Automotive Use Cases
Based on real-time and historical automotive data, drivers can choose from more offerings, companies can build better services, and OEMs can generate more revenue streams. The combination of shared mobility, connectivity services, and feature upgrades has the potential to expand automotive revenue pools by approximately 30 percent - McKinsey & Company.
- Improved Vehicle & Product Design
- Location Based Promotions
- Optimize Marketing Content
- Dealer Performance Analysis
Smart City and Policy
- Traffic Management
- On-Demand Car Sharing
Driver and User Behavior Analysis
Connected vehicles are creating an entirely new opportunity landscape for automotive manufacturers and dealers. This includes driving operational efficiencies through predictive maintenance scheduling, inventory management, production planning and creating data monetization opportunities such as relevant on-demand infotainment, location-based promotion optimized for in-car marketing and dealer benchmarking.
Driver and user behavior analysis allows manufacturers to understand aspects of how, when, why and by whom cars are being driven. Along with additional dimensional data such as load factors, manufacturers can use analytics to understand and improve safety factors, target in-car or companion device content, help design better in-car features, inform traffic planning and environmental policies and much more.
Combined with rapidly advancing technology, the ability to extract this level of analytical insights is unlocking new opportunities on an unprecedented scale. Understanding and monetizing use cases such as “Based on travel pattern and load factors, what is the premium in-car infotainment that I could provide to passengers of high-income vehicle owners - and when should I optimally offer it?” is now easily within the reach of vehicle manufacturers.
Connected vehicle analytics to understand driver and user behaviour is still in its infancy. The next few years will see more and more potential opportunities unlocked through analytics. The challenge for the industry is that these use cases will require explicit and specific consent. Consent holes in data will lead to lower quality, inaccurate analytics driving ineffective decision making.
The Trūata Anonymization Service and its operational controls are purposely designed to anonymize data while retaining maximum utility, allowing automotive manufacturers to protect against future uncertainty through privacy-enhanced analytics.
New analytical scenarios can be executed as soon as they are devised, without the need to collect specific consent for that program. Operational and data monetization programs are fully protected against the unknown.
Usage Based Insurance
Usage based insurance is an emerging opportunity for vehicle manufacturers and insurance companies, which rely on detailed analytical insight on driver behaviour.
The connected vehicle solves this by allowing vehicle manufacturers to collect key driver performance indicators such as time travelling, distance travelled, speed, braking patterns, etc., often in real-time, which can then be monetized to insurance companies to inform insurance policy pricing.
ePrivacy laws and the GDPR impact manufacturers ability to process data in this manner, requiring specific and informed consent under ePrivacy and a lawful basis under GDPR. However, user perception of insurance premiums and how this data might be used to justify increasing premium charges could lead to low consent rates and high withdrawal of consent.
Processing customer data via the Trūata Anonymization Service means that all consumer driving behaviour can be captured, anonymized, analysed and modelled to inform on optimal insurance pricing and policy design.
Follow the links below to learn more about the use cases for the Trūata Anonymization Service.