Ben Robinson | aperture

The 10-point BaaS buyer checklist

We created this 10-point checklist for BaaS buyers: to distil some of our experience from helping companies to select the right BaaS platform.

The embedded banking opportunity is clear, but choosing the right partner might be less so.

You probably won’t be very surprised to hear that the Banking-as-a-Service market is pretty hot right now. Just last week, Swan announced a series A fundraising, Starling announced it is taking its BaaS offering into Europe, Modularbank rebranded as Tuum to position itself as a core banking platform for BaaS – and there were probably dozens of other announcements we missed.

Making banking products available as a service to be embedded into non-banking channels is a logical next step to improve customer experience and grow the financial services market – which explains the market momentum we are seeing. However, even though the BaaS opportunity is compelling and well-understood, it doesn’t mean there aren’t significant nuances and complexities to navigate.

This is why we created this 10-point checklist for BaaS buyers: to distil some of our experience from helping companies to select the right BaaS platform.

Banking distribution is the hard bit

Banking services distribution is splitting from banking services manufacturing. As in other industries, this is happening because of digitization, which in the case of banking specifically has been given a helping hand by the open banking legislation that has increased the rate of data-sharing between institutions.

When services digitize, the pinch point in the value chain inverts from manufacturing to distribution. Back when it was difficult and costly to produce stuff, the manufacturers with scale economies enjoyed the highest return on assets and the highest profits. Now the costs of producing digital services have fallen, the difficulty has become distribution not production. (See below our Challenger Bank Map to get an idea of how many bank new entrants we’ve seen in recent years).

The distribution challenge manifests itself in high CAC and low LTV. B2C financial services companies, both incumbents as well as fintechs, need to reach time-poor and attention-poor consumers. This costs a lot of money. According to ARK Invest, the average Cost of Customer Acquisition (CAC) for a bank in the US in $1,500.

This might be lower for a fintech with an innovative product (Nutmeg, now acquired by JP Morgan, had a CAC of c.$680 for example), but fintechs tend to have narrower product sets with which to cross-sell customers, contributing to low revenue per customer (Nutmeg generated c.$200 per customer in 2019).

The bigger problem both banks and fintechs have, however, is low engagement. Banking is transactional. We open up an app to make a payment or check our balance and then we close it. There isn’t anything to hold our attention and keep up using the app. This makes up-selling and cross-selling difficult for both fintechs and banks, because it reduces the surface area, the touchpoints, for providing the right offers. As a result, customer LifeTime Value (LTV) suffers.

Embedded banking lowers CAC, but it does MUCH more too

Embedded banking, the act of inserting banking services into typically non-banking channels, can solve these issues of high CAC and low LTV. When, for example, H&M offers a customer credit for a purchase they make, there is no additional CAC; it’s an existing customer. The credit service is a cross-sell, boosting LTV, from an already engaged consumer.

But embedded banking goes much further than just improving the economics of banking – it also grows the market for banking services. Why? Because it presents what the customer needs, where the customer is and when the customer needs it – removing the friction and cost of discovery that result in massive under-provisioning of financial services.

It transforms banking because it adds it as part of an existing user or buying journey. If we continue with the H&M example, the buyer does not have to change channels. If the buyer needs credit for a purchase, they don’t have to open up a separate app and apply for credit. Instead, because the context is understood, the credit is presented at the right time and in the right place.

In addition, and this is often overlooked with embedded banking, the form of the banking products is changing – moving from off-the-shelf services closer towards what makes sense for that buyer journey (or what Clayton Christensen termed the customer’s “job-to-be-done”). In this H&M example, the customer is not presented with a credit card, but is offered the chance to pay for the purchase in installments.

BaaS has made embedded banking an option for every brand

Baas providers – and, by extension, the BaaS market – have emerged to build the bridge between banking and brands.

Linking banking with consumer brands is not new, but the speed and cost of doing so is what has changed with BaaS. Consumer brands have long seen the potential of distributing banking services. Some built banks. Others created deep partnerships with banks to white-label their products. But these were expensive, long-term projects with high switching costs.

