THE NEW GROWTH LANDSCAPE #2 — READ 1,3,4,5,6
Understand the Network to Design for Growth and Defensibility
A framework for the long term viability of a markeplace-platform strategy by Simone Cicero and Manfredi Sassoli de Bianchi
This second research update on the emerging landscape of growth in platforms and marketplaces is brought to you by the team at platformdesigntoolkit.com and is dispatched in our fortnightly newsletter.
This essay introduces a framework to think about acquiring relatively sustainable competitive advantage in a marketplace-platform context, ensuring better defensibility through the analysis of the networks of relationships that underlie the platform experiences we bring on the market, a conscientious use of specific flywheels and other tactics. In this post we don’t focus on solving the liquidity problem, and the so-called chicken-egg issue, that will be the subject of the next installment. The Platform Design Toolkit team is now working on a new Guide and a new Learning experience on Growth, Network Effects and Defensibility that will also cover the “product” side of a Platform-Marketplace Strategies. Stay tuned for the release by subscribing here!
Catch up with the whole series here.
When approaching the design and validation phases of a new marketplace-platform strategy two key questions that we always want to ask ourselves are: “Is this a good idea, overall?” and “Is this idea viable over time?”
To answer these seemingly simple but clearly crucial questions is essential to assess the long term viability of our marketplace-platform strategy as an expression of the relevance (value perceived by the user), its defensibility (the capability of the platform shaper to defend the invested effort over the long term) and the overall growth potential (for example as dependent on the size of the target market).
Despite being largely dependent on the path of execution, part of these questions can also be approached earlier on in the process and assessed. First of all, let’s get back to release #1 quickly and let’s remind ourselves the process that, from initial exploration, brings us to identifying the platformization space where the aggregation strategy will be implemented:
- first, we identify an “ecosystem” where we are set to explore for opportunities;
- then we break the ecosystem in major “arenas” that are essentially based on clusters of entities that are looking to execute a certain set of jobs-to-be-done;
- inside a specific arena, we identify all the two-sided relationships and their inter-relationships and we understand how the value works;
- we pick a certain core two-sided relationship, design an experience and start to build growth around that.
Platform Design Toolkit helps the designers in the process by first helping them break ecosystems and assess their assets, capabilities and existing moats through the Platform Opportunity Exploration Guide (an update of which will be released in a matter of days — in the meanwhile one can check this example of the process) and then with the Platform Design Toolkit core set itself, that helps the designer identify and map the relational context, pick one or more core relationship and then build an “experience” as a scalable interaction journey that mixes peer to peer exchanges and platform provided services (for learning and facilitation), and has a replicable business model of which one can understand and assess unit economics.
This core relationship, we choose to start building an experience around, is characterized by an underlying network that has a certain shape, and it’s made of multiple two-sided relationships. These relationships are inevitably around supply and demand and are characterized by certain growth flywheels and show a certain set of properties related to their characterizing supply-demand underlying relationship.
The network properties (dependent on the type of relationship between supply and demand)
We have identified so far a set of seven key properties of a network (or better of the relationship underlying the network):
- the level of supply commoditization/differentiation;
- the symmetry or asymmetry of the core relationship (likely a supply-demand relationship);
- the flexibility of location: Locally or globally bound;
- the single tenancy or multi-tenancy;
- the transaction frequency and lifetime;
- the value of the transaction;
- the monogamous or polygamous nature of the relationship.
Level of supply commoditization/differentiation
This property describes how the perception of the value of the demand side of the spectrum perceives the supply side. Is every supplier different? Are all perceived as comparable? what value is attributed to the supplier?
A good explanation of this property comes with an archetype of the platform economy: ridesharing. It doesn’t matter how good a Uber driver is, she will be always perceived as a cost to be minimized, as the landscape putting in competition different alternatives for local mobility is largely price competitive. Furthermore, the very nature of the experience notoriously caps the value perceived by the user at a certain point: you don’t want your car to arrive in less than 3 minutes (this wouldn’t even let you time to say goodbye) and this effectively caps the “need” for suppliers to a certain extent. Much different is the situation in which, let’s say, a learning marketplace where every different provider of a certain learning experience can specialize in infinite niches, the object of the exchange is heavily praised as a key aspect of our lives, and therefore is much harder to imagine a commoditization process: supply, in this case, is heavily differentiated.
