Winner Takes All: Case Studies in How Online Marketplaces Are Creating Modern Monopolies


The next is an excerpt from “Winner Takes All: Case Studies in How Online Marketplaces Are Creating Modern Monopolies” by Shirish Nadkarni.

In line with Wikipedia, “A network effect (also called network externality or demand-side economies of scale) is the effect described in economics and business that a further user of a very good or service has on the worth of that product to others. When a network effect is present, the worth of a services or products increases in response to the variety of others using it.” We’ve seen many examples of how powerful network effects may be in creating unstoppable, winner-take-all juggernauts. As we discussed earlier within the book, eBay was an early entrant within the auctions market within the US. Many later entrants like Yahoo and Amazon didn’t tackle eBay due to strong network effects it had established. The buyers on eBay got here since it had a critical mass of sellers and sellers got here since it had the biggest audience of buyers available. Unlike eBay which charged for listings, Yahoo and Amazon made the listings freed from charge. Even then, the sellers stuck with eBay’s platform as they saw more sales transpire through eBay. Similarly, corporations like Airbnb and Instacart have established dominant positions of their market due to strong network effects they’ve.

While network effects may be very powerful when the network has achieved a critical mass of users and suppliers, it is extremely hard to ascertain early network effects. Generally, marketplaces have gotten going by first acquiring a critical mass of suppliers e.g. as DoorDash did by signing up restaurants with a promise of making latest demand for these restaurants. One other strategy that’s sometime possible is to sell products to suppliers that provide value on their very own. A terrific example of this strategy is OpenTable which sold a customer and reservation management system to restaurants without promising that it could drive customer demand to those restaurants. Its only when OpenTable had signed up enough customers that it created its own destination site that drove reservations for its restaurant customer base.

Not all network effects are as strong or global as we have now seen with eBay and Airbnb. Some network effects may be fairly weak or localized. Let’s explore among the nuances in regards to the strength of network effects.


Multi-tenanting is practice of buyers and/or sellers to utilize multiple networks at the identical time. If it is straightforward to multi-tenant and to utilize multiple networks at the identical time, it becomes much harder to ascertain strong network effects. Food delivery apps are a very good example of a category where multi-tenanting is common. It is comparatively easy and advantageous for restaurants to participate on multiple platforms as they’ve done to this point. Similarly, it is straightforward for users to check out different food delivery apps especially as a lot of them were doing a “$10 off your first order” promotion to get users. Because of this, the food delivery market has not been a winner take all market with multiple major players like DoorDash, Grubhub and UberEats.

Multi-tenanting can be an issue within the ride sharing market. Many drivers go surfing concurrently to Uber and Lyft and take whoever provides a ride first. Because of this, each corporations have spent

an infinite amount of cash trying to amass drivers exclusively to their platform. Again, like food sharing apps, the ride sharing market has not been a winner take all market.

Local Network Effects

In lots of situations, the network effects will not be national or global in scope as we have now seen with eBay and Airbnb. The network effects need to be established market by market. OfferUp is a very good example of this phenomenon. It originally launched in Seattle where it proved that the model worked and was capable of raise funding that allowed it to expand to other markets. The corporate also kept a comparatively low profile within the press in order not to draw competitors in markets where it has not yet entered. Over time, OfferUp was capable of attract large funding rounds that enabled it to rapidly expand to all the important thing markets within the US before other players had the chance to enter these markets. Nonetheless, if multiple players had entered different local markets it could have been much harder for OfferUp to create nationwide network effects.

The identical phenomena applies internationally where you frequently see copycat competitors establish themselves before you’ve got a likelihood to enter that market. In lots of situations, the US company generally finally ends up acquiring a foreign competitor to enter the market if it determines that network effects within the local market are too hard to beat. Given the larger market size within the US and the better access to enterprise capital within the US, it becomes relatively easy for the US company to make the acquisition of a smaller foreign player.

Network Effects Throttling

Not all network effects show an ever-increasing strength because the variety of players increase. They will get throttled and even reverse depending on the product. For instance, with ride sharing there are initially strong network effects as drivers and users join the platform. Nonetheless, once the market has been saturated with drivers in order that the wait time is 5 minutes or less it doesn’t really help if more drivers join the platform. Because of this, within the US, Uber hasn’t been capable of kill off Lyft because it has also been successful in attracting enough drivers to its platform to ascertain a viable service within the US.

Similarly, Facebook is an awesome example of a network with very strong network effects which have dissipated and even reversed to some extent. Most teenagers and young adults not use Facebook and as a substitute use Snapchat as their social networking platform. The explanation is that with Facebook, the teenagers have been forced to just accept their parents, uncles/aunts and grandparents as friends. Teenagers will not be very comfortable sharing their private moments with their relatives whereas they’re way more prone to accomplish that on Snapchat which is especially populated with young teens.

Commoditized vs Differentiated Supply

It’s loads easier to ascertain strong network effects if you’ve got a differentiated supply versus a commoditized supply. Ride sharing is an example of commoditized supply where as a consumer you don’t really care who your driver is – you’re trusting the app to make the appropriate selection for you. Because of this, once the network reaches a certain level of liquidity (e.g., 5-minute wait times or less), the network effects not accrue further.

Then again, in the event you are providing an extended tail of differentiated supply as, for instance, with the Amazon marketplace or with Airbnb, the network effects proceed to accrue. The greater the availability, the more likely that there is powerful price cutting war among the many suppliers which makes the marketplace much more attractive to consumers. Today you’ll be able to go to the Amazon marketplace and find virtually any item making the marketplace value proposition very strong for consumers.


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