As banking began to digitize, internet started to introduce banking services natively into their experience, such as the Amazon one-click checkout, which many credit as Amazon’s breakout moment – but without either becoming banks or having to white label banks. But, while Amazon and Uber demonstrated the massive potential of embedded banking, not many others were able to follow because they didn’t have the technology, compliance and other resources to do so.

This is where BaaS comes in. In an example of what Union Street Ventures termed the “apps-infrastructure cycle”, once the potential had been demonstrated, along came the infrastructure. In this case, the infrastructure sits between regulated banks, on one side, and brands on the other, abstracting away the complexity to make it possible for any company to distribute banking services.

Navigating the BaaS maze

Not all BaaS providers offer the same value proposition, however. As we set out in an earlier article, once you start to look into the market you realize there is a lot of complexity.

Some BaaS providers are vertically integrated, while others – what we call modular BaaS providers – sit between the bank and the brand. But modular BaaS providers are not all created equal either. Some, for example, are regulated and other are not. Furthermore, we see a growing trend towards further modularization – or fragmentation – with modular BaaS providers beginning to specialize on certain aspects such as user experience and aggregation. In addition, BaaS provider tend to cover specific use cases and specific geographies, which must also be considered.

All in all, it’s a bit of a maze.

A 10-point checklist

To help navigate this maze, we have put together a 10-point checklist as well as a Market Map for BaaS Providers.

Since not all BaaS providers are the same, here are some of the questions that an embedder brand should use when evaluating the options:

Can the BaaS provider meet my use case? If your goal is to offer employees a debit card, you’ll need a BaaS provider that can issue cards.
Could the BaaS provider meet my future use cases? This is important to consider upfront because once you’ve started to improve customer experience and retention with a single banking use case, you’ll want to add more. If your BaaS provider can’t meet these use cases, you might get stuck.
Does the BaaS provider operate in my existing geographies? If your business is in France, you’ll need a BaaS provider that is regulated (through their own licenses or partnerships) to operate in France.
Could the BaaS provider support international expansion? If yours is a global business or you’re considering expanding internationally, it is worth noting that many BaaS providers have a limited geographical footprint, which could evolve into you having to manage multiple BaaS relationships across the group.
Does the BaaS provider offer competitive fees (and revenue share)? In our experience, there are massive variations in the way BaaS providers price – everything from the split of fixed vs variable costs, the per unit pricing for services (e.g an FX payment) as well as interchange revenue split. It is worth building a detailed model to compare BaaS providers side-by-side using business projections.
What is the BaaS provider’s compliance status? Might we need to take on some compliance obligations? Significant differences appear here as well. Some BaaS providers manage all of the compliance obligations, while others do not. If your business is not banking, you might want to think carefully about whether you want to be hiring a Money Laundering Officer, for instance.
How much development do we need to do to launch this service and do we have the resources/appetite for it? While some BaaS providers offer completely out-of-the-box service, including pre-built UIs, others require the embedder brand to do a lot of development. There are advantages and disadvantages for each approach, just make sure you go into the relationship with your eyes open, understanding you may have to take on a lot of development which impacts costs and timelines.
How quickly can we launch this service? This question is partly a function of question of how much development is needed, but also reflects other factors, such as whether the BaaS provider already has all other ancillary services in place.
Are we going to receive the insights to be make our end customers’ experiences truly contextual and relevant? This is crucial. Even if a BaaS provider can help you launch quickly across the globe, they may not have the capabilities that allow you to develop the insights to offer more relevant content and recommendations over time.
Is the BaaS provider going to provide a service that gets better as its customer base grows? We think one of the main points of differentiation between BaaS providers is the extent to which they have built common models (e.g. for detecting fraud) that will confer network benefits to clients and make being part of ecosystem more valuable over time.

The Market Map for BaaS Providers

As an aid to navigating this complex space, and to begin to answer these 10 questions and others, we have put together The Market Map for BaaS providers.

The Market Map is a new approach to evaluating technology platforms. First used to look at WealthTech solutions, the Market Map ranks providers according to their ability to enable business model innovation and technology innovation.

