In my book and previous posts I build a broad case for the key role big data and AI play in next-generation mobility, and provide several examples from transportation and logistics. Next-generation mobility is about intelligent, connected vehicles that utilize some form of electrified propulsion, and on-demand shared transport services of people and goods that will be offered through such vehicles. Many of these vehicles will be capable of autonomous movement. Next-generation mobility will help us address some of our biggest challenges, such as pollution and climate change, urbanization and congestion, aging population, and traffic fatalities, while enabling us to maintain economic prosperity by operating highly optimized supply chains that span the globe. It will give rise to a new value chain where big data and AI will play a key role. It is therefore important to identify the new monetization opportunities enabled by big data and AI in the context of this value chain.
Despite the growing number of announcements about technological achievements, investments, partnerships and acquisitions relating to autonomous vehicles utilizing Level 4 or Level 5 driving automation, we are still several (5-10) years away from the broad utilization of such vehicles, particularly for passenger transportation. Many incumbent automotive OEMs envision that Level 4 and Level 5 autonomous vehicles that will be used for passenger transportation will be privately owned. However, as we will see in the next section, the majority of the use cases being tested today involve fleets of autonomous vehicles. For this reason, in this post I first examine the value that is added by big data and AI in every component of the fleet-based on-demand shared mobility value chain that I have previously defined. I conclude by identifying the monetization opportunities that big data and AI offer.
Six Use Cases For Next-Generation Mobility Vehicles
Based on my research I have identified six general use cases where autonomous vehicles can have transformative impact. The successful deployment of these use cases will depend on a number of different but highly interdependent factors that include:
- Creating the right technologies at reasonable costs and energy efficiency to enable autonomous vehicles to deal with environments of increasing complexity that can change unpredictably;
- Devising scalable business models;
- Building the appropriate transportation and electrification infrastructures;
- Instituting the appropriate regulations;
- Resolving liability issues;
- Addressing issues relating to cybersecurity and data privacy, and
- Achieving social acceptance of autonomous mobility, including the resolution of labor issues that will undoubtedly arise.
I have ordered the six use cases based on increasing complexity:
- Specialized vehicles operating in controlled environments. Corporations have already started deploying specialized autonomous vehicles, some of which may be electrified, in environments where employee injury risk is high and labor shortages exist. Initial examples involve hauling ore in open-pit mines, planting, maintaining crops and harvesting in farms, large warehouses, and transporting containers within ports. Trials, and even deployments, for additional specialized applications will continue in increasingly less controlled environments such as urban garbage collection.
- Trucks used in long-haul freight logistics. Several factors motivate the use of autonomous, and increasingly electrified, long-haul trucks for freight transportation between distribution centers that are outside city limits. These include: digital, global and highly optimized supply chains, shortage of long-haul truck drivers, fuel savings, driver safety, and supply chain economics. Utilizing freeways, and maybe even specially designated lanes in freeways, provides for environments that are more stable and consistent and less complex, compared to urban settings. The first significant tests for this case will start in 2018 and involve truck platooning.
- Vehicles used for short-haul package handling. The continued explosive growth of ecommerce is leading to the dramatic increase for package delivery and merchandise returns logistics. Logistics companies are looking for ways to effectively address last-mile on-demand delivery in urban and suburban environments. They are also exploring numerous different options involving autonomous vehicles most, if not all, of which will be electrified. Tests of such vehicles have started and more are planned for the near future.
- Passenger shuttles. Autonomous, electrified passenger shuttles will come in different forms. Today they are providing transportation in a variety of university campuses, and other controlled environments that have low traffic, and impose low speed restrictions. Additional trials are on the way, including trials in retirement communities. Over time such shuttles will be introduced in increasingly complex urban settings to provide, first-mile/last-mile transportation to private and public mass transit systems, as well as transportation for the elderly and people with disabilities. Though the technology employed by the special-purpose autonomous vehicles is impressive, the business cases and associated models, as well as the necessary regulation have not yet been fully defined. Remember the Segway. It employed an impressive array of technologies to address personal urban mobility but encountered several obstacles, including business model and regulation-related issues, in establishing a viable business case.
