McKinsey estimates $2.5 Trillion in potential economic impact from continued adoption of mobility services, by 2025

From  Their December 2016 report compares results with 2011 and looks forward

Hyperscale, real-time matching is a disruptive model that will influence transportation and logistics, automotive development, and smart cities and infrastructure.

Indicators of potential for disruption:
▪ Assets are underutilized due to inefficient signaling
▪ Supply/demand mismatch
▪ Dependence on large amounts of personalized data
▪ Data is siloed or fragmented
▪ Large value in combining data from multiple sources
▪ R&D is core to the business model
▪ Decision making is subject to human biases
▪ Speed of decision making limited by human constraints
▪ Large value associated with improving accuracy of prediction

Digital platforms provide marketplaces that connect sellers and buyers for many products and services. Some platform operators using data and analytics to do this in real time and on an unprecedented scale—and this can be transformative in markets where supply and demand matching has been inefficient (or haven’t been occurring yet).

In personal transportation, ride-sharing services use geospatial mapping technology to collect crucial data about the precise location of passengers and available drivers in real time. The introduction of this new type of data enabled efficient and instant matching is a crucial innovation in this market. In addition, the data can be analyzed at the aggregate level for dynamic pricing adjustments to help supply and demand adjust.

The typical personally owned car is estimated to sit idle 85 to 95 percent of the time, making it a hugely underutilized asset. Platforms such as Uber, Lyft, and Chinese ride-sharing giant Didi Chuxing have been able to expand rapidly without acquiring huge fleets themselves, making
it easy for new drivers to put their own underutilized assets to work.

The speed of hailing is of primary importance and 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.(“Shared mobility on the road of the future,” Morgan Stanley Research,

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.

McKinsey estimates that by 2030 mobility services, such as ride sharing and car sharing, could account for more than 15 to 20 percent of total passenger vehicle miles globally. This growth—and the resulting hit to the taxi industry—may be only a hint of what is to come. Automakers are the biggest question mark. While sales will likely continue to grow in absolute numbers, MGI estimates that the shift toward mobility services could halve the growth rate of global vehicle sales by 2030. Consumers could save on car purchases, fuel, and parking. If mobility services attain 10 to 30 percent adoption among low-mileage urban vehicle users, the ensuing economic impact could reach $845 billion to some $2.5 trillion globally by 2025.  Some of this value will surely go to consumer surplus, while some will go to the providers of these platforms and mobility services. (See graphs on pp. 58-59)  McKinsey estimates up to $1 trillion in potential consumer savings from adopting mobility services rather than purchasing vehicles.  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. 

The largest part of this impact stems from consumers shifting away from car ownership. By moving to mobility services, the average global consumer is likely to gain around $2,000 annually in cost savings over purchasing and maintaining a vehicle. With around 1.6 billion active vehicles projected by 2025, even 10 to 30 percent adoption of mobility services among low-mileage vehicle users can lead to $330 billion to $1 trillion in savings annually (this includes about $140 billion to $430 billion potentially realized in developed regions and $190 billion to $570 billion in developing regions).

In addition to direct car ownership costs, the shift toward mobility services will generate significant savings in related costs like parking. Consumers are projected to spend $3.3 trillion on parking services in 2025, but the use of mobility services could allow them to save $330 billion to $990 billion. Around $220 billion to $650 billion of this could be realized in developed countries and $110 billion to $340 billion in developing countries. There is an additional benefit from the reduced demand for driving and parking. If 15 to 30 percent of drivers on the road in cities are looking for parking, this is a major logistical challenge in dense urban cores. By boosting the utilization of each vehicle, mobility services can decrease demand for parking and help reduce congestion, which creates further positive ripple effects on mobility and time saved. The reduced search for parking can generate a time-saving effect due to reduced congestion that can be valued at $10 billion to $20 billion as well as fuel savings in the range of an additional $20 billion to $60 billion.

Meanwhile, the shift to mobility services can improve productivity. Each day, workers spend 50 minutes in driving commutes on average in both developed and developing countries.67 If even half of that time can be used more productively for work, mobility services could generate an additional $100 billion to $290 billion in potential benefit.