Unlike other industry rankings, the methodology isn’t meant to separate the good from the bad, but rather to help decision makers determine which platforms can meet their strategic goals.

For example, if you’re a tech savvy challenger bank looking to get to market quicker with a new service using a BaaS provider, you are going completely different set of business and technology requirements from a small retailer with no tech department looking to offer its clients point-of-sale credit.

The opportunity is clear, the right BaaS partner might be less so.

If you’d like to get a copy of our report on embedded banking, which includes The Market Map for BaaS Providers, it is available here. Or, if you’d like helping selecting or working with a BaaS provider, please reach out.

The Market Map
Best BaaS Providers 2021

Your comprehensive guide to the burgeoning Banking-as-a-Service market. Who does what, who operates where, who runs on who, who’s regulated and who’s the best fit for your needs.

What’s inside?

35+ pages;
45 BaaS Providers in the Spotlight;
14+ charts and tables;
The Market Map quadrant.

Choose your option

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.

Choose your option

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

Choose your option

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.

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.

For many, ourselves included, the extent of the current crisis came as a bit of surprise. At first, we thought we had a MERS or SARS-like crisis: a novel virus, but which would be limited to one region of the world. Now it’s clear that we have something that, although less deadly than some predecessors, is far more widespread — a global pandemic. As a result, we will see a profound economic impact and companies should prepare for this.

However, this is not an article about how businesses should face up to the crisis or conduct contingency planning. There are already plenty of articles out there that already do that — like this one from Sequoia Capital for startups and this one from BCG for more established firms.

Instead, we want to look at one small aspect of the crisis: how financial services companies should look to rationalize their ongoing technology projects.

Don’t pull up the drawbridge

The normal, knee-jerk response of most companies in a crisis is to issue an immediate moratorium on all new projects. The email comes from the CEO or CFO and it says: until we have more visibility on how the crisis will impact the business and, in order to conserve liquidity, all new projects are on hold.

The intentions are good — let’s take a temporary pause on spending and restart projects when we have money and bandwidth— but the outcome is inevitably sub-optimal. Why? Because it’s an arbitrary cut-off point. The projects you kicked off in 2019 might be of very marginal value to the company’s future success whereas the projects you were about to start in April — and which have now been indefinitely deferred — might have been game-changers.

All projects should be considered in the light of the new situation, not just future ones. If it had been possible to anticipate the current crisis, how many projects would not have been given the green light in order to conserve cash for later? And so, rather than just stopping all new projects, a company should revisit the business case for all projects and move forward with those — existing and new — that pass the criteria set now, even if stopping some projects may incur penalties. Sunk cost bias can be especially dangerous in a crisis.

So here are some of our tips for prioritizing the right projects.

Beware risk and opportunity cost

Most the large banks we have consulted over the last year are undertaking one or more transformation projects, such as implementing a new lending, core banking or payments system. These are the kinds of projects that should be heavily scrutinized in this kind of environment.

Firstly, the payback from these projects is inherently difficult to predict.

Secondly, these are long projects that tie up critical resources — money, of course, but many of the best internal people too — and so have massive opportunity cost.

Thirdly, as with any long and complex project, there are significant risks and, in our experience, when these are properly factored into the financial model, the return on investment often turns negligible or even negative. But, the most important reason for considering slowing down or pausing some of the projects is that, increasingly, there is a quicker, safer and cheaper route to the same outcome.

Bag some quick wins

In a crisis, when the management team is under pressure, creating some quick wins earns important buy-in from internal and external stakeholders as well as creates the financial circumstances — freeing new flows — for new or follow-on projects.

This is why we recommend that, even when a bank wants to embark on a significant project like modernizing its lending technology, it tackles the project in chunks targeting the highest impact/lowest risk areas first.

This doesn’t mean progressive renovation — which is normally shorthand for staging the rollout of a monolithic system — but building out a new platform use-case by use-case using modern, microservices-based systems whose APIs allow for easy integration with the rest of a bank’s IT estate.