- Vehicles used for ride-hailing. Autonomous vehicles will provide numerous advantages to companies offering urban and medium-haul (up to 150 miles) ride-hailing and ride-sharing services. These include the ability to reduce operating costs, control the overall passenger experience, and reduce urban traffic congestion and pollution. While trials of growing scope will continue, the broad use of autonomous vehicles for such applications is several years away. These vehicles may not initially be electrified but ultimately they will evolve to be such.
- Vehicles used for private transportation. The growing proliferation of mobility services particularly those that blend on-demand with scheduled mobility (in the form of public and private mass transit) will negatively impact private car ownership but will not eliminate it. Automotive OEMs will definitely offer vehicles with Level 4 and Level 5 driving automation initially at their high-end models, and, as the price of the components that comprise the autonomous platform drops, to lower-price models that target broader segments of the market. Some of these vehicles may be shared.
These use cases lead me to two observations. First, two use cases (2-3) are about next-generation mobility for freight transportation. Three use cases (4-6) are about next-generation mobility for passenger transportation. Even as consumers continue to transition from vehicle ownership to vehicle access for transportation, autonomous vehicles will need to operate side-by-side with human-driven vehicles in streets and highways.
Second, five use cases (1-5) involve fleets rather than private vehicles. A case can be made on whether autonomous farming equipment will be privately owned or owned by fleet operating companies. The Fleet-Based On-Demand Shared Mobility value chain that I presented in previous posts can be employed in use cases 2-5. The new value chain will involve, impact, and potentially disrupt many industries beyond automotive. Industries such as energy, financial services, insurance, telco, utilities, and others are already being affected. Because of the previous observation, the new value chain will co-exist with the Vehicle Manufacturing and Sale, and Vehicle Use value chains that I presented in my book.
The “simpler” use cases will be deployed, and therefore monetized, before the more complex ones. The operators of specialized autonomous vehicles are already enjoying operating cost reductions from their use. Once deployed, truck platooning will enable the reduction of fleet operating cost reduction due to fuel savings. Eventually, the biggest monetization opportunities will come from the use of autonomous short-haul and long-haul trucks, as well as from passenger vehicles employed for ride-hailing and ride-sharing services where I will focus for the rest of this post.
The Value Added By Big Data and AI
The fleet-based on-demand personal mobility value chain consists of four basic components as shown in Figure 1.
Big data and AI add value in every component of this value chain by automating many tasks and enabling insights-based decisions. In order for these to be possible, it is necessary for the data flows shown in Figure 2 to exist among the components of the value chain.
Vehicle design, test, and manufacturing. Data from fleet creators, fleet operators, and fleet service and maintenance companies, can significant value to the data about vehicle and component manufacturing. The combined data can be analyzed using AI and used in order to improve vehicle designs and their overall manufacturing process.
Big data collected from test and production vehicles can be utilized by the machine learning systems that are incorporated in the Perception, Localization, and Planning software components of the Autonomous Vehicle Operation Platform to improve their performance. Such improvements lead to the transition from the “simpler” to the more complex of the six use cases described previously.
Simulation systems, such as those built by our portfolio company Metamoto, can fuse big data collected by fleet operators with simulated data and use AI. The fused data is used to test the performance of the predictive models incorporated in the various components of the Autonomous Vehicle Operating Platforms under a variety of “corner cases.” In this way these simulations help assess the performance of such platforms under conditions that may only be encountered infrequently but are important nonetheless in order to certify self-driving vehicles.
Fleet creation. Corporations that will create the vehicle fleets for on-demand shared mobility can realize important value by utilizing big data and AI. In particular, by analyzing
- The data about the performance of each vehicle in specific fleets along with the various conditions (weather, road, etc.) under which this performance was observed (provided by fleet operators),
- Vehicle configuration data for each vehicle in such fleets (provided by vehicle manufacturers and fleet operators), and
- Prior financial and insurance data pertaining to the acquisition of the fleets (which they presumably have)
they can identify insights about optimal terms for acquiring a new fleet, such as per vehicle price. Companies can use these insights in order to create a fleet that they will subsequently operate or lease to a different fleet operator.