Symmetry or asymmetry of the core relationship (likely a supply-demand relationship)
Most of the networks have asymmetric weights of their supply and demand for a very simple reason: suppliers can normally serve many more customers than the other way around. This is clearly true in, again, a ride-hailing service or in a short distance food distribution platform (such as The Food Assembly) but may not be true, for example in marketplaces that connect non-professionals (e.g.: with second hand reselling marketplaces).
The flexibility of the location: is the network locally or globally bound
Another very complex aspect to understand in network properties is related to the degree to which the relationship between suppliers and demand is more or less constrained to a certain location. The flexibility related to that attachment limits or enables the growth of the network. One extreme of this spectrum is a network that needs both locally residing providers and consumers (à la Thumbtack, or Rover where the providers and the suppliers are both insisting on the same area for the long term), and the other extreme is when there’s no relationship whatsoever between the place of living and the consumption of services (think Upwork). Related to this property we often talk about a key concept, the so-called canonical unit (Dan Hockenmeier spoke about this in this seminal conversation at Venture Stories). The Canonical Unit is the unit where liquidity needs to be sought: as an example, in a network such as that of Thumbtack, according to Hockenmeier people seek for a particular profession — such as a plumber — at a particular location, such as New York. Overall, in networks where local implications are bigger, competitive advantage is harder to sustain and the chance that a more traditionally managed model can outperform a marketplace are higher. When — instead — there’s at least one player in the network that is global, the advantages may be harder to displace and the nature of the network may help the growth engine: think Airbnb; despite growth was hard to attain in a city, the “spillover” effect (travellers coming back home with the idea to start a room rental activity) helped the platform organically land on new cities.
Single tenancy or multi-tenancy
When participants from the supply and the demand side of the experience are able to juggle through multiple platforms, we call this phenomenon multi-tenancy. Normally platforms do not have the capacity to enforce “single-tenancy” if not by artificial constraints (e.g.: terms of services, regulation).
Transaction Frequency and Lifetime
It is defined as how often a participant goes through the key transaction/experience in the platform and how long this can last. Sustainable growth is hard to attain in contexts where transaction frequency is very low: this is also why in Real Estate platforms — for example such as Crexi or Zillow — the focus is often also on realtors and real estate agencies because they are transacting more often than actual buyers (one, or few, transactions in a lifetime). Particularly interesting on this topic is the podcast episode at Two-Sided featuring Crexi on “embracing the middlemen”.
Transaction Value (AOV)
Clearly, also the Average Order Value exchanged within the network has an impact on the network effects and defensibility and it’s important because it is connected with the frequency of transactions and nonetheless with the lifetime value of a user — that is an essential part of the unit economics. The sweet spot would clearly be that of a high frequency of transactions and a high AOV, like it may happen in some cases such as holiday booking, or space rental platforms like Spacebase. A low AOV must be compensated by really high-frequency transactions to make unit economics stand (we’ll see this later and in other posts). Paradoxically, very high AOV doesn’t ensure great defensibility for marketplaces and ink towards more managed approaches to the market, just because the size of the transaction makes the case for more efforts to be poured into making them happen, especially if the frequency is low and the network is local.
Monogamous vs Polygamous
Another obvious key aspect to keep in mind is the relationships between the two parties involved in the exchange: sometimes these relationships may require relevant time, effort or trust between the demand side and the supply side in order to create a relationship. In that case, the relationship tends to be long term and stable (monogamous): a good example would be the relationship between a caregiver and an elderly person, the relationship between a student and a teacher or the one between a cleaning person and a homeowner. Some others just need a quick check-in or even a “plug&play” interaction (polygamous) with less trust required.