Public benefits will include reduced real estate dedicated to parking, improved road safety, and reduced pollution.

Ride sharing can improve road safety by creating a more viable option that keeps people from getting behind the wheel when they have been drinking, they are excessively tired, or they have other impairments (such as difficulties with night vision). Traffic accidents result in about 1.25 million deaths globally per year, with millions more sustaining serious injuries.68 One study found that ride-sharing services have reduced accidents by an average of 6 percent.69 Another found a 4 to 6 percent reduction specifically in drunk driving fatalities.70 We estimate that reduced accident rates due to the expansion of digital mobility services could save $50 billion to $160 billion in economic terms—not to mention the
incalculable value of reducing the human toll of accidents.

Beyond their effect on traditional taxi services, mobility services could have wider impact. Automakers are the biggest question mark as the calculus of car ownership changes, particularly for urban residents. While sales will likely continue to grow in absolute numbers, the shift toward mobility services could potentially halve the growth rate of global vehicle sales by 2030 (Exhibit 9).71 In response, car manufacturers will likely need to diversify and lessen their reliance on traditional car sales. Many appear to be preparing for this future; partnerships are forming between traditional automakers and mobility service providers or other high-tech firms.

Autonomous vehicles could accelerate this wave of change. When self-driving cars are added into the equation, supply and demand matching could improve even further since these vehicles can have higher utilization rates. Car pooling may increase, and the cost of urban transportation could plummet.

On the flip side, the demand for car purchases could fall further, and many people who make a living as drivers (nearly two million in the United States alone with the majority being truck drivers) could
be displaced.

The role of data and analytics in transportation is not limited to urban centers. It can also improve the efficiency of trucking routes and handoffs in the logistics industry. Rivigo has applied mapping technology and algorithms to improve logistics efficiency in parts of India.

In energy markets, for instance, demand can fluctuate dramatically and frequently by time
and by region. The current energy grid is ill-equipped to smooth out the spikes in peak
demand with excess off-peak supply. But wider deployment of smart grid technology can
address this inefficiency by using new sensor data to generate more dynamic matching of
supply and demand, in part by allowing small, private energy producers (even individual
homeowners) to sell excess capacity back to the grid. This technology is developing quickly:
the United States alone has committed more than $9 billion in public and private funds
toward smart grid technology since 2010.73 In the Netherlands, some startups are using the
peer-to-peer model to match individual households directly with small providers (such as
farmers) who produce excess energy. Vandebron, for instance, charges a fixed subscription
fee to connect consumers with renewable energy providers; in 2016, this service provided
electricity to about 80,000 Dutch households.
The markets for certain types of short-term labor services are also being redefined. Driving
passengers is only one of the many types of services now being offered through digital
marketplaces. Others include household chores and errands, data entry, and simple coding

Recent research from MGI has found that already some 15 percent of
independent workers in the United States and Europe have used digital platforms to earn
income.75 The non-profit Samasource is seeking to bridge this market gap by breaking
down larger digital projects into smaller discrete tasks that can be handled by remote
workers in developing countries. As of 2016, almost 8,000 workers participated on this
platform, increasing their earnings by more than three-and-a-half times.76

76 Company website.
77 A labor market that works: Connecting talent with opportunity in the digital age, McKinsey Global Institute, June 2015; and Independent work: Choice, necessity, and the gig economy, McKinsey Global Institute, October 2016.

Faster decision making: Smart cities

Automating complex decision making could enable smart cities to become more efficient and better able to respond to changing environments in real time. Transportation and utilities in particular are two areas of urban management in which rapid decision making is crucial.

Smart transportation systems utilizing the IoT and automated algorithms can enable more seamless traffic flows, reduce waits on public transportation systems, and make parking availability fully transparent to prevent circling and congestion. Some cities have begun to deploy technologies that can produce these benefits. In Singapore, sensor data is used to predict traffic congestion in real time and adjust tolls to limit jams. In Copenhagen, road sensors detect approaching cyclists and turn signals green.