Fleet operation. A lot of big data is generated by vehicle fleets. A few examples include:
- Demand-based pricing for each trip,
- Passenger profile data,
- Passenger wait times and travel times,
- Vehicle idle vs revenue-generating miles,
- In-cabin sensor data and vehicle sensor data,
- Data collected from transportation and other infrastructures,
- Weather and traffic conditions on routes taken.
Using such data, AI adds value in two important ways. First, by helping the fleet operator optimize the overall fleet’s and each vehicle’s financial, and operational performance by maximizing each vehicle’s uptime and revenue-miles. Second, by automatically configuring and personalizing each vehicle’s cabin for each passenger type, individual passenger, or group of passengers through the vehicle’s UX Platform thus enhancing the overall customer experience and increasing customer loyalty. In next-generation mobility, customer experience extends outside the vehicle as well. Data-driven AI can be used for the creation of end-to-end personalized ground transportation solutions. This is a new and potentially differentiated services opportunity for fleet operators offering on-demand shared mobility. Our portfolio company Safegraph is developing such solutions.
Fleet management, service, and maintenance. Vehicle performance data, such as fuel consumption and tire performance, vehicle service, maintenance and repair records, together with data about the trips each vehicle in a fleet is fulfilling, the accidents and breakdowns it is involved, can be used by AI systems in order to optimize the vehicle’s maintenance and service. Such optimizations increase a vehicle’s uptime, resulting in higher revenue-miles, improve the overall customer experience, and enable the fleet operator to better control the fleet’s operating costs. Airlines provide a very good example of what is possible by continuously collecting such data.
The Monetization Opportunities Of Big Data and AI
My firm invests in startups developing next-generation mobility solutions. I’ve been particularly interested on how to monetize the value provided by big data and AI. As with every value chain, monetization in fleet-based on-demand shared mobility value chain monetization will result from the creation of new revenue streams but also from lowering operating costs across the value chain. I have identified several such opportunities across this value chain.
Vehicle design, test, and manufacturing. Coupling data-driven AI with 3D printing will lower the automakers’ manufacturing costs by optimizing material selection and component designs. The application of predictive maintenance analytics to part failures will lower the manufacturer’s warranty costs. Knowing which parts fail and why allows for better designs and few recalls. It could also result in more effective marketing campaigns to fleet owners, fleet operators, as well as to consumers who will buy such vehicles for their private use.
The Autonomous Vehicle Operating Platform must be constantly updated and extended to enable the vehicle to operate in new situations and environments. The same is the case for the UX Platform. The extensibility of these platforms provides a continuous data monetization opportunity to automakers, component suppliers, and cities. Fleet operators will subscribe to receive over-the-air (OTA) updates. Using ecommerce transactions they will also acquire new features or information from the automaker, a platform provider, data providers, e.g., HERE, or a network of other partners. For example, using OTA updates in conjunction with subscription- and transaction-based business models Tesla is monetizing the introduction of new features to its privately-owned vehicles, e.g., battery range extenders.
Fleet creation. By analyzing each vehicle’s usage data, past performance, service records, and maintenance data, fleet owners may be able to negotiate lower per vehicle prices with manufacturers, and lower premiums with insurers. In this way they will positively impact their overall fleet investments. The same analyses can also help fleet owners determine the terms under which they will lease vehicles to a fleet operator in order to maximize their revenue opportunity while reducing their risk. For example, if the fleet owners detect that cars are frequently traversing hazardous areas, they may charge a premium to cover risks to the fleet operator. Conversely, if the fleet has a low accident rate, the owner may offer discounts in future leases.
Fleet operation. Big data and AI offer several revenue-generation and cost-reduction opportunities to fleet operators. The key to realizing these opportunities is to understand how next-generation mobility changes Brand Loyalty and enables Passenger Commerce.