In addition to modern systems that allow for complex projects to be truly decomposed into dozens of small projects with separate, predictable (and positive) RoI, the IT enterprise stack is splitting into three layers, which gives more options for deciding which projects to undertake, when and why.

Consider Impact on the future

As custodians of the company they run, management’s first priority in a crisis is to ensure the survival of their company. But crises never last forever and in addition to riding out the storm, management need to be mindful of the future and weigh up the actions they take now on the future of the business, especially when — as now — the future looks so different.

What can we already surmise about the post-pandemic world? Probably the safest bet is that speed of digitization in banking, already rapid, will accelerate from here. Making this statement slightly more concrete, we predict:

faster translation of lower costs into lower spreads and fees
a decline in face-to-face interaction coupled with a greater tendency to embed banking functions — credit, payments, hedging, etc — into third-party channels (with higher engagement)
a rapid switch in value creation from transaction provision to utility and convenience: essentially removing any friction in customer interactions at the same time as using customer data to help customers make better financial and commercial decisions

Against this future picture, should a bank still go ahead with a 5 year project to replace one monolithic back office system with another?

And this is where the split in the enterprise IT stack also matters.

Not only can banks tackle IT projects in much smaller increments than before thanks to new microservices-based systems, but they can address interaction, intelligence and record-keeping independently of each other.

In our view, and as we have written about before, it is the intelligence layer that requires most urgent action. As product manufacturing splits from distribution and as interaction moves increasingly to non-banking channels, value will move to orchestration — connecting customers (channels) with product manufacturers.

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.

Given the changing role of financial institutions, should a bank still go ahead with a 5 year project to replace one monolithic back office system with another?

Don’t waste a crisis

As Machiavelli said

“Never let a good crisis go to waste.”

During a crisis, management has an infallible pretext and the tacit permission of the organization to push through major change — to ensure continuity and the ongoing success of the business. Stopping all new projects and hunkering down on the initiatives already in flight is almost the antithesis of this. It is a defence of past decisions, a protection of the status quo ante, when we all know that the post-crisis world will look dramatically different.

The best leaders are those who capitalize on a crisis to overcome the corporate immune system. They recognize that a crisis gives a unique chance not just to renovate IT systems, but to reshape culture and business model. All the opposition to change — the internal IT team that wants to build all of their own systems, the board that wants you to buy systems from the industry dinosaurs, the product owners who fear cannibalization and block any kind of ecosystem move— should dissolve against the rallying cries of a crisis.

Articulate the change narrative

Kick-starting systemic business model change is never easy and the ease of change is normally inversely proportionate to the size of the organization.

Crisis gives the pretext, but management still needs to make the argument if it is to carry the organization with it. Furthermore, a powerful narrative for change, which marries the company’s existing strengths — such as large customer numbers and a trusted brand — with a cogent justification for doing things differently in the new world, will also buy management a lot of time and goodwill beyond the company itself, with customers and investors who may exercise more than their normal patience.

Use stop/go triggers

One of the reasons why so many existing IT projects are overrunning in time, budget and scope is because the conditions for success weren’t narrowly defined enough — or weren’t enforced tightly enough. They were justified on dubious economic grounds and they’ve been allowed to run in the hope of one day achieving this uncertain pay-off.

We now need to do things differently. We need to break down the mega projects. We need to prioritize projects that are relevant to future operating models. But we also need to conduct more experimentation. We are moving to a trial-and-error economy where firms, as adaptive ecosystems, need to enter into more experiments. This may seem daunting but needn’t be if companies put in place clear and regular stop/go triggers on all projects.

Essentially, as we’ve written before, governance needs to pivot from making long, well-considered yes/no decisions about projects to one that introduces the conditions for agility and experimentation at scale.

In summary

We don’t know how deep the economic impact of the new coronavirus will be, but there will be a profound hit to many businesses. The temptation is focus on the short term, defer decisions and conserve cash. But the much smarter move is to capitalize on crisis to introduce change, to take the steps that will underwrite the company’s future competitive advantage and, ultimately, enable it to prosper in the post-pandemic world.

