Banking-as-a-Service (BaaS): Navigating the Maze

The worlds of Open Banking, SaaS and BaaS may actually be converging – with several highly lucrative winner-take-most platforms likely to emerge.

I came across this impressive twitter thread a few weeks ago from Nicolas Benady, who runs a new Banking-as-a-Service (BaaS) platform called Swan. It explains why BaaS, Core Banking and Open Banking solutions are totally different.

But, to our mind, some of the confusion is justified. While today there is a big difference between Open Banking, BaaS and B2B SaaS systems, like core banking solutions, a lot of the differences are dissolving. Further, not all BaaS platforms are created equal and, as the example of Sequoia walking away from its investment in Finix demonstrates, the BaaS space is also quite complex.

What follows is our schematic for understanding the differences between BaaS models, as well as a discussion about the battle to dominate the BaaS space over time, which we think is likely to involve both open banking and B2B SaaS platforms.

PaaS vs BaaS

Platform-as-a-service (PaaS) is about providing a platform on which others can simply and cheaply build a new proposition. Banking-as-a-Service (BaaS) is about providing a platform that allows (normally non-financial) businesses to embed banking into their existing proposition, like point-of-sale credit.

However, this is a somewhat false dichotomy in that many of the platforms actually do both. They can enable companies to build a new proposition on top of the platform and offer an already-assembled service to be embedded directly into an existing proposition; the distinctions can be quite nuanced.

The differences in BaaS

Instead, we perceive the main points of differentiation in BaaS to be the extent to which services are vertically integrated vs modular and the extent to which they are out-of-the-box vs customizable.

 

Out-of-the-box BaaS

Let’s start in the top left quadrant.

Out-of-the-box BaaS models are fully-formed, end-to-end services that a brand can embed into their propositions, typically with little customization. They tend to work on the basis of a variable, revenue-sharing model. And they are generally best suited to smaller businesses that have few internal developers (or don’t want the hassle of development) and do not want to be regulated in any way: that is, those business willing to forgo customization (and higher margins) for ease of use.

An example in the payments space would be Stripe, which allows any digital business to accept payments through a simple API integration. Stripe integrations are largely the same – the same layouts, with the same fields. And Stripe charges a variable fee for this service, 2.9% and $0.30 “per successful card charge”.

An example from the wealth management space would be DriveWealth, which we profile in our recent report, which provides an end-to-end brokerage service that brands can embed into their proposition on a revenue-sharing basis.

Vertically-integrated, customizable BaaS platforms

In contrast to out-of-the-box BaaS services are those that are still vertically integrated (in that a single platform provides the end-to-end service) but they allow for much higher levels of customization. This is where the line gets slightly blurred between PaaS and BaaS in that with many of these platforms it would be possible to build both a standalone fintech proposition as well as highly customized user journey with distinctive look and feel within an existing proposition.

There are broadly two models for vertically-integrated, customizable BaaS.

Vertically-integrated, customizable BaaS offerings from incumbent banks

The first are from incumbent universal banks, such as Goldman Sachs (Marcus), BBVA (Open Platform) and Standard Chartered (Nexus). This is a logical play for these banks, since it allows them to spread costs and grow volume. Much of a bank’s cost base – software, infrastructure costs, compliance – is relatively fixed and, therefore, generating a higher volume of business through indirect channels helps to spread these costs and improve cost/income ratios. Furthermore, new customer coming via indirect channels should be acquired at much lower cost since they are existing customers of the acquiring platform. However, what is less clear is the extent to which the better unit economics will be shared between the brands distributing banking services and banks manufacturing them (especially over the medium to long term).

Newer entrants offering a narrower range of services

The other players–more numerous for now–operating in this area are relative new entrants, regulated digital platforms or banks which offer generally a much smaller range of services than could be offered by a universal bank. These players, by dint of being platforms and having little or no consumer-facing activities, are not particularly well known, such as Solarisbank in Germany or Cross River bank in the US. There are also examples solely focused on specific segments. One example, profiled in our wealth management report, is WealthKernel, which provides a full-end-to-end stack for businesses to start wealth management businesses, a kind of Shopify for wealth managers.

Finix is a kind of hybrid model

In the matrix above, we have positioned Finix between the regulated and the unregulated spaces. This is deliberate since, although Finix is regulated under the Payment Card Industry Data Security Standard, it is not regulated as a payments company. The quirk here is that Finix basically provides all of the capabilities for its customers to become payments facilitators, for which they need to be regulated, and in doing so it changes the economics for those customers. Instead of paying a per-transaction fee, customers pay a subscription to Finix meaning that a much greater proportion of the income from payments accrues to customers over time. In a strict sense, then, Finix is not a direct competitor to Stripe. But, it is an alternative to Stripe, which is why Sequoia felt the need to walk away from its investment.

