Tesla: Automaker or Data Company?Add bookmark
Tesla has had an interesting year and investors (along with the rest of the world) are taking notice.
While just one year ago, the company posted $907 million in net losses for the first nine months of 2019. According to Tesla’s Q3 financial statements, it’s posted profits of $566 million and more than doubled cash flow. In addition, in just the past year, the price of Tesla stock has ballooned from a bit under $100 per share to over $400 per share. Tesla’s current, though highly controversial, valuation stands at $400 billion, more than the combined value of Japan’s 7 automakers.
Though Tesla is selling more vehicles, energy solutions and, yes, tequila than ever before, this is not the only reason why the company’s valuation is increasing. But rather it’s Tesla's unparalleled use of data.
From the very beginning, Tesla has prioritized the collection and utilization of all possible data analytics from their car owners. In fact, since 2018, Tesla has collected over 3 billion miles of real world driving data, significantly more than the 20 million collected by its closest competitor.
With Tesla’s approach, every human interaction with the vehicle (i.e. wheel turn, hitting the brakes) generates a data point that is analyzed and used to improve or create new algorithms which are sent back down to the vehicle via over the air updates. So, in essences, “Tesla owners are not just driving a car to commute to work or run errands. They are simultaneously training the Tesla AI/ML engines as they go,” according to George Paolini of CIO Magazine. As a result, Telsa has created “one of the most effective crowd-sourced AI/ML training initiatives around today.”
The most high profile use of this data is the potential development and deployment of autonomous vehicles. Though we won’t get too much into the weeds about how autonomous vehicles work here, AV technology requires a reliable stream of data about what is around the vehicle and what it should be expecting down the line. In other words, autonomous vehicles use data to “see” and make decisions about what to do next.
In late October, Tesla released a beta version of it’s first fully self-driving system. Based on the data collected in previous versions of its Autopilot technology, they extensively re-engineered the product to improve 4D data continuity and trajectory projection. Though the jury is literally still out on the quality of this more advanced driver assistance program, it does represent one interesting way Tesla is using its extensive collection of driver data to advance self-driving technology.
One of the other major factors fueling Tesla stock prices is their innovative approach to monetizing its 3 billion miles worth of driver data. For example, this past July, Tesla CEO, Elon Musk, announced it will be building a “major insurance company” that would use driver data to determine payment rates based on how aggressively or safely a person drove. It could also leverage the massive amounts of comprehensive driver data it has to predict and warn drivers of potential driving risks. For example, if a road is especially slippery in rainy conditions or alert the driver to get off the phone if an upcoming turn requires increased attention.
"We believe Tesla's venture into insurance is part of a larger trend towards leveraging advanced data to enable insurers to build better risk models, thereby lowering both premiums and loss ratios," Asad Hussain, an analyst at PitchBook Mobility, told Business Insider in a recent article. "Data is increasingly a focal point for insurers as it enables rate adjustment based on behavioral metrics such as driver phone use and propensity for heavy braking."
Another area of data monetization with high potential is hyper-targeted advertising. Tesla not only records human-car interactions and where a person travels to, but also records in-car conversations, texts and calls. Using this information, Tesla could easily structure and categorize this data to understand drivers' travel and consumption habits. Using these insights, they could display paid ads on the vehicles infotainment system or alert the driver to nearby places of interest via text, mobile app, etc.
Last but certainly not least, this massive amount of driver data helps Telsa create new services and products designed around customer needs. For example, the robustness of Tesla’s analytics capabilities allow them to identify performance issues often before the customer does. Using remote diagnostics and over the air updates, Tesla can fix many of these issues without the customer even knowing. Using predictive analytics, Tesla can often predict car maintenance needs and use smart alerts to notify the driver when it’s time to bring the car in for repairs.
In addition, these systems could also make recommendations for in-car adjustments based on individual driver data. For example, based on a person’s height or style of driving, the car could suggest a person move back their seat or adjust the height of the steering wheel to achieve more control or comfort. If they aren’t already, these analytics could even eventually automatically adjust braking systems or torque based on how a person drives.
There is consensus amongst industry experts: the future of the automotive industry is data. Not only is data fueling the innovation necessary to deliver level five autonomous capabilities, it is opening new revenue streams and business models via data monetization. In fact, according to a report by Mckinsey, the "car data market" could be worth as much as $750B by 2030. Though Tesla is one of the more high profile examples of this newfound data-centric approach, they are far from the only automakers experimenting with data monetization and driver analytics.
Image sourced from "Connected Car Industry: Big Picture." https://www.eetasia.com/connected-car-industry-big-picture/
To wrap things up it should be noted that the “future of X is data” could be said about any industry at this point. Increasingly, investors and other stakeholders are not only assessing the value of companies based on the cut and dry metrics of profits and losses, but by the current and potential future value of enterprise data. Furthermore, organizations that fail to establish data and analytics as vital assets in the near-term are unlikely to be able to capitalize on innovation, such as AI and other cognitive technologies, in the long-term.
We’re now entering an era where data-centricity is no longer simply a competitive advantage but a mandate. Are you prepared?