In short, instead of bedding down for winter, renovate instead.

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.

The investment “moat”

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.

Scale is no longer a moat

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.

Achieving network effects in banking

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.

Embedded banking

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”.

There is already a strong move underway to do this. Because payments is the most frequent of the transactions we make (as well as an extremely rich data source), it makes sense that payments has been the beachhead for most non-banks from Uber to Apple to start their push into banking or, better put, into embedding banking. But it won’t stop there.

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.

What’s the fintech opportunity?

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.”

Don’t just believe us

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.

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.

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.

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).

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.

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, “the battle is moving from the old moats, the sources of the data, to the new moats, what you do with the data.”

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.

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.

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?

The rise of the 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.

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.

Different business models

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.

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.

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.

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.

Hard for incumbents to match

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.

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.

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

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

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

Where can it make sense to launch a challenger bank?

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.

1. Entering new markets.

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.

2. Entering new geographies.

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.

3. Tackling financial inclusion

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.

4. Lowering the cost of capital

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.

5. Technology renovation

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.

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.

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.

6. Launch a new business model

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.

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 not challenger banks, then what?

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.

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

There is a structural move underway towards subscription-based business models. According to Zuora’s Subscription Economy Index, subscription revenues are growing five times faster than regular transaction-based revenues. Part of this reflects changing consumer behavior towards renting, rather than buying, assets. But the other explanation is that with internet distribution, digital services have become abundant. As such, it makes less sense to restrict supply to maximize profits, as companies did in the past. Instead, companies are working with new business models that take as their starting point low or even zero marginal costs of supply and seek to generate revenues and profits differently. Some seek to make the service available for free and charge advertisers (like Facebook), others seek to drive down the cost of sales to maximize output (like Uber) while others seek to aggregate abundant supply in a way that brings convenience to consumers, for which they charge a subscription price in return.

Is a subscription model right for Spotify?

Prima facie, Spotify and, to a lesser extent, MoviePass look like good candidates for subscription pricing. Abundant, there is negligible cost and no deterioration in the quality of either music or movies with additional consumption (although for movie theaters, there is finite capacity, of course — not to mention that MoviePass doesn’t access the most profitable aspect of moviegoing, which is concessions). But the common business model challenge with Spotify and MoviePass is that marginal costs increase as consumption increases. In Spotify’s case, it pays the labels and the artists every time a song is played. For MoviePass, it pays the cinema every time a customer watches a movie. As such, they are offering for a fixed cost to consumers what in their case are uncapped marginal costs. And so, their business models become a gamble that 1) most consumers will stop consuming before marginal costs exceed average monthly revenue per customer 2) they can drive down operational costs to compensate for low gross margins 3) they can open up additional revenues streams over time, like selling data to artists or movie makers or 4) they can develop superior experience that drives down customer acquisition cost (CAC) and churn over time. But it is easy to see how this is precarious — and hence why Spotify isn’t profitable — and also how elements of the model are in conflict, such as developing superior experience without pushing up consumption.

Spotify vs Netflix

For Netflix, the model works very differently. Netflix buys the rights to its content, so that it turns variable costs to fixed costs that it spreads over higher revenues, meaning high gross margins. Moreover, the content producers are in general fragmented — Disney , for instance, has its own route to market through its own streaming service — such that their power is small and diminishing vis-a-vis Netflix, allowing it to extract continuously better terms. Contrast this with the music industry where three labels control practically all of the rights and thus have strong bargaining power to negotiate advantageous terms.

Another major difference between Spotify and Netflix is that Netflix is able to constantly improve its offering by investing in original content. Where people say that spending billions on original content creates the pressure for Netflix to keep growing, this is somewhat true — they want to recoup the costs. However, it doesn’t oblige them to up the efforts to acquire new subscribers, but instead allows them slowly but steadily to increase subscription prices over time without seeing an increase in churn, meaning gross margins continue to rise, more than offsetting the increase in operating costs. The issue with Spotify is that, for now at least, it is offering a largely undifferentiated service. Sure, it has better playlists than Apple’s or Amazon’s streaming services, but all streaming services allow you to access all music. This not only limits the scope to take up pricing (in contrast to Netflix, this would provide significant churn), but it also pushes up CAC since consumers have many direct substitutes (witness for example how Spotify is giving away Google speakers to induce new subscribers), further pushing down gross margins.