McKinsey estimated the size of the economic potential associated with a selection of analytics
applications in smart cities around the world.(The internet of things: Mapping the value beyond the hype, McKinsey Global Institute, June 2015) In transportation management, we find that a number of uses could produce $200 billion to $500 billion in economic impact through reducing traffic congestion by about 10-20%. These include centralized and adaptive traffic control, smart parking meters and pricing, schedule management of buses and trains, congestion pricing in express lanes on roads, and monitoring public transit for maintenance needs. (McKinsey, Age of Analytics, p. 77)



Data and analytics also can reveal finer levels of distinctions, and one of the most powerful uses is micro-segmenting a population based on the characteristics of individuals.

p. 24:

There is a 40x increase in processing power between fastest supercomputer of 2010 and the fastest today (which is in China).  Meanwhile, the amount of computing power each dollar can buy has increased by a factor of ten roughly every four years in the past quarter century, making cheap computing more available than ever before.21 Continued investment is pushing the boundaries of these

As computation capacity and data storage alike have largely been outsourced, many tools have become accessible, and data can now be more easily combined across sources. NoSQL databases offer alternatives to relational databases, allowing for the collection and storage of various types of unstructured data, such as images, text, audio, and other rich media. Data storage costs have fallen dramatically over the years. But now the increasing firehose of data streams simply exceeds the amount of storage that exists in the world.

Luke Muehlhauser and Lila Rieber, “Exponential and non-exponential trends in information technology,” Machine Intelligence Research Institute blog, May 12, 2014.
Top500 list of world’s supercomputers, June 2016, available at

Firms aggregating data from different sources can capture value for a few reasons.  First, they can serve as a “one-stop shop” for data from multiple sources. Second, data aggregation adds value because combined data may yield better insights. Benchmarking the performance of multiple entities may help identify areas for improvement, for example. Risk assessments are more accurate when they incorporate multiple pieces of behavioral and environmental data. Combining mobile and desktop browsing behavior offers a more complete picture of a customer’s consumption patterns. Going back to our credit example, a consumer’s history of timely mortgage payments is more valuable when joined with data on how that same consumer handles credit cards. Third, third-party aggregation can be useful in circumstances where there is a collective action problem among competitors. Aggregation can produce significant value, but it is becoming easier for users to perform many aspects of this function themselves. There has been robust growth in new software services for organizing data from different internal and external sources; this niche has
attracted significant venture capital. End-users now have cheaper and more powerful tools to aggregate data on their own.

The value of aggregation therefore seems likely to increase only in cases where integrating data from various sources is challenging. In these cases, aggregators may find that the information they synthesize can be sold to a broad range of customers. Location data, for example, is huge and noisy, and data that are validated from multiple sources may be more valuable to customers across industries that want to deliver targeted advertising and offers. Aggregating location data, as well as other very large data sets such as those taken from sensors, will be an increasingly valuable function.

Aggregation services are particularly valuable when combining and processing data are technically difficult, or when coordinating access across diverse sources is a barrier. This can be a complex process even if the underlying data are commoditized (as with financial market data) or when they are highly varied and differentiated (as with health records). Many traditional marketing data providers (such mailing list vendors) and information services providers (such as Westlaw, Bloomberg, and Argus) fall into this category and have developed long-standing relationships with data collectors or have technical assets that enable aggregation. Many of these aggregators also serve as “data guides,” using their deep understanding of complex data environments and privacy regulations to advise their clients on how to best handle the data.


In 2011, the McKinsey Global Institute (MGI) studied big data in five domains—healthcare in the United States, the public sector in Europe, retail in the United States, and manufacturing and personal-location data globally. Big data can generate value in each. For example, a retailer using big data to the full could increase its operating margin by more than 60 percent. Harnessing big data in the public sector has enormous potential, too.

If US healthcare were to use big data creatively and effectively to drive efficiency and quality, the sector could create more than $300 billion in value every year. Two-thirds of that would be in the form of reducing US healthcare expenditure by about 8 percent.

In the developed economies of Europe, government administrators could save more than €100 billion ($149 billion) in operational efficiency improvements alone by using big data, not including using big data to reduce fraud and errors and boost the collection of tax revenues. And users of services enabled by personal-location data could capture $600 billion in consumer surplus.