 

Modular and customizable BaaS platform

 The other main type of BaaS systems are those that are both customizable and modular. By that, we mean that they afford their users a lot of flexibility in the implementation of the services, which can be easily extended, but also that it is not a single provider providing the service.

These types of BaaS platforms are a partnership between a service provider, which typically provides the service configuration and orchestration as well as customer and risk management, and a regulated institution, normally a bank, which provides compliance, balance sheet, settlement, custody and other regulated services.  

An example in the wealth management industry is Bambu (profiled and evaluated in our wealth management report) with its Bambu GO platform for the US market, which allows new entrants to launch a robo advisory service or to embed one into an existing offering, using Apex Clearing to provide the custody and brokerage.

The business rationale for these partnerships is around specialization and flexibility. The partner banks can focus on banking manufacturing while the partner can focus on the distribution of these services, which requires these services to be served up in context-aware user journeys. Moreover, the BaaS provider can work with different partner banks in different countries to overcome the geographical constraints of these partner banks, which tend to be limited to operating in single countries or jurisdictions.

This kind of partnership with BaaS providers can be very lucrative for the banks involved, but it is harder to see how this can be a viable model for incumbents. Owing to the scale economies and lower CAC, small credit unions and community banks in the US that have developed these partnerships are earning elevated returns on equity. Celtic Bank, for instance, a small commercial bank based in Salt Lake City, earned an RoE of 37% in the quarter to end of September 2020 (source: US Bank Locations).

The problem with the incumbent banks is that they have very different cost bases compared to these small credit unions or digital banks. The idea of distributing services wholesale through these intermediaries is difficult to imagine given the sunk distribution costs, such as a branch network, that most banks have as well as the high costs from legacy technology. Distributing a subset of services through these intermediaries might be more viable to grow subscale or non-strategic business lines through an indirect channel, such as Goldman partnering with Stripe Treasury. But, in the main, we expect incumbents to move slowly on BaaS and when they do to elect, at least initially, a vertically integrated model – because they are not yet ready to decide which services are core and non-core.

 

BaaS operating platforms: the value of linking many to many

A change that we foresee is for the BaaS systems to become less static and more networked over time. In short, we expect the modular BaaS providers to evolve into providing a many-to-many gateway. By that we mean that rather than partnering with a single or a very small number of banks on the supply side, which is generally the case at the moment, we anticipate that these players will work with both many suppliers and many brands on the demand side, in a model more analogous to a software operating system. We already see the early signs of this. Synapse for example offers a service to connect brands with their existing bank provider, while Bond is even closer still seeking to use AI to make the bridge between brands and bank services. Stripe Treasury is also interesting, both because for this service Stripe moves from top left to bottom right on the matrix above, but also because its go-to-market is to partner with other platforms like Shopify, making it the platform of platforms.

This model of the BaaS operating system is, in our view, the most exciting area in the whole of the fintech landscape – the pinch point where the most value is likely to accumulate.

The emerging competitive landscape for BaaS operating systems

However, it is a not a foregone conclusion that the most successful BaaS operating systems will stem from the BaaS field. B2B and B2B2C are becoming increasingly blurred and this opens up the possibility for B2B systems of intelligence to also emerge as BaaS operating systems contenders.

Consider the graphic below, which relates to the wealth management market. On the left-hand side, we illustrate a B2B SaaS system of intelligence, in this case additiv for wealth management. additiv provides wealth managers with software that enables them to manage the customer relationship independently of the core banking system and independently of distribution channels, but the software is mostly used by single institutions in a vertically integrated business. Elinvar has the same proposition as additiv, except it also offers some business processing outsourcing. Next is WealthKernel, mentioned earlier, an end-to-end vertically integrated BaaS platform. Then, we have an example of a modular, BaaS platform: Bambu GO. The interesting point to note here is that Bambu’s main business is being a SaaS provider to regulated firms, such as Standard Chartered, providing its solution in the service of an integrated business model, just as additiv does. The offering provided by Bambu doesn’t change, just the end customer (now not a regulated entity, but fintech or consumer brand) and the provider of regulated services (a partner, rather than the end customer).