Spotify as Netflix

It is against this backdrop that Spotify’s move into podcasting is so interesting. In contrast to music, the supply of podcasting is highly fragmented — there are 700,000+ free podcasts available today, including our own :) — meaning that the aggregation dynamics are completely different. In addition, the audience is similarly fragmented making it difficult to monetize podcasts through ads — today, podcasts in the US generate around $300m in advertising or $0.01 per active user per hour, 10x less than radio and 70x less than newspapers.

Spotify has made three acquisitions in this space — Gimlet Media and Parcast, which give it a catalog of high-quality podcasts and the team to be able to commission and produce more of the same and Anchor, which is a platform for amateurs to make and distribute podcasts easily. So, in short, it is building original content à la Netflix as well as providing a route to audience for the long tail of producers, on top of which is can seek to increase advertising revenues or take up subscriptions. With podcasts, therefore, Spotify is a position to become a true aggregator, gaining control over supply such that gross margins increase and able to leverage its position to deliver increasing returns to scale that would see a rise in average per user revenues at the same time as reducing churn and lowering CAC.

Spotify vs Amazon and Uber

One of the things you realize when you read this fascinating oral history of Amazon Prime is that, at the time, no one knew what it would cost to launch and what kind of a black hole in the company accounts it could create with massive adoption. Instead, you get a sense of Jeff Bezos’ drive and leadership, the company’s focus on constantly improving customer experience as well as how innovation materializes in real life.

What is interesting about Amazon Prime in the context of Spotify is that it is analogous to the Spotify model: a fixed price subscription for a service where marginal costs are uncapped. In the same way, Amazon’s best customers could have become its worst customers, driving down profitability. However, not only did Jeff Bezos see this the other way around, as a means to encourage its best consumers to consume more (“ We’re not going to take our best customers for granted”), but because of all its different business lines, Amazon was able to capture that extra consumption across its estate and turn it into revenue. This has continued to be the Amazon philosophy (“When we win a Golden Globe, it helps us sell more shoes”), but it’s not a strategic play for Spotify since, for now at least, it’s a single service offering.

The problem with the Amazon model — and the Uber model — is that it leaves a lot of money on the table. In the case of Uber and Amazon, both have low distribution costs but high other costs (drivers, fulfillment centers, logistics, etc) so the incentive is not to maximize profits by cutting supply, but instead by maximizing supply to maximize revenues (and network effects). This effectively pushes out the supply curve, which creates significant additional consumer surplus (a difference between the price paid and the price customers would have been willing to pay). A study by Freaknomics author Stephen J. Dubner estimates that, in a single year in the United States, Uber produced USD7bn of consumer surplus, with consumers paying USD4bn when they would otherwise have been prepared to pay USD11bn. This is money that would historically have gone to workers and to profits.

Spotify vs Tencent Music

There do, however, seem to be emerging business models — coming out of China — that conform to the platform dynamics of low distribution costs and high supply, but which manage to capture much of the consumer surplus generated. A look at Spotify compared to Tencent Music helps to illustrate this.

Tencent Music is a music subscription service like Spotify, but with a twist. Its best customers are its best customers.

Tencent Music operates a completely different business model from Spotify, one described by Eric Feng as shared-value transactions. For most users the service if free (less than 15% of sales are generated through subscriptions). This opens up the service to the largest possible audience (800m+), maximizing the potential for network effects and downstream monetization. This monetization comes from getting its most active and engaged users, those with the highest willingness to pay for the service, to pay more — in turn converting consumer surplus into revenue. Practically, it does so through social interaction, such as allowing users to sing karaoke together or send each other virtual gifts, gamification, such as fan leaderboards that put users in the running for prizes, as well as plenty of paid-for exclusives like live-streamed concerts or early access to new albums. As a result, 4% of its customers account for 70% of its revenues.