The research offered seven key insights.

1. Data have swept into every industry and business function and are now an important factor of production, alongside labor and capital. We estimate that, by 2009, nearly all sectors in the US economy had at least an average of 200 terabytes of stored data (twice the size of US retailer Wal-Mart’s data warehouse in 1999) per company with more than 1,000 employees.

2. There are five broad ways in which using big data can create value.

First, big data can unlock significant value by making information transparent and usable at much higher frequency.

Second, as organizations create and store more transactional data in digital form, they can collect more accurate and detailed performance information on everything from product inventories to sick days, and therefore expose variability and boost performance. Leading companies are using data collection and analysis to conduct controlled experiments to make better management decisions; others are using data for basic low-frequency forecasting to high-frequency nowcasting to adjust their business levers just in time.

Third, big data allows ever-narrower segmentation of customers and therefore much more precisely tailored products or services. Fourth, sophisticated analytics can substantially improve decision-making. Finally, big data can be used to improve the development of the next generation of products and services. For instance, manufacturers are using data obtained from sensors embedded in products to create innovative after-sales service offerings such as proactive maintenance (preventive measures that take place before a failure occurs or is even noticed).

In their 2016 report, The Age of Analytics, MGI said:

Data and analytics capabilities have made a leap forward in recent years. The volume of available data has grown exponentially, more sophisticated algorithms have been developed, and computational power and storage have steadily improved. The convergence of these trends is fueling rapid technology advances and business disruptions.

ƒ Most companies are capturing only a fraction of the potential value from data and analytics. Our 2011
report estimated this potential in five domains; revisiting them today shows a great deal of value still on
the table. The greatest progress has occurred in location-based services and in retail, both areas with
digital native competitors. In contrast, manufacturing, the public sector, and health care have captured
less than 30 percent of the potential value we highlighted five years ago. Further, new opportunities
have arisen since 2011, making the gap between the leaders and laggards even bigger.

The biggest barriers companies face in extracting value from data and analytics are organizational;
many struggle to incorporate data-driven insights into day-to-day business processes. Another challenge is attracting and retaining the right talent—not only data scientists but business translators who combine data savvy with industry and functional expertise.

Data and analytics are changing the basis of competition. Leading companies are using their
capabilities not only to improve their core operations but to launch entirely new business models. The
network effects of digital platforms are creating a winner-take-most dynamic in some markets.

Data is now a critical corporate asset. It comes from the web, billions of phones, sensors, payment systems, cameras, and a huge array of other sources—and its value is tied to its ultimate use. While data itself will become increasingly commoditized, value is likely to accrue to the owners of scarce data, to players that aggregate data in unique ways, and especially to providers of valuable analytics.

Data and analytics underpin several disruptive models. Introducing new types of data sets
(“orthogonal data”) can disrupt industries, and massive data integration capabilities can break through organizational and technological silos, enabling new insights and models. Hyperscale digital platforms can match buyers and sellers in real time, transforming inefficient markets. Granular data can be used to personalize products and services. Above all, data and analytics can enable faster and more evidence-based decision making.

Recent advances in machine learning can be used to solve a tremendous variety of problems—and deep learning is pushing the boundaries even further. Systems enabled by machine learning can provide customer service, manage logistics, analyze medical records, or even write news stories. The value potential is everywhere, even in industries that have been slow to digitize. These technologies could generate productivity gains and an improved quality of life—along with job losses and other disruptions.

Previous MGI research found that 45 percent of work activities could potentially be automated by currently demonstrated technologies; machine learning can be an enabling technology for the automation of 80 percent of those activities. Breakthroughs in natural language processing could expand that impact even further.

Data and analytics are already shaking up multiple industries, and the effects will only become more
pronounced as adoption reaches critical mass. An even bigger wave of change is looming on the horizon
as deep learning reaches maturity, giving machines unprecedented capabilities to think, problem-solve,
and understand language. Organizations that are able to harness these capabilities effectively will be able
to create significant value and differentiate themselves, while others will find themselves increasingly at
a disadvantage.