Therefore, it follows that any B2B system of intelligence can easily pivot through partnerships to become a modular BaaS provider and, from there, to become a BaaS operating system. In fact, the move to a BaaS operating system is, in many ways, easier since these systems of intelligence are being used by many banks in a SaaS setup, meaning there are already several supply-side options for the brands on the demand side. Bambu has already started down this route with Bambu GO, but others in the wealth management field, such as Elinvar and additiv, are moving in this direction, too.

 

The other players that are potentially in the running for the BaaS operating system are the Open Banking platforms, like Plaid, Bud, Yolt, and Tink. These platforms provide the capabilities for connecting customer data with other services, where the customer provides the permission for this to happen. For example, if you want to connect your accounting system to your bank account, an open banking platform will provide the connectivity to make that happen. However, most platforms have realized the potential to go further, moving beyond just connecting banks and services and towards fulfilling user journeys, such as switching a utility provider. By connecting banks and brands, they are also in a position to pivot and become BaaS operating systems.

So, rather than being completely different, the worlds of open banking, SaaS and BaaS may actually be converging – with several highly lucrative winner-take-most platforms likely to emerge.

This blog was based on an excerpt from our recent “Digital Age Wealth Management” report, which you can access and purchase through this link.

The Market Map |
Some surprising results

We get very different outcomes compared to conventional evaluation studies. A lot of smaller vendors rise up the rankings thanks to advanced technology and flexible architectures. And for incumbent software vendors, it clearly distinguishes those that have kept up with technology change and those that haven’t.

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A new approach to enterprise software analysis (and why we launched The Market Map)

Introducing The Market Map for Wealth Management Software Solutions, a new approach to evaluate technology solutions in the digital age.

When was the last time you went into a branch (even pre-COVID)? Or the last time you paid for something in cash?

Financial services have been gradually digitizing for years. It started with digital distribution of existing services. And then the services and service providers started to change. Who still uses their high street bank for FX payments or for buying stocks?

We’re now moving into a new phase where, now digitized, financial products are going to be more and more embedded into other services – either tightly, like payments within the Uber app, or just bundled together for convenience like in super apps.

But while the industry is undergoing this paradigm shift, the way we evaluate the enterprise software on which it runs remains unchanged.

At best, the conventional analysis is becoming increasingly irrelevant but, at worse, it is steering decision makers towards systems that are not fit to address their changing needs.

So we’ve decided to shake things up. We’ve written a report explaining in detail how wealth management is changing as a result of digitization and, as part of the report, we launch The Market Map, a new criteria for evaluating software solutions in the digital age.

Here’s why we think enterprise software analysis is broken and a look at how a new approach would produce radically better results.

From supply to demand

When we talk about digitization, we invariably focus on technology itself, specifically how far it has advanced. But its more significant — and often overlooked — consequence is that business models are also changing.

If the industrial revolution solved the problem of supply and made economies of scale possible, the digital revolution is turning the supply-demand equation on its head.

Supply is no longer an issue because, most of the time, sourcing, distribution, or both can be digitized. Even where creating economies of scale isn’t feasible, the proliferation of as-a-service tools means you can borrow them at a reasonable cost.

Instead, it’s now demand — or, more specifically, customers’ attention span — that’s in short supply.

This shift from supply-side to demand-side economies of scale has far-reaching implications. Mass consumerism is falling out of favour because, with supply abundant, customers don’t have to settle for a limited selection of highly standardized products or services in exchange for affordability.

On the contrary, they now expect to be treated as individuals, and will vote with their feet if their needs aren’t met.

A vicious cycle

If individual consumers are embracing their newfound power and demanding more tailored experiences, enterprise clients — particularly in the financial services space — are yet to smell the (small batch roast) coffee.

Digital transformation has been one of the financial services industry’s top priorities for several years. And Covid-19 has only made the need for it more pressing. But the business models underpinning most firms have remained largely unchanged, in that they’re still predicated on mass production.

The result is that firms tend to approach vendor selection in an anachronistic way. And the system is self-perpetuating.

Procurement teams’ requirements are rooted in supply-side economies of scale.  At the same time, the businesses who evaluate enterprise software need to appeal to the enterprise buyer. So, their reports give importance to the criteria procurement teams look for, even though these aren’t necessarily the criteria that matter.

And the cycle continues.

Size doesn’t always matter

The way enterprise software analysis is currently done has two major flaws.

For starters, many reports are directly or indirectly pay to play. Right off the bat, this creates an economic barrier for smaller vendors.

More significantly, even where the economic hurdle can be overcome, vendors may still be let down by the methodology. This is because reports tend to rank on criteria that make sense from a supply side perspective rather than the demand side.