A new way?

When you read this excellent A16z article, you realize that these kinds of shared-value business models based on deep customer engagement are proliferating across Chinese e-commerce, from music to video to books. They offer an alternative to business models based on subscription, which aren’t the right fit for many businesses and which lock consumers into long duration contracts; an alternative to business models based on advertising, which create a conflict of interest between service provider and consumer (as well as plenty of negative externalities); and an alternative to business models based on transactions, which leave a lot of money on the table.

The question is why are these business models emerging out of China? Connie Chan suggests it’s because businesses in China thought about “content consumption in a mobile-first way”. This is certainly true, but it is also because China built its payments infrastructure to support a mobile-first world, one where business models based on micropayments can be viable. As we discussed in our first podcast with David Galbraith, the “US internet” is built on layers and layers of legacy technology that make payments extremely expensive and which effectively force companies into transaction, subscription and ad-based models.

One solution to this problem could be crypto. Crypto is almost infinitely divisible, making the most tiny of micropayments practically possible. In addition, because it circumvents the existing payments intermediaries and their fees, it can make micropayments economically viable. The problem, however, is that — for now at least — distributed ledger technology is nowhere near scalable enough to cope with the volume of e-commerce payments, which would of course multiply if transaction amounts got smaller and if micropayments opened up a larger addressable market (note, for instance, that ad spending is around 2% of GDP, creating a ceiling on ad-based revenue).

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.”

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.

Measure twice, cut once

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.

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.

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.

Complex, adaptive system

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.

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.

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.

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.

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.

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.

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.

The model for Europe

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.

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.

Tweaking the Swiss model

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.

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.

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.

A confident future

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.

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Banking distribution is the hard bit

Embedded banking lowers CAC, but it does MUCH more too

BaaS has made embedded banking an option for every brand

Navigating the BaaS maze

A 10-point checklist

The Market Map for BaaS Providers

The Market Map Best BaaS Providers 2021

PaaS vs BaaS

The differences in BaaS

Out-of-the-box BaaS

Vertically-integrated, customizable BaaS platforms

Vertically-integrated, customizable BaaS offerings from incumbent banks

Newer entrants offering a narrower range of services

Finix is a kind of hybrid model

Modular and customizable BaaS platform

BaaS operating platforms: the value of linking many to many

The emerging competitive landscape for BaaS operating systems

The Market Map | Some surprising results

From supply to demand

A vicious cycle

Size doesn’t always matter

From one-stop-shop to best of breed

New business models

Introducing the Market Map

Better outcomes

The Market Map | Some surprising results

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

Renting scale and the end of fixed costs

Platforms, aggregators and the long tail

More precarious

Where to play

Underserved and overserved markets

Direct to consumer

Avoiding the aggregator tax

Don’t just rent commodities, rent luxuries

Achieving internet escape velocity

Don’t pull up the drawbridge

Beware risk and opportunity cost

Bag some quick wins

Consider Impact on the future

Don’t waste a crisis

Articulate the change narrative

Use stop/go triggers

In summary

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.

The investment “moat”

Scale is no longer a moat

Scale can become a hindrance

The new moat

Achieving network effects in banking

Embedded banking

What’s the fintech opportunity?

Don’t just believe us

A brief history of banking software systems

​

Bank systems in the internet era

The open banking era

Systems of intelligence

Systems of intelligence in banking

Commoditization and de-commoditization — the emerging vendor landscape

To sum up…

The rise of the challenger bank

​

Different business models

Hard for incumbents to match

Where can it make sense to launch a challenger bank?

1. Entering new markets.

2. Entering new geographies.

3. Tackling financial inclusion

4. Lowering the cost of capital

5. Technology renovation

6. Launch a new business model

If not challenger banks, then what?

The move to subscriptions

Is a subscription model right for Spotify?

Spotify vs Netflix

​

Spotify as Netflix

Spotify vs Amazon and Uber

Spotify vs Tencent Music

The Market Map
Best BaaS Providers 2021

The Market Map |
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The Market Map |
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