capital flows, and the exploitation (internalization) of network effects across group companies.

This is what makes Amazon such a formidable company. It has figured out how to make the conglomerate model work in the digital age — through a holding company structure. And, furthemore, in its digital form, it overcomes one of the key shortcomings of its industrial age predecessor — it can achieve increasing returns to scale thanks network effects.

In the financial services space, the best example of this holding company structure is Ant Financial. Where Amazon has figured out how to adapt the conglomerate model for the digital age, Ant Financial has figured out how to recreate the universal banking model for an unscaled world. Its hub and spoke model sees the group leverage data, brand and distribution while the subsidiaries remain narrowly enough focused — on unsecured lending, investing, money market funds — to remain nimble and adaptive in the face of changing technologies and customer trends.

Ant Financial Holding Company
Ant Financial's Business Model as a Holding Company

The Holding Company as a model for reinvention

We see a strong trend in banking for incumbents to launch new digital banks. The examples abound, such as BNP Paribas’ Hello Bank. While this model to reinvention is in many ways sound — it allows these banks to transplant customers and trust into a new digitally native version of themselves — it risks creating another universal banking model, albeit one built on digital foundations. A better way of going about reinvention would seem to be a holding company model. This might be built on a Berkshire Hathaway model, as seems to be the case with Equitable Bank’s creation of its , to create a sticky, low cost source of funds for its lending business. Or, probably more likely, it would be an Ant Financial model of having individual subsidiaries target different business lines, which is the approach that Bank Leumi seems to be taking with Pepper Bank.

Pepper Bank, by Leumi


There is a clear danger that with the constant hype around technology, banks miss the need to redefine their business models before embarking on major technology renovation. In fact, technology renovation in the absence of business model renovation may well make things worse because it would entrench existing business models based on selling undifferentiated products at the greatest possible level of scale. The digital age calls for something else — products, many of which will be new, targeted on specific demographics, made possible now thanks to technology change. In this blog, we have presented 5 models which would work in this new paradigm, of which the holding company offers perhaps the best route to success — especially for incumbent organizations looking to reinvent themselves.