Data monetisation has been the buzzword of recent years. As companies today become more data-driven, the ambition to leverage these new, rich and deep data sets beyond their own business operations or for product optimisation has accelerated the field of data monetisation and established it as a serious value driver.
Data monetisation is generally defined by two main categories: direct (creating new revenue streams by making data or insights from that data available to customers or partners, often thought of as ‘external’) and indirect (optimising business performance with the help of data-driven insights or improved, data-enriched services, or broadly, ‘internal’).
Rise and fall of the new saviour
In the case of automakers, however, this field has developed differently than originally hoped. Automakers were among the first to seek to monetise their vehicle data as soon as the first connected vehicles hit the road. Since then, they have been capturing the ever-expanding data sets from their vehicle fleets and individual drivers.
Expectations from the auto industry were high. Bubbling new revenue streams were promised, but then reality struck
The OEMs’ ambition was not only to use the data sets for safety monitoring, maintenance, or product development, but also to offer them for sale to interested outsiders, who would use the data to enrich their own business, for customer insights, new product offerings or infrastructure development and management. They even went so far as to emulate existing data marketplaces in other industries, for example for medical, machine, or financial data, or to sell vehicle data directly through third-party providers such as Otonomo and Wejo in various revenue-share models.
Expectations from the auto industry were high. Bubbling new revenue streams were promised, but then reality struck. Returns, and often third-party demand, were nowhere near what was expected as select consumers and regions may have shown initial interest but their efforts to leverage that data have fizzled out over time (limited technical capabilities, limited need to real-time data, limited impact etc.) and the model did not generate anywhere near the additional revenues that had been hoped for. As a result, the carmakers’ initial optimism faded.
A new hope
After an initially disappointing experience, many OEMs are now rethinking their external data monetisation strategies. Some manufacturers are even going so far as to stop exchanging data with other parties altogether. Instead, they want to tap into the value of the data itself. To do this, they are turning the focus inward to fully exploit the potential of connected vehicle data for their own new business models, such as usage-based insurance, fleet management and other proprietary digital services, both inside and outside the vehicles.
This strategic shift is further accelerated by the fact that, in oder to differentiate themselves in the future, automakers will have to overcome their traditional business model of ‘moving metal’ and advance to become holistic providers of mobility and digital services. A comprehensive transformation approach is therefore essential, because they will have to focus much more on data and software than they do today, with all the agile processes, newly required capabilities and the necessary data operations to ensure quality, implement governance and allow data and analytics democratisation across the enterprise.
The business potential
So, what are some of these new business models? Many OEMs have long considered offering car insurance and creating a one-stop ecosystem that includes purchasing, financing, and insurance, covering the entire lifecycle of the vehicle. Thanks to this new treasure trove of data, they are now able to create a seamless customer experience regarding certain aspects of car insurance. For example, offer “pay as you drive” insurance models that use specific driving behaviour to assess risk and optimise accident detection and management by leveraging their access to real-time alerts. Depending on the OEM’s commercial strategy, this may occur in a direct data monetisation model (working with insurance companies as backend provider sharing costs and revenues) or in the indirect model (by providing this as a data-powered service) as a new revenue stream. Commercial research shows that usage-based insurance holds the most promise, as it’s expected to grow globally from US$31bn to US$175bn by 2030 (21% CAGR).
Another high value area is fleet management: an often complex, labour-intensive process, entailing fit-to-purpose vehicle provision, continuous uptime, maintenance, service optimisation and customer management. When vehicles can automatically provide locations, predict servicing needs, and provide an all-around better driver experience through connectivity and personalisation, the initial ambitions by OEMs regarding cost savings and new revenue generations can finally be unlocked. These digital services can then be fully licensed by B2B customers who do not have to stand up and operate these capabilities themselves. With expected growth from US$18bn in 2021 toUS $80bn 2030 globally, a CAGR of 18%, this is category is definitely enticing.
Other interesting growth categories for revenue through connected car services are infotainment, mobility-as-a-service, and in-vehicle payments and since costs for data capturing, transformation and usage will decline over time there is a lot of potential to continue deriving additional value from it, over the lifetime of each individual customer and each individual vehicle. A focus on customer lifetime value is key to success.
Tracking data value
So how do you link value and expected return on investment to individual data assets? A first step is to have a clear commercial strategy regarding digital services by key category (usage-based insurance, fleet management, mobility services etc.) and key region (due to various market priorities, preferences, regulation, coverage etc.).
By aligning this business strategy with a data strategy—through the analysis of required data (and other resources) needed to deliver prioritised services—it is possible to attribute revenue to data assets and their supporting platforms (e.g. data catalogues, data access tools). It is important to consider investments for infrastructure, data capturing, storing and processing, people to build and manage respective systems, providing capabilities for data wrangling and analysis as well as the marketing and promotion of services. This is similar to connecting revenue and margins to individual consumers through the products and services they buy.
Developing such a data strategy thus enables and informs critical business decisions: identifying the data that supports the greatest value, as well informing further investments in data.
Guidelines for data monetisation success
Regardless of the use case for data monetisation, i.e., the digital service it powers, it is critical to proceed with sensitivity and not follow the ‘intrusive’ data collection behaviour of big tech corporations. The latter are facing regulatory backlash, not without reason, after years of collecting and exploiting user data largely unregulated. Any strategy for data monetisation must clearly focus on the added value for customers and the creation as well as maintenance of trust, especially as consumer’s awareness and maturity regarding their data privacy is growing. To achieve this, the following three cornerstones are indispensable:
Transparency: Automakers must commit to full transparency with their customers about what data they collect, how it is used, and with whom it is shared. Proactively establishing and openly communicating this transparency, even when implied by data protection laws, is critical to building trust.
Value: Automakers must offer their customers real added value, whether through a better customer experience, greater safety and convenience, or cost savings. To drive adoption of paid connected car services or other services that monetise data, customers must be explicitly aware of the value they are being offered. This value must not only outweigh, but exceed, the costs and risks of data sharing.
Control: Automakers must give customers control over what data is collected, when it is collected, and with whom it is shared in a seamless customer experience. This prevents a sense of data misuse and builds trust. Toyota, for example, recently added the Data Privacy Portal to its mobile app. Customers can use it to keep track of what data is being used, which third-party companies are using it, how it is being used, what value it adds, and how to turn data processing on and off.
The top priority for automakers must be to build sustainable customer trust and credibly demonstrate that they are handling their customers’ data ethically and positively. This is the only way they can ensure their long-term success and realise the full potential of data monetisation.
About the author: Amadeus Tunis is Vice President, Data Strategy, at digital consultancy Publicis Sapient