Consider size.

On paper, staff numbers, office locations, and annual revenue are important, because they’re the mark of a financially viable, trustworthy company. In a tightly regulated industry where the stakes are sky high, it’s understandable that firms would prefer an established firm over an untested upstart.

But when it comes to the actual job of launching a new business model, do these numbers matter?

Probably not.

Take revenue. In an on-premises or single instance scenario, whether a vendor makes $5 million or $500 million a year has little significance aside from, possibly, indicating they have more resources to put into R&D.

But bigger R&D budgets and capabilities aren’t as beneficial as you might think. On-premises and single instance upgrades are time-consuming, expensive, and, as a result, infrequent. And in an era where APIs make integrating highly specialized tools quick and easy, it may actually be a disadvantage for software to have a wide breadth of functionality.

If anything, with 80% of digital transformation projects doomed to fail, the bigger risk is investing time and money and undergoing a disruptive implementation phase only to find out your chosen software isn’t fit for purpose.

Of course, this is not to say size never matters. Having a large number of clients is extremely important where a vendor is able to leverage this customer base to externalize network effects by, say, training a common AI model or aggregating services that are useful to the network.

But to deliver these kind of demand-side economies of scale requires a modern technology architecture, which can orchestrate interactions across a network. A lot of older vendors, with higher revenues and more employees, don’t have this.

From one-stop-shop to best of breed

When packaged software became commercially available, it was a vast improvement on what banks were working with.

Before there was an IT industry, banks wrote their own apps in-house at considerable expense. In comparison, packaged software met most of their functional needs and ran on cheaper hardware.

Better still, changes could be applied easily, so the ongoing run-the-bank costs were lower, while it was possible to launch new products much more quickly than in the past.

But, the move to SaaS has changed the landscape entirely.

SaaS is more than just a new delivery model. It has changed the plane of competition. When integration was hard, having monolithic systems with broad functionality was a competitive advantage. Now that integration is less of a concern, the focus has changed to software architecture and the quality of both (narrower) functionality and the user experience.

There are many second-order effects. One is the possibility to circumvent the enterprise buyer to sell simpler and better solutions bottom-up directly to the end user. But another is the move to best-of-breed.

If integration is easier, why would an enterprise buyer not want to source the best functionality, rather than take it all from a single application? This again can make breadth of functionality actually a disadvantage, rather than advantage.

The logical play for incumbent software solutions is to become the bridge to these best-in-class applications, but many applications don’t have the capability or the vendors fear the risk of cannibalization. Either way, this is not something that most software evaluations even consider.

New business models

In the same way as vendors need to start thinking about changing their business models, so do financial services providers.

They similarly need to think about demand aggregation, supply aggregation (becoming a platform) or focusing on the plethora of underserved or overserved customer demographics that can now be reached directly through digital channels.

However, to be able to do, requires them to have different technology solutions. For example, they might need to aggregate multiple (non-financial) data sets or deliver services over third-party distribution channels. But, again, these are not criteria in most evaluation reports.

Introducing the Market Map

So, we have launched a new methodology for evaluating software solutions. It starts from the assumption that, now or in the future, financial firms will need to do more than routine innovation. That is to say, to survive and thrive, they will need to move beyond just distributing existing solutions to existing customers over new channels.

Financial firms will need to undertake non-routine innovation.

Since non-routine innovation is a function of capacity for technology innovation and capacity for business model innovation (and normally both), these are the key criteria we use in our evaluation methodology.

Better outcomes

The way enterprise software is analyzed and evaluated is bad for everyone.

Vendors with exciting products are missing out on growth opportunities. Enterprise customers are missing out on potentially transformative technologies. And analysts are spending time putting together reports that are out of sync with their clients’ needs and, so, ever more valueless.

Clearly, enterprise software analysis is broken. Our bet is that changing the methodology will encourage the industry to embrace tools that, though not fitting the traditional mold, unlock more value at lower cost and allow them to get better at what really matters in the digital age: delivering better services at greater scale.

 

The Market Map |
Some surprising results

We get very different outcomes compared to conventional evaluation studies. A lot of smaller vendors rise up the rankings thanks to advanced technology and flexible architectures. And for incumbent software vendors, it clearly distinguishes those that have kept up with technology change and those that haven’t.

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Strategy in the Post-fixed Costs Economy

October 2020

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

 

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.”

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.

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

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,…

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

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 Force.com (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?

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.

Revolut

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?

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

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

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.