I blogged recently about the economics of Uber and other ride hailing services. Further light has been shed by the excellent new book from Andrew McAfee and Erik Brynjolfsson of the Massachusetts Institute of Technology: Machine, Platform, Crowd. From this I gleaned the thoughts below.
Network effects have long been recognised: some services become more valuable to each user as more people use them. The telephone is the historic example, WhatsApp a recent instance. Network effects reflect demand-side economies of scale, where benefits to users, the source of demand, grow as the scale increases (contrasted with supply-side economies of scale where costs fall as scale increases).
Two-sided digital platforms serve to match supply with demand. The most successful take advantage of network effects to become powerful aggregators of both supply and demand. They are early to the business space and pay a great deal of attention to user interfaces and experience, since users may be unwilling to employ more than one or two competing platforms.
In the transport sector, we are concerned with platforms that function ‘online to offline’ – digital access to physical mobility. Examples: Uber, Lyft, Gett and other taxi services, BlaBlaCar and Liftshare for ride sharing, car clubs, dockless bike hire by app, online rail ticketing. As well as matching users with services, the platforms optimise operations, for instance selecting the fastest routes and predicting the location of future demand. The negligible cost of digital scaling means that these platforms can handle huge volumes of information – about user preferences, availability and price of services, payments etc.
In the past such data handling would have been limited to large organisations. Now, the availability of cloud computing with unlimited amounts of capacity helps innovators enter the market, scale rapidly and compete aggressively.
Demand side economies of scale can grow much faster than costs. However, the main challenge for digital platforms arises because the supply side involves physical plant and infrastructure whose capacity is finite, hence capacity, a perishable commodity, must be carefully managed. An important tool is revenue management, pioneered by the budget airlines, where varying price is used to match supply with demand – which needs lots of data and lots of supply and demand to run well.
As well as benefiting from network effects, digital platforms can reduce information asymmetries that inhibit transactions, such as whether you can trust your taxi driver, particularly in an unfamiliar city. Uber asks both customers and drivers to rate each other after each transaction, which allows poor performers to be dropped and increases confidence in quality of service.
Operators of two-sided platforms typically prefer lower prices than their providers of service. The maximum revenue of a taxi service arises at low fares, given the price elasticity of demand. However, two-sided platforms have to satisfy both providers and users. Lower fares increase demand, which will attract more providers onto the system, a benefit to providers. But lower fares also mean less income to drivers.
There is a belief that two-sided platforms for taxis offer network demand side economies of scale such that the biggest platform will dominate each local market. Patient capital to support growth of the market will reduce the marginal costs of arranging a ride, to yield attractive returns to investors.
Analysis of the economics of transport digital platforms is at an early stage. A key question is whether scale economies would tend to result in monopoly, or whether competition would arise on account of low barriers to entry and a gig workforce open to recruitment by the offer of better terms.