This post is original my learning diary in one of my Master courses in Information and Service Management program. It is my own thought that may consist some errors.
The prevalence of platform mediated network
Nowadays most famous brands and digital economy are dominated by platform. I used to think that Platform Mediated Network is only technology facilitated and the platform model is rather new. However, platform has been existed for quite a long time, and we can conceptualize many brick-and-mortar businesses as platforms too. And somehow I see that unions such as real estate unions, agriculture unions, or even student unions, are not only there to support their members (brokers, farmers, students respectively), but also to connect their members to external parties (property owners and buyers, farming goods companies, future employers respectively). Thus those unions can be conceptualized as non-technical platforms as well.
The growth of Chinese platform companies
Another interesting information from the lecture is a rapid growth of Chinese platform companies. The drive is not just from a market of 1.4 billion consumers. One fascinating drive is the strategy and the technical ability of Chinese platform companies to expand themselves and provide more functionality to users. Users can perform almost everything they need in the platforms, for example WeChat users can set up a store right on the application, and buyers can purchase directly on WeChat. It’s mind-blowing to see that all successful Chinese platforms are multiplatform bundles, such as TaoBao, WeChat, AliBaba… Or even in a nascent market like CryptoCurrency exchange, a well known Chinese platform called Binance is actually multiplatform bundle. It acts as CryptoCurrency exchange, as well as ICO audit, invest, and also issuer.
Platform characteristics
Platform can be categorized into one-sided, two-sided, or multi-sided. One-sided platform is where users play several roles at the same time, e.g. email or messages platform. Two-sided platform, on the other hand, is where users can only act one role at a time, e.g. a user can be rider but not driver at the same time. I noted one interesting point that, these concept definitions, however, are not strictly set and black and white like mathematics. They serve more for discussion and negotiation to agreement and to be on the same page.
The first and utmost distinguished characteristic of platform is its network effects, a crucial concept that can explain the success or failure of one platform. This effect can be same side or cross side, positive or negative. Platform is also a combination (nexus) of rules and architecture. It is open to everyone to join under a certain set of rules and guideline. However there are some platforms that leverage “Exclusive Access” to differentiate themselves. One case is Dribbble, a platform for designers to showcase their work and get hired from recruiters and companies. Everyone can register as “new” user but can not showcase their works until received “Invitation” from an “old” user. Or in the beginning of Facebook, it only allowed Ivy League students to join, rather than opened to students from all universities.
Winner-take-all (WTA) or the Abba Doctrine
The conditions for a networked market be served by one single Winner-Take-All platform are:
- Multi-homing cost are high, which means users can not use many same platforms, e.g it is pretty hard to use all iOS, Android, and Windows Phone at once. On the other hand, when multi-homing cost are low, a user can own and use several platforms easily and cheaply, for example a rider can use service from Grab, Lift, and Uber at the same time to search for the cheapest fare.
- The network effects are strong and positive so that the Winner can keep amassing more users, like the case of Airbnb, Facebook, …
- The demand from users for different features are relatively low.
Example of WTA market:
Besides the market of DVD having WTA dynamics (Thomas Eisenmann 2016), the market of Internet search also has the WTA dynamics because
- The cost of inconvenience to use multiple browsers and search engines are high to the end users
- The more users use a search engine, the more data the engine will gather and analyze to provide better search results, then more websites want to be crawled by the engine
- Users do not demand anything more than receiving the search results relevant and quick.
When a single platform appeared as WTA and soon become monopoly, the authority may step in. In some cases, users will take charge, e.g. teenagers refuse to use Facebook and instead find Snapchat “cooler” and not “mainstream”.
Multi-homing cost is essential for Winner Take All
Multi-homing cost is the cost of using more than one platform at the same time for the same purpose. In a sector where multi-homing cost is high, there is higher chance that one platform will be the WTA because after homing, users are less likely to use competitor platforms. As I noticed, multi-homing cost is proportional to switching cost. High multi-homing cost deters the switching process, which causes the switching cost even higher. In a low multi-homing cost market such as P2P shared driving, the switching cost is basically zero. Drivers can easily switch back and forth between Uber, GrabTaxi, and Lift by just one tap to open the mobile app. Riders can also multi-home to several platforms and choose the ride that cost less to them.
Switching cost — only as a barrier?
Switch cost is the cost of changing from one platform to another, or from one product/service to another, that the users will incur. This cost is not limited to only monetary cost, but instead it can be friction (mental) cost of new learning curve or metal frustration. Switching cost also includes hidden cost of time for exiting the old platform, setting up for the new platform, or migrating data from the old to the new platform if needed. The higher switching cost is, the less likely that users will change platform providers. For example, changing to a totally new blogging platform requires users to migrate old blog posts and to start attracting readers again.
However switching cost is not just a barrier. Newcomer can exploit territory by actively reducing this cost. For example, in its beginning, Airbnb hacked into Craiglist so that users could list their houses on Airbnb and then the bot automatically surpassed security and posted on Craiglist. Therefore, Craiglist users could easily switch to Airbnb yet still multi-home in Craiglist. This piggyback approach was efficient for new small platform to exploit the big one (Geoffrey G. Parker 2016).
Fund Racing Strategy — When “Get Big Fast” by pumping fund is counterproductive?
Racing to acquire users faster than competitor is common in platform competition. For example in P2P shared driving, Uber, GrabTaxi, and Lift all try to subsidize both drivers and riders by discounting heavily to acquire more users and increase positive network effect. However in a low switching cost market along with poor network effect, racing by discount is very inefficient because customers not only can switch to whom offers the highest discount, but also do not care much about how large the customer base is. For example in the Dot.com era, there were multiple pet supply platforms competed to be the WTA and tried to “get big fast”. Unfortunately, customers prefered discount price and did not care whether their friends/neighbors get pet foods from other platforms. They could easily multi-home to several platforms and buy from the heaviest-discounted pet supplier. Although the pet companies spent huge amount of money in advertisement, free delivery, and price discount, they still failed right when the Dot.com burst. The cost of acquiring new customer was certainly one cause to the failure.
Although in P2P driving market the switching cost is rather low, the network effect is strong when a larger number of riders can attract more drivers to join and vice versa. This explained why companies like Uber still offers heavy subsidy when entering new territories or occasionally gives away in current territories.
Works Cited
Thomas Eisenmann, Geoffrey Parker, and Marshall W. Van Alstyne. “Strategies for two-sided markets.” 2016.
