What are your privacy concerns about IFTTT

Quantified Car: Potentials, Business Models and Digital Ecosystems

Summary

A vehicle is a “high-performance computer on four wheels” and is equipped with heterogeneous sensors. The collection of vehicle life cycle data made possible by this sensor system allows the development of completely new products, services and business models. In the USA, in analogy to the quantified self movement, a lively quantified car startup scene has established itself, which is endowed with enormous venture capital sums of sometimes more than 20 million USD. These developments show very clearly how high the market value of a digital ecosystem for quantified cars is estimated by investors. Against this background, this article provides an introduction to the quantified car phenomenon and analyzes the business models of the three startups Automatic, Mojio and Dash. An essential finding from this analysis is that the pursued application scenarios, services and basic technologies of the startups certainly overlap. The article closes with a short discussion about the increasing competition between the ICT industry and the established automotive industry giants about the predominance in the development of a digital ecosystem for quantified cars.

Abstract

A modern vehicle is a “computer on four wheels” equipped with many different types of sensors. The so enabled continuous collection of vehicle lifecycle data facilitates the generation of innovative products, services and business models. In analogy to the Quantified Self-movement, the USA has already evolved a plethora of Quantified Car startup companies backed by enormous amounts of risk capital, reaching more than 20 million USD in some cases. These developments clearly demonstrate how high investors perceive the market value of a working digital ecosystem for Quantified Car. This paper provides an introduction into the Quantified Car phenomenon and analyzes the business models of three different Quantified Car startups: Automatic, Mojio, and Dash. One major finding is that their use cases, services and underlying technologies show many similarities. The paper closes with a discussion on the increasing competition between the players from the ICT and the automotive domain on the supremacy in the development of a digital ecosystem for Quantified Car.

From Quantified Self to Quantified Car

In a networked world, physical things from everyday life collect more and more data about themselves and their environment and are slowly transforming into “Smart, Connected Products” [5, 6]. Products become data sources and "data scientists" evaluate the steadily increasing amounts of data collected over the entire life cycle of these intelligent products in order to gain interesting insights into user and product behavior. In the private sector, for example, the quantified self movement uses the tools of modern data-supported analysis to gain complex knowledge about one's own organism.

As a term, quantified self refers to the growing willingness of many people to collect data about themselves, their behavior and their environment, be it of a biological, behavioral, or physical nature [9–11]. Today, millions of “quantified selfers” want to gain complex insights into personal, health or sporting issues with a systematic data collection, analysis and evaluation. They often use their personal smartphone for this, which is equipped with a large number of sensors and tools and accompanies the Quantified Selfer on a daily basis. The already in 2007Footnote 1 Quantified Self-Trend, which has its own term, shows its economic relevance primarily through the recent acquisitions of mobile smartphone apps in the sports sector. For example, Adidas took over the majority in the Austrian company Runtastic in 2015 for around 220 million eurosFootnote 2. The ecosystem built by Runtastic now offers consumers a wide range of health and fitness products, services and content. As of April 2016, the popular Runtastic app already had over 80 million registered usersFootnote 3. This example shows very clearly that established industry giants are now investing large sums of money to participate in innovative technology startups in the quantified self environment. The Gartner analyst is even anticipating a market volume of USD 5 billion for quantified self devices for 2016 [4].

In many people's lives, vehicles are a big purchase that is of great value and interest to their owners. The interest in knowledge gained by quantifying vehicle life cycle data, such as diagnosing the object condition, analyzing and optimizing driving style, or improving one's own driving safety through the integration of environmental data, can be rated as particularly high by vehicle enthusiasts. Because vehicles also have great potential as a data source to enable new services. If the term quantified self describes the recording, analysis and evaluation of the data generated in the course of one's own activities, the term quantified car analogously includes the systematic collection of the life cycle data of a vehicle in the usage phase and then the intelligent analysis in order to benefit various stakeholders donate.

Quantified car as an enabler of digital ecosystems

Due to constant changes in the competitive environment as a result of the digital transformation, there is a high pressure to adapt in companies. The focus of the current discussion about digitization is primarily the networking of technical devices that are used in everyday life [12]. Digital ecosystems represent the habitat of digital content by forming a technically delimited system that networks hardware, software, content and services with one another [1]. According to this system, for example, the much-cited Apple ecosystem consists of iPod, iPhone, iPad, Mac desktop, MacBooks and peripheral devices (hardware), iOS / Mac OS, Office Suite & Core Apps, iTunes & iBooks, tools for media developers (software), Music Podcasts, Audiobooks, Music Video, TV Series, Movies, eBooks, iBooks, Textbooks (content), iCloud and iTunes (services). The opportunities arising from such digital ecosystems are minimized by risks, not least because providers are constantly confronted with rethinking their services and the underlying business models in order to take strategic measures, if necessary, that include a classic, innovative or disruptive new concept. or cause reorientation [3].

