Marketplace Liquidity: How Side Switching Can Help

The traits that make marketplaces scalable and defensible also affect the ease of reaching critical mass — Side switching is a key feature that disadvantaged marketplaces can use to mitigate liquidity challenges

Sameer Singh
Jun 1 · 8 min read
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Image credit: Unsplash

So far, I have explained some of the key characteristics of marketplace network effects. This includes the impact of fragmentation, geographic range, supply differentiation, SaaS integration, and engagement. But none of this is remotely useful unless startups can actually get transactions flowing on their marketplace. In order to bootstrap these interactions, marketplaces need to hit a critical mass of demand and supply. In other words, they need to have “liquidity”.

As Julia Morrongiello explained in her excellent post, liquidity refers to how effectively a network or marketplace matches demand and supply. Liquidity or critical mass is easier for 1-sided networks (e.g. social networks like Facebook) to achieve because the same users represent both demand (content consumption) and supply (content creation). Since the same user plays both roles, the bar to hit critical mass is significantly lower. For example, assume a network has 100 users. If every user or participant is connected to every other user, the total number of connections between them is 100*(100–1)/2, i.e. 4,950 connections. However, in a two-sided network where users can either be on the demand or supply side (but not both), creating the same number of connections requires a total of 140 participants (70 buyers and 70 sellers). The required number of participants increases even further if the supply and demand are unequal (as they often are). For example, to maintain the same number of connections with a supply to demand ratio of 1:10, the marketplace requires more than 250 participants. Liquidity is a measure of possible interactions between participants (enabled by connections) and so two-sided (and multi-sided) marketplaces face a higher barrier.

Two-sided and multi-sided marketplaces are also more complex because they require an optimal balance between demand and supply. Too much demand and you end up with a poor user experience (unfilled requests) and customer churn. Too much supply and you end up with a poor supplier experience (underutilized capacity) and supplier churn. Maintaining this balance is a constant struggle, but the challenge it poses varies based on the type of marketplace.

In general, the liquidity of any network is a function of the number of users and network density, i.e. the number of actual connections between users relative to the number of users. On marketplaces, these connections link demand with supply and, therefore, liquidity depends on the number of suppliers that can be matched with a given customer request, i.e. density of supply relative to demand. As Borja Moreno de los Rios explained in his TechCrunch post, supply density can be broadly measured in two ways — the ratio of “match-able” supply to demand within a region or within a category. The relative importance of each type of supply density differs based on primary marketplace characteristics — geographic range and nature of supply for the marketplace in question.

Supply Density by Geography

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Take Uber as an example — it is the quintessential hyperlocal marketplace. Riders primarily judge Uber’s product experience by wait time, which depends on the number of drivers in their immediate vicinity. Typically, riders expect a wait time of roughly 5 minutes. This means that Uber needs to maintain a specific supply to demand ratio, within the radius of a few miles, in order to hit that targeted wait time. This makes achieving liquidity more challenging because Uber cannot just make blanket investments in supplier acquisition. For example, assume Uber needs 10 drivers on their marketplace to produce a match with a rider (taking into account active times, etc.). Acquiring 10 drivers spread across a city does nothing to improve liquidity, but acquiring 10 drivers within a few miles from active user locations can trigger matches (bookings). The exact radius may differ for other hyperlocal marketplaces (e.g. Deliveroo and, until recently, Shpock), but they have to grapple with the same, broad constraint. As a result, hyperlocal marketplaces typically need to make significant investments in field operations to achieve and maintain liquidity.

In contrast, Airbnb is a marketplace with cross-border network effects. Guests judge Airbnb’s product experience by their ability to find a high-quality property at a destination of their choice. However, the exact location of that property is rarely an overarching constraint. Airbnb still needs to maintain a specific supply to demand ratio within a destination to achieve liquidity, but that can be spread across a larger geographic area. This gives them more flexibility in supply acquisition. In some cross-border marketplaces, the degree of geographic flexibility is a function of the distance that buyers or suppliers are willing to travel (e.g. Airbnb, Flexport). Meanwhile, others like Preply are purely online and don’t have any geographic constraints whatsoever. But in either case, geographic density is not a significant constraint to liquidity.

Supply Density by Category

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Now let’s take a look at supply density from a different point of view. Uber has commoditized or interchangeable supply. This means that Uber riders are just looking for on-demand transportation, and are not sensitive to the brand of the vehicle or identity of their driver. On the whole, it just has 2–4 categories of vehicles available on its marketplace, differentiated by price and capacity. I have previously explained how this makes them more vulnerable to multi-tenanting and competition. But it also has a benefit, i.e. it is much easier to acquire a critical mass of supply across just a handful of categories. In other words, it is far easier to acquire a critical mass of Uber X and Uber XL drivers, as compared to a critical mass of drivers across every vehicle make and brand (e.g. Toyota Camry, Mitsubishi Outlander, etc.).

