HYPERSCALE PLATFORMS CAN MATCH SUPPLY AND DEMAND IN REAL TIME
Data and analytics are transforming the way markets connect sellers and buyers for many products and services. In some markets, each offering has critical variations, and the buyer prioritizes finding the right fit over the speed of the match. This is the case in real estate, for example, where buyers have strong preferences and finding exactly the right house is the priority. In others, the speed of the match is critical. “Hyperscale” digital platforms can use data and analytics to meet both types of needs.
These platforms have already set off major ripple effects in urban transportation, retail, and other areas. But that could be only the beginning. They could also transform energy markets by enabling smart grids to deliver distributed energy from many small producers. And they could make labor markets more efficient, altering the way employers and workers connect for both traditional jobs and independent work.
These platforms are already transforming the market for transportation
Hyperscale digital platforms can have notable impact in markets where demand and supply fluctuate frequently, where poor signaling mechanisms produce slow matches, or where supply-side assets are underutilized. These characteristics describe the status quo that prevailed in the taxi industry for many years before the arrival of Uber, Lyft, Didi Chuxing, and similar services. Conventional taxicabs relied on crude signaling mechanisms—literally, in this case, a would-be passenger attempting to wave down an empty cab in the street or calling a company’s dispatcher. These mechanisms created significant unmet demand. On the supply side, many cabs spent a large share of their time empty and cruising for passengers. Furthermore, most vehicles are underutilized; globally, most personally
owned cars are in use for approximately 5 to 10 percent of waking hours.61 Excess supply sometimes pooled in certain spots, while other areas went largely underserved. For several reasons, including heavy regulation and static pricing, taxi markets were and continue to be highly inefficient. These inefficiencies—combined with the fact that the speed of hailing is of primary importance—made the market ripe for a radically different model to take root.
That model combined digital platforms with location-based mapping technology to
instantly match would-be passengers with the driver in closest proximity. In addition, the location data can be analyzed at the aggregate level to monitor overall fluctuations in supply and demand. This allows for dynamic pricing adjustments, with price increases creating incentives for more drivers to work during periods of high demand. The platform nature of these services, which makes it easy for new drivers to join, unleashed flexible supply into the transportation market. Different types of mobility services have been launched, including not only ride sharing (such as Uber and Lyft) but also car sharing (Zipcar) and ride pooling (Lyft Line, UberPool).
From the outset, these platforms collected data from their user base to implement improvements—and as the user base grew, they generated even more data that the operators used to improve their predictive algorithms to offer better service. This feedback mechanism supported exponential growth. Uber, founded in 2009, is now in more than 500 cities and delivered its two billionth ride in the summer of 2016.62 Lyft reportedly hit almost 14 million monthly rides in July 2016.63 In China, ride-sharing giant Didi Chuxing now matches more than ten million rides daily.64 Today mobility services account for only about 4 percent of total miles traveled by passenger vehicles globally. Based on their growth momentum, this share could rise to more than 15 to 20 percent by 2030. This includes only real-time matching platforms and excludes the potential effects of autonomous vehicles.65
The changes taking place in urban transportation—including a substantial hit to the taxi industry—may be only the first stage of an even bigger wave of disruption caused by mobility services. These services are beginning to change the calculus of car ownership, particularly for urban residents. Exhibit 7 indicates that almost one-third of new car buyers living in urban areas in the United States (the segment who travel less than 3,500 miles per year) would come out ahead in their annual transportation costs by forgoing their purchase and relying instead on ride-sharing services. For them, the cost of purchasing, maintaining, and fueling a vehicle is greater than the cost of spending on ride-sharing services as needed.
If we compare car ownership to car sharing instead of ride sharing, around 70 percent of potential car buyers could benefit from forgoing their purchase. A future breakthrough that incorporates autonomous vehicles into these services, thereby reducing their operating costs, could increase this share to 90 percent of potential car buyers in urban settings.
These trends are beginning to reshape the structure of the overall transportation industry. Value is already shifting from physical assets to data, analytics, and platforms as well as high-margin services such as matching. This is even playing out within the car-sharing market itself, as Car2Go, Zipcar, and other firms that own fleets now face newer platform- based players such as Getaround. Hyperscale platforms will likely create concentrated markets, since network effects are crucial to their success.
Economic impact and disruption
Hyperscale, real-time matching in transportation has the potential to generate tremendous economic impact. Individual consumers stand to reap savings on car purchases, fuel,
and insurance by shifting to mobility services; they could also gain from having to spend less time looking for parking. Furthermore, the public will benefit from reduced real estate dedicated to parking, improved road safety, and reduced pollution. Summing these effects and assuming a 10 to 30 percent adoption rate of mobility services among low- mileage travelers, we estimate global economic impact in the range of $845 billion to some $2.5 trillion annually by 2025 (Exhibit 8).66 However, these shifts will create winners and losers. Some of the benefits will surely go to consumer surplus, while some will go to the providers of these platforms and mobility services.
