Liquidity vectors in marketplaces

The lifeblood of a marketplace is its liquidity. If you’re helping to build or grow marketplaces, you’re probably familiar with this concept. I like James Currier’s definition:

“Liquidity is the probability of selling something you list, or finding what you are looking for.”
James Currier, NFX

To achieve this, we have to ensure that whenever there’s a need on either side of the market, there’s a very high probability that this need will be met.

A marketplace’s ability to facilitate liquidity creates the network effects of the marketplace. Think Uber’s short wait times, AirBnb hosts’ ability to keep their listings booked year-round, and consider the whopping 13% of people who convert after searching for a product on Amazon.

Liquidity Vectors

Marketplaces can be complex and multi-sided across widely different industries. We’ve helped grow companies while building diverse marketplaces in healthcare, e-commerce, travel, construction, and local services. As an outside team that’s brought in, we started creating a mental model for how to think about growing these companies

I’ve been referring to our mental model as “Liquidity Vectors.” Creating liquidity in a marketplace isn’t as simple as having enough participants on all sides of the marketplace available at all times. There are many other facets to consider when trying to build liquidity.

Liquidity Vectors, at least as I define them here today, are all of the different dimensions necessary for a marketplace to create liquidity and, therefore, a healthy environment for stakeholders.

For example, some of the Liquidity Vectors a company like Doordash would have to consider include delivery zip-codes, restaurant cuisine types, delivery times, and demand volume to ensure that all three participants in the marketplace get value out of the network.

Growing Subsets of Vectors to Achieve Liquidity

We’ve learned that, after defining the different vectors needed to help improve liquidity in a marketplace, we have to start working on a subset of these vectors with a localized approached to be able to nurture a healthier marketplace.

We were fortunate enough to have Deepak Chhugani on our podcast the other week as he talked about this localized approach for his company, Nuvocargo. In order to initially generate transaction volume, they had to focus on a single bridge in Texas with a specific type of cargo (non-refrigerated payloads at a certain tonnage). That may sound very specific, but it’s simply a subset of the vectors in their marketplace: trade corridors, payload types, vehicle types, etc.

Similarly, in our experience helping grow healthcare management marketplaces like K Health (with specific medical issue and location being the important vectors) or acquiring drivers for a mobility company (where zip codes and car type were the important vectors), we’ve seen this approach to be most successful.

Using this outlined approach, we are then able to focus our efforts on reaching liquidity faster:

  • Creating separate audience tests, creative iterations, and landing pages for specific subsets
  • Focusing ad spend on the subsets showing promise and getting to a healthy transaction volume in the submarkets before launching new markets
  • Using creative iterations to understand the nuances of the submarket (e.g. Spanish language ads, or ads focused on a specific neighborhood, etc.)
  • Employing the hyper-localized approach to understanding how to grow the different sides of the marketplace (e.g. supply/demand) and creating a playbook for scaling

Our biggest learning from executing this type of work is that the localized vectors building up the liquidity in the network sometimes display different behaviors in the marketplace. It may be due to the culture of the city (people travel differently in Paris compared to San Francisco), the nature of the supply in that city (people might drive different types of cars or speak Spanish more in Phoenix), or specific nuances of the vector itself (acute issues like UTIs need to be treated differently than chronic issues).

This all brings us to our next learning: defending liquidity.

Avoiding Regression in Liquidity

Understanding the nuances of the localized vectors in your marketplace, and maintaining this understanding over time, is incredibly important.

The most common issue we face in trying to maintain liquidity while supporting growth for our clients is competition in existing liquid markets. Marketplace (or market network) business models are well established ideas with giants competing for attention, market share, and profits.

This means that when we advertise, we engage in many forms of competition, including auction, visual (on paid social you are literally competing with the visual aspects of other brands), price, and reputation.

Competition is expected for any company using advertising to grow, but the key difference in marketplaces is liquidity regression. Just as using the Liquidity Vector approach above can work in growing a network, it can also be an approach to attacking an existing marketplace.

Alerting Infrastructure

To defend against this form of attack where a competitor decides to enter a subset of your market, we use an alerting mechanism to keep watch. 👀

Using alerting tools to keep a pulse on each different submarket, especially if you have a global footprint, is extremely important. Fraud, competition, and/or market saturation will be the trigger for changing your approach .

Clustering & Personalizing Infrastructure

As you learn how to focus your efforts — unlocking new markets and understanding the nuances of growing the network — you will find efficiencies in how to scale-up markets, and create playbooks for how to enter them.

  • Control growth for each side of the market by using different campaigns or ad sets with models to help ensure efficiency
  • Use your liquidity as your competitive advantage. You may have millions of listings in your marketplace. Every individual listing can be converted into an ad via Catalog Ads
  • Produce creative that feels personalized to each specific market and let platforms sub-target the right markets for you in order to maintain low costs
  • Leverage efficiencies of scale in ad spend that come with clustering (I’ve written more about this here)

Ensuring that you have a strong playbook for scaling up your efforts while maintaining a personal approach to each market is an important philosophy for avoiding liquidity regression.

By author

Nima Gardideh

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