Brand loyalty has always been important to monetization. It impacts revenue and costs. This has always been understood by incumbent automotive OEMs and to this day it is one of their remaining assets. Under the car ownership-centric model automakers measure brand loyalty by their ability to protect their market share. They achieve this by retaining a consumer, or household, within their brands, and ideally, over time, transition them from the entry-level to their more expensive brands. This is the reason each automaker owns several vehicle brands. For example, in the case of Toyota this may mean moving a household that initially purchases, or leases, a Corolla to later leasing a Camry and eventually a Lexus. Under these scenarios brand loyalty is measured every 3-10 years depending on how frequently the household changes vehicles. During the intervening years the automotive OEM, at best, interacts infrequently with the household’s members. In fact, the OEM’s dealers interact more frequently with household members particularly during the manufacturer’s warranty period, building a richer relationship with them.
The transition from the car ownership-centric model to the hybrid model that combines car ownership with on-demand shared transport services will change the prevailing notion of brand loyalty. Loyalty will not be measured by the willingness to continue purchasing a particular OEM’s vehicles, but will be measured by the miles traveled using an OEM’s vehicles and a fleet operator’s service. The emerging transportation model combined with the new way of measuring brand loyalty will mean that the OEM’s value delivery doesn’t stop with the sale of a vehicle, as it does today, but becomes a lifetime journey with the customer. This journey includes transportation using a personally-owned vehicle, a fleet operator’s transport services, as well as other multi-modal transportation. The new model will pit the OEM against the fleet operator for each consumer’s loyalty. The ongoing challenge for the OEM and fleet operators will be what share of the customer’s revenue-miles traveled they have at any point in time, which will translate to share of wallet. Data can play a huge role in understanding loyalty, influencing it and monetizing it. AI-driven customer acquisition, retention, and upsell campaigns will be used extensively to increase the revenue-miles traveled by each consumer.
The new loyalty model will be monetized not only by the revenue-miles traveled but also through Passenger Commerce. Passenger Commerce refers to the ecommerce performed by the passenger of autonomous vehicles while being transported. It can be conducted through the UX Platform or a mobile device via:
Subscriptions to access content, e.g., an annual subscription to Apple Music, or a particular service, e.g., entertainment concierge.
Transactions for the purchase of goods, services, and content. For example, a traffic data provider, e.g., Inrix, can offer congestion data about a particular city on a particular day. An early example of such transaction-based commerce today is offered by Hertz for its Neverlost GPS and travel information platform. Hertz is able to charge extra for the vehicles that are equipped with this platform because of the information it provides to driver and passengers.
Advertising in exchange for free access to content, goods, and services. For example, rides to certain establishments, e.g., a specific restaurant or casino can be sponsored by the establishment itself.
Redemption of loyalty points. Automakers and fleet operators can reward their customers for their loyalty using a system similar to that used by airlines or hotel chains. These points can then be redeemed in much the same way these and other industries use such programs. For example, for every 5,000 ridesharing miles in a particular automaker’s vehicles, the consumer receives points that are redeemed towards a free meal from a particular food chain.
Fleet management, service, and maintenance. By analyzing the combined fleet utilization and per vehicle fleet management, service, and maintenance data we can predict how long to keep each vehicle in operation before bringing it in for maintenance. The optimization of maintenance results in better cost controls and better vehicle performance per location and even ride for both the fleet’s maintainer and its operator. For example, because of the difference in the weather and road conditions, operating an autonomous vehicle in New York will be different than in San Francisco, or in Singapore. The same data can also be analyzed to improve the fleet operator’s overall economics.
The already established automotive business models continue to face margin compression and declining value. The models used today by companies offering ride-hailing and ride-sharing services are not offering better margins. By understanding the value of big data and AI in each component of the fleet-based on-demand shared mobility value chain as this will be utilized in the various autonomous vehicle use cases, we can identify several monetization opportunities that can result in higher-margin business models.