Not all network (effects) are created equal: relevance and defensibility depending on the network effects structure
We’ve seen thus that not all networks are created equal. The big question is now to understand how the traits of the relationships that are underlying your network characterize the shape of the network effect curve. The network effect curve describes how the value perceived by n-th users joining the network depends on the size of the network itself. In the most unlikely case, the value perceived by a user is directly proportional to the number of users one finds in the network: this would be the case of a network that behaves according to Sarnoff’s law. We’ve presented already the three basic network types that you’d encounter in an ideal world (see here) so we won’t introduce them again, but the most basic thing to understand about network effects is that normally the curve that describes how value is perceived in your network behaves according to an S-curve.
When the curve starts to ramp up as a “hockey stick” we normally define this moment as the moment we reach “liquidity”. Complete coverage of liquidity and its nature, dependencies and implications will be given in our next post, but it’s important to understand that the properties of the network that we have listed above impact the shape of the curve and therefore are essential to understand what to expect in the early stage. More importantly, for the sake of this post that is all about exploring how to understand if — by design — your target network and experiences can sustain growth, relevance and defensibility. We also need to understand those network properties also impact the long term behaviour of the curve. As one can see, some of the properties have an impact on the curve in a way to generate what is normally called an “asymptotic” network effect behaviour, where value perceived by users plateaus at some point, effectively posing a threat to your platform strategy defensibility.
The case of ridesharing is well known: despite ridesharing platforms can grow substantially big, they’re indeed fated to scarce “network effect driven” defensibility: irrespective of how many Uber drivers there are available in your city, you won’t perceive better marginal value after your ride is there in a time that is shorter than, say, 2 minutes: conversely too much of a crowded network may generate anti-patterns such as reduced value perception in providers (too much competition). Indeed on the supply side of the market, we always need to identify the minimum amount of “orders’’ that make being on the platform a meaningful investment of time for the producers. Properties such as high asymmetry and high frequency of interaction tend to bend the curve down at a certain point because of the capabilities of the providers to serve a huge amount of customers. Frequency is normally also an indicator of “commoditization” for the suppliers: frequent transactions (normally low in AOV) normally require low cognitive load in selection, thus making the case for a common supplier base that is normally substantially price-ranked, sometimes even allowing prices to be set by the platform (for considerations on the impacts on producers agency of such a pattern please check our podcast and webinar with Sangeet Paul Choudary).
A network effect curve that flats out at some point is, therefore, a big red-light on your project’s defensibility perspective as it will let competitors catch up with your value and — over the long term, especially in the context of commoditized producers — cut chunks of your market away.
Competitive advantage: ensuring growth, relevance and defensibility
In presence of weak network effects, other aspects need to be taken care of to ensure the business can be sustainable over time in the face of competition. One of the key ways to do that is to leverage additional flywheels and growth loops.
In 2019, Max Olson provided an excellent breakdown of the most usual sources of competitive advantage that can be integrated with a platform strategy. These can be achieved through a set of craftily engineered flywheels, see diagram above.
Besides generating economies of scale (obvious) and generating an association of brand quality by providing the minimum depth of inventory to generate a credible conversion rate (and thus the association of “this brand is going to solve my problem”), proprietary technologies or — the development of core capabilities in traditional strategy terms — that impose switching costs on the adopters are also useful techniques to pursue.
To the diagram above we would need to add data as a popular source of competitive advantage, built through network effects, particularly for tech companies, from at least two points of view. The first is through what’s called a data network effect, whereby more people share their data the higher the utility of the product (e.g. Waze). The second is a secondary effect of data: a high volume of data enables high tempo testing that leads to continuous learning and improvement. If your platform proposition is indeed dependent on a technology that can acquire advantage through usage (e.g.: by accumulating data), a good platform designer should always try to enable such dramatic improvements.
Furthermore, if you’re attentive enough to focus on enabling the most frequent, important, valuable use case for the users and you provide ways for the user to embed your platforms provided services at the core of their workflow (or communication structure), this design choice can really provide powerful defensibility. A great way of framing this recently came from Kevin Kwok with his idea of atomic concepts as the way the “best products map to how customers think about their workflow” matching “the abstraction level of their customers: not too high that it’s unusable, but not too low that it’s hard to use easily or extend”. According to Kwok, atomic concepts, besides being “the core concepts around which the entire product is built”, “not only align with how customers think of their workflow but often crystallizes for customers how they ought to”. So as a recap: finding the right lens and zoom factor to look into their “jobs to be done” and creating the capability for your platform to fit into a key element of the workflow, or communication structure, of the customer, makes a great growth lever and defensibility.
As we anticipated in the latest post then, defensibility can also be created by an “extension platform strategy”: once you got the “atomic concepts” well and you are empowering a key workflow for the adopters, then you can create a strategy to attract third parties to integrate extensions of your product side of the platform strategy to other infrastructures, through APIs. We spoke about this kind of evolution here in our first instalment — leveraging heavily on Casey Winters theory around this.
Kwok also offered a great way to visualize such a way to use extension policies to create a platform strategy that can fulfil a wide angle of “nicheness”: by allowing the ecosystem to jump in and provide extensions and — to some extent — also the user to self customize the product experience we can be confident to answer the widest scope of use cases (see picture below from his recent essay).
Another key lever of defensibility may come from the capability of your platform strategy to leverage the so-called UFA, or Underutilized Fixed Assets. Again, in another timeless essay Kwok describes UFAs as “things with fixed costs that are not being used as much as they could be. They are important because they can be used more, and from their owner’s perspective all additional usage is free.”
According to Kwok, UFAs have several essential capabilities of which one is key to represent a defensibility option: first of all, they are quick to be unlocked as they sit down mostly unused so they can support rapid growth. Secondly, they can provide preferred pricing options to the producers on the ecosystem as any money these assets make turns into profit (they are already paid), making their cost of acquisition much lower and providing savings that can be passed along to consumers. Finally — and here’s the main defensibility capability — as they are a finite source of supply, it’s hard for new competitors to tap into them once they’ve been discovered and tapped by someone else — ideally you.
New challenges to the concept of defensibility and new defensibility options are also now emerging through the maturation of the technology behind blockchain and the blockchain-enabled token economy. As more layers of the stack unbundle defensibility becomes less dependent on pure network effects mechanisms: one way to think about it would be to consider the impacts of having sovereign digital identities and portable reputation: what would the impact be of such solutions on defensibility? In 2020 Jesse Walden wrote an essay on this topic explaining how all networks are subject to an equation: until the cost of “switching” to another network remains lower than the “fees” (take rates) that the participant will need to pay to the network during her lifecycle, exiting is not an option. Certainly, blockchain-enabled, token-economy based solutions lower the switching cost (forks are easier for instance) but still creating an alternative network and re-gaining the network effects won’t come free. No doubt though, that web3 alternatives will depend much more on the alignment between stakeholder incentives to keep their advantages.
As Lou Kernen stated in his recent article account of Messari’s 10 crypto thesis for 2021:
“If capitalism is all about creative destruction, then DeFi capitalism is all about rentier destruction […] advantage can be maintained only with legitimate commitments to community governance and fair (and early) stakeholder alignment.”
The conversation around how to leverage these new, enhanced design tools that let platform thinkers to not only play with marginal utility but also with ownership, returns and distributed decision making is just starting up, but we believe that the new landscape of growth needs to definitely integrate those further solutions into the scope.
In the next post we will:
- explain the concept of growth as a continuous discipline (not just a hack);
- explore recent developments in the space and how the discipline lends itself to the area of platforms and marketplaces;
- look at a planning framework for a new platform business;
- go through different tactics for a successful launch, towards reaching liquidity.
A new course
As said above, this new framework — that we are already partially teaching at our public bootcamps (next coming up in February) — will seamlessly integrate with our widespreadly adopted Platform Design Toolkit methodology and nicely fit into our existing toolset for the age of networks.
A new intensive course will be released before the summer: the course will be offered at premium discounted price for early registrants to this list of super-early-bird subscribers, provide your expression of interest here at this link: https://platformdesigntoolkit.com/growth-subscribe
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