By extension, achieving liquidity across categories should be more challenging for marketplaces like Airbnb that have differentiated supply.

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Each of Airbnb’s properties has a range of unique attributes beyond location and price. Potential guests could be looking for a specific type of property, ranging from shared rooms to apartments to private villas. Beyond this, they may also look for properties with other unique features, like a kitchen, fireplace, or parking facilities. This form of differentiated supply creates immense complexity in enabling matches and makes Airbnb more defensible, but at the cost of liquidity challenges, i.e. it is very challenging to acquire a critical mass of supply across each of the numerous categories.

The Marketplace Matrix: Liquidity and “Side Switching”

At this point, it is evident that marketplace liquidity is deeply dependent on the unique characteristics of each marketplace. What makes marketplaces defensible and scalable also has a direct impact on how easy or difficult it is to reach critical mass. Since these are inter-dependent, we can layer liquidity drivers on top of the marketplace matrix to identify broader patterns.

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Note: SaaS enabled marketplaces outlined in orange

Clearly, marketplaces that have both commoditized supply and cross-border network effects (Tier-2A, bottom-right quadrant) face the lowest hurdles to liquidity. They don’t require exceptionally high geographic supply density and they have only a handful of categories across which they need to gain critical mass. This certainly makes execution easier, but it also means that new competitors can scale rapidly. This includes logistics marketplaces like Flexport and Convoy, as well as telehealth marketplaces like Teladoc and AmWell.

Conversely, liquidity is most challenging for hyperlocal marketplaces with differentiated supply (Tier-2B, top left quadrant). Since they need to create both geographic density and category density, it is nearly impossible to reach critical mass without relying on other advantages. In the absence of deep pockets that can fund a “brute force” approach, Tier-2B marketplaces have two options to achieve liquidity.

The first approach is to use a “come for the tool, stay for the network” approach. In this case, marketplaces can leverage “single-player” software to bootstrap the supply side of the marketplace. Creating a SaaS product first allows prospective marketplaces to control and grow their geographic supply density until critical mass is reached before opening the marketplace to the demand side. For example, OpenTable first launched a reservation system for restaurants to manage their own bookings. Once they gained sufficient regional density of restaurants, they opened up their restaurant reservation marketplace for consumers. As a result, they already had liquidity when it was made available for consumers.

In the absence of SaaS integration, the only other way to bootstrap marketplaces that are both hyperlocal and have differentiated supply (Tier-2B) is by relying on “side switching”.

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Side switching occurs when marketplace participants do not have fixed roles, i.e. a buyer can become a seller and vice versa (think Freaky Friday applied to marketplaces). This is an approximation of what happens on social networks, where most users simultaneously act as demand (content consumption) and supply (content creation). As I explained earlier in this post, if every participant on the demand side is also active on the supply side, then the number of participants required to attain critical mass reduces significantly. This reduces the complexity of reaching critical mass across both geographies and categories.

For example, companies like Shpock begin their journey as purely C2C marketplaces (focused on 1–2 categories) where consumers can buy and sell from each other. Once they create liquidity and grow, they can expand to other categories and leverage the existing base of users to bring in more valuable network participants with fixed roles. For example, Shpock recently opened up its marketplace to car dealers to grow transaction sizes and improve unit economics.

Side switching is not exclusive to Tier-2B marketplaces and can occur in any tier as well. For example, even Airbnb (Tier-1) and Uber (Tier-3) exhibit side switching — riders can become drivers and guests can become hosts. This does help liquidity, but only marginally because the rate of side-switching is low, i.e. a very small percentage of the demand side become suppliers. In order to maximize the liquidity advantage from side switching, marketplaces need users to switch back and forth from the demand to the supply side frequently, almost on par with social networks. To really take advantage of this, it is not enough to merely present side switching as an option for your users. Rather, marketplace startups need to embed side switching as a core part of their value proposition. Poshmark is a great example of a Tier-1 marketplace built on side switching.

To summarize, the odds of achieving liquidity depend on the characteristics of the marketplace in question. Both hyperlocal network effects and differentiated supply create different barriers to achieving liquidity, and a combination of both can be even more problematic. SaaS integration and side switching are two important tools that entrepreneurs can leverage to mitigate these risks.

Breadcrumb.vc

A network effects guide for founders

Sameer Singh

Written by

Network Effects Advisor, Author & Investor

Breadcrumb.vc

Network effects are among the most powerful, but also the most misunderstood, forces that shape the future of startups. Breadcrumb.vc is my attempt to lay a “trail of breadcrumbs” to help founders find their way.

Sameer Singh

Written by

Network Effects Advisor, Author & Investor

Breadcrumb.vc

Network effects are among the most powerful, but also the most misunderstood, forces that shape the future of startups. Breadcrumb.vc is my attempt to lay a “trail of breadcrumbs” to help founders find their way.

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