Digital ecosystems have also developed around the quantified self trend: In addition to classic smartphones, a range of smart products such as smart glasses, smart watches and fitness bracelets are available to enable further facets of continuous data collection and data analysis. For this reason, the Google and Apple marketplaces offer an unmanageable number of quantified self applications. Some manufacturers of the smart, connected products listed above, such as Recon Instruments, a company acquired by IntelFootnote 4 already offer their own marketplaces on which third parties can also provide applications.

Compared to Quantified Self, the quantified car movement is still in its infancy and the number of users is correspondingly lower. With regard to the development of driver assistance systems, the networking of the vehicle with other vehicles ("Car2Car") and with the infrastructure ("Car2X") is currently being discussed intensively. However, an innovative use of the life cycle data collected from vehicles with a focus on generating added value for the driver - in analogy to the quantified self movement - has not yet achieved a high priority among European vehicle manufacturers. So the question of how the quantified self phenomenon can be successfully transferred to the vehicle remains unanswered.

Modern vehicles are high-performance computers on four wheels. Equipped with an extensive and multi-layered sensor system, they already collect a huge amount of data about themselves and, with the wide availability of driver assistance systems, more and more about their environment. According to the EU AutoMat project coordinated by VolkswagenFootnote 5, which deals with technical issues relating to the establishment of a marketplace for vehicle life cycle data, a modern vehicle processes up to 4000 signals per second in the Controller Area Network (CAN) bus system established in vehiclesFootnote 6 and thus represents a much more comprehensive and interesting sensor measurement node and data generator than the smartphones and wearables used by quantified selfer to collect data. The continuous collection and ongoing analysis of vehicle data serves a central purpose today, the guarantee and monitoring of vehicle functions. But the possibilities of a more advanced, intelligent use of this data go far beyond this original purpose, because completely new products, services and even digital ecosystems can be developed.

Quantified car ecosystems

Actors in a digital ecosystem for quantified cars

The following actors can be defined in a quantified car ecosystem:

  • Primary end users, as individual service consumers, are drivers / owners of vehicles who benefit directly and immediately from innovative products, visualizations, statistics, gamification elements and recommendations for optimizing driving style, the basis of which they created by providing their data.

  • Secondary end users are organizations or organizational units such as urban planners, insurance companies or fleet operators, which indirectly benefit from the collected and evaluated vehicle life cycle data by consuming the services provided by service providers.

  • Service providers, in turn, are organizations that offer products / services for primary and / or secondary end users and thereby generate sales. These include, for example, fleet management service providers, service providers for driving style-dependent insurance, or service providers for services for preventive vehicle maintenance. All services are based on data provided by primary end users.

  • Cloud providers (platform operators) are responsible for operating the entire infrastructure of a digital ecosystem and making it available to service providers. Primary and secondary end users, as individual or organizational service consumers, are users of the services provided by service providers in this cloud infrastructure (FIG. 1).

The USA as a driver of innovation - quo vadis Europe?

While enormous efforts have been made in the USA for several years to open up digital ecosystems for quantified cars, this development is almost bypassing Europe. Apart from the already mentioned EU project AutoMat, only isolated and comparatively small activities without significant impact are taking place in Europe. In contrast, a very lively start-up scene, financed with enormous venture capital investments of more than USD 20 million in some cases, has established itself in the USA, as the following table 1 shows. In addition to the IT scene, according to CrunchBase, a portal with information about innovative technology companies and related investor information, companies such as Magna International, Continental ITS, and BMW i Ventures are also investing.

These developments clearly show that the automotive industry also considers the market value of a digital ecosystem for quantified cars to be enormous, although from today's perspective it does not represent the innovation driver there. The EU project Automat coordinated by Volkswagen cites three main reasonsFootnote 7Why the automotive industry with its connected efforts is not yet in a position to establish an open and comprehensive digital ecosystem:

  • Currently, offers around the Connected Vehicle are characterized by brand-specific business approaches, which have resulted in proprietary and closed individual solutions. Original Equipment Manufacturers (OEMs), as vehicle manