Amadeus Tunis writes that the market outlook is shifting from overrated hype to solid growth with potential for innovation.
Data monetization is a buzzword in recent years. As today’s enterprises become more data-driven, their ambitions to leverage these new, rich and deep datasets beyond their business operations or for product optimization are in the field of data monetization. Accelerate and establish it as a driving force for serious value.
Data monetization is generally defined in two main categories. Direct (create new revenue streams by making data or insights from that data available to customers or partners, often considered “outside”) and indirect (use data to drive business performance Optimize)-Leading insights or improved, data-rich services, or broadly “internal”).
The rise and fall of the new savior
But for automakers, this area has evolved differently than originally expected. Automakers were one of the first companies to try to monetize vehicle data as soon as the first connected vehicle collided with the road. Since then, they have captured ever-expanding datasets from fleets and individual drivers.
Expectations from the automobile industry were high.A new source of revenue was promised, but then reality struck.
The OEM goal is not only to use the dataset for safety monitoring, maintenance, or product development, but also to use the data to enrich their business and deliver customer insights and new products. It was also to sell to outsiders. Offering or infrastructure development and management. They emulated existing data marketplaces in other industries such as healthcare, machinery and financial data, and sold vehicle data directly through third-party providers such as Otonomo and Wejo in a variety of revenue sharing models. ..
Expectations from the automobile industry were high. A new source of revenue was promised, but then reality struck. Revenue, and often third-party demand, was far from expected, as some consumers and regions may have shown initial interest, but to leverage that data. Efforts have been at a loss over time (technical capacity limitations, real-time data needs limitations, limited impact, etc.), and models have not been generated near the expected additional revenue. did. As a result, automakers’ initial optimism has diminished.
After a disappointing experience at first, many OEMs are now rethinking their external data monetization strategies. Some manufacturers are even trying to stop exchanging data with other parties altogether. Instead, we want to take advantage of the value of the data itself. To do this, they focus on leveraging the potential of connected car data in their own new business models, such as in-vehicle and external usage-based insurance, fleet management, and other proprietary digital services. I’m guessing.
This strategic shift must advance automakers to overcome the traditional business model of “metal movement” and become a comprehensive provider of mobility and digital services in order to differentiate themselves in the future. The fact that it is further accelerated. Therefore, a comprehensive transformational approach is essential. Because, with all the agile processes, new features, and data manipulations needed to ensure quality, implement governance, and enable data and analysis, much more data than it is today. And you need to focus on software. Democratization of the entire company.
So what are these new business models? Many OEMs have long considered providing car insurance and building a one-stop ecosystem that includes purchasing, financing and insurance that covers the entire life cycle of the car. Thanks to this treasure trove of new data, we are now able to create a seamless customer experience for certain aspects of car insurance. For example, it provides a “pay when driving” insurance model that uses specific driving behaviors to assess risk and leverage access to real-time alerts to optimize accident detection and management. Depending on the OEM’s commercial strategy, this can be a direct data monetization model (working with insurers as a back-end provider that shares costs and revenues) or an indirect model as a new revenue stream (this as a data-based service). (By providing) can occur. .. Commercial research shows that usage-based insurance is the most promising, as it is expected to grow from US $ 31 billion to US $ 175 billion worldwide by 2030 (21% CAGR).
Another high value area is fleet management. It is often a complex, labor-intensive process that requires tailored vehicle delivery, continuous uptime, maintenance, service optimization, and customer management. The OEM’s initial ambitions for cost savings and new revenue generation are ultimately finalized when vehicles can automatically provide location, anticipate service needs, and provide an overall superior driver experience through connectivity and personalization. Can be unleashed. These digital services are fully licensed by B2B customers who do not have to get up and operate these features themselves. With a growth rate of US $ 18 billion in 2021 to US $ 80 billion worldwide in 2030 and a CAGR of 18%, this category is undoubtedly attractive.
Other interesting growth categories of revenue through connected car services are infotainment, mobility as a service, and in-vehicle payments. The cost of data capture, conversion, and use decreases over time, so it is likely that additional derivations will continue. Its value over the life of individual customers and individual vehicles. Focusing on customer lifetime value is the key to success.
Tracking data value
So how do you relate value and expected return on investment to individual data assets? The first step is a clear commerce of digital services by major category (usage-based insurance, fleet management, mobility services, etc.) and major regions (by different market priorities, preferences, regulations, coverage, etc.). It’s about making a strategy.
By aligning this business strategy with your data strategy (through analysis of the data (and other resources) needed to provide priority services), you can generate revenue for your data assets and their support platforms (data catalogs, data access tools, etc.). Can be attributed to. It is important to consider investing in infrastructure, data capture, storage, processing, who builds and manages each system, data wrangling and analysis, and features for marketing and promotion of services. This is similar to connecting revenue and profits to individual consumers through the products and services they purchase.
Therefore, developing such a data strategy enables and informs important business decisions. That is, identify the data that supports the maximum value and signal further investment in the data.
Guidelines for successful data monetization
Regardless of the data monetization use case, that is, the digital services it provides, it is important to increase confidentiality without following the “cumbersome” data collection behavior of large companies. The latter faces regulatory backlash, not without reason, after years of collecting and misusing user data that is largely unregulated. Data monetization strategies need a clear focus on creating and maintaining customer value and trust, especially as consumers become more aware and mature about data privacy. To achieve this, the following three foundations are essential.
transparency: Automakers need to ensure complete transparency with their customers about the data they collect, how they are used, and where they share it. Proactively establishing and communicating this transparency openly is important for building trust, even as implied by data protection legislation.
worth: Automakers need to provide their customers with true added value, such as improving the customer experience, improving safety and convenience, and reducing costs. To facilitate the adoption of paid connected car services and other services that monetize data, customers need to be clearly aware of the value they are offering. This value should not only exceed, but must exceed, the costs and risks of data sharing.
Control: Automakers need to give their customers control over what data they collect, when they collect it, and with whom they share it in a seamless customer experience. This helps prevent data misuse and builds trust. For example, Toyota recently added a data privacy portal to its mobile app. Customers can use it to track the data being used, the data used by third-party companies, how they are used, added value, and how data processing is turned on and off.
Automakers’ top priority is to build sustainable customer trust and ensure that they process their data ethically and positively. This is the only way they can ensure long-term success and maximize the potential for monetizing data.
About the author: Amadeus Tunis is Vice President of Data Strategy for Publicis Sapient, a digital consultancy.
https://www.automotiveworld.com/articles/car-data-monetisation-needs-a-reality-check/ Reality check required to monetize car data