Most aggregators don't have network effects..

..and why Wefox might be an exception to this rule

In a recent conversation, Bill Gurley made an interesting point regarding marketplaces that applies to online aggregators. He posited that most online marketplaces merely bring together demand and supply and don’t enjoy any network effects. The real advantage under such a model is the better customer experience and a lower CAC due to discounts enjoyed through bulk buying of media. As such, these companies typically only trade at 1-2x revenue versus the 10x+ revenue multiples that marketplaces with network effects enjoy. And as an early investor in Uber, Yelp and OpenTable, Bill is someone who understands network effects better than most.

He then laid out a nice framework to test whether an aggregator benefitted from network effects or not. Plot a graph with the x-axis having penetration rate for the product or service and y-axis having ‘Value to nth customer/Value to nth supplier’. Two such graphs should exist for each aggregator - one having the y-axis for the demand side (customer) and one for the supply side (supplier). If one of these graphs is upwards sloping, the aggregator benefits from network effects. So something like this:

I decided to test his theory by looking at recent valuation for GoCompare (GC), a UK-based price-comparison website, focused primarily on selling car insurance. First, let’s imagine the ‘network effects’ graph for GC. Let’s start with the demand side. The 1000th user on GC got roughly the same value from using the platform as the 10,000th user. So our graph is basically a flat horizontal line. How about supply? The 10th insurer GC signed up to its panel of insurers also got roughly the same *benefit* as the 100th insurer. So another flat line and no network effects here either. Now let’s look at the public market valuation for GC - the company is currently worth £337M on 2019E revenues of £160M so no network effects and a valuation multiple of 2.1x - roughly in line with what Bill hypothesized.

Now, let’s look at Wefox - a relatively newer insurance aggregator with a different business model focused largely on providing a better technology platform to brokers. For those interested in learning more about Wefox, I encourage you to watch this brief video:

Now let’s put Wefox through the network effects test. The demand side is similar to GC’s since the nth users gets the same value as (n-100)th. However, the supply side is different as the value proposition to brokers is different. Small and medium brokers (the ones Wefox works with) operate at a hyper-localized scale. As such, from a competition perspective most of them don’t care if Wefox signs up another 100 brokers to the platform. If anything, more brokers on Wefox leads to a better technology platform with more use cases covered by the product. Additionally, having more customers and brokers go through the platform gives Wefox additional leverage in negotiating rates with insurers. Since Wefox shares these discounts with its broker-partners, its a win-win for Wefox and the broker. And the 1000th broker likely got a lot more out of signing up to Wefox than the 100th broker did.

And here’s how the performance looks for a new broker on Wefox:

While private market valuations are a different beast from public equities, it is worth pointing out that Wefox raised a $125M series B at a valuation close to a $1B. This was on the back of $40M in 2018 revenues. More importantly, the company was working with 1500+ brokers and had 400,000 customer passing through its platform. And since the flywheel effect exists here, investors seem happy to fund this company despite the lofty valuation.

So network effects = ✅, Customers = 😊, Partners = 🤑 and Valuation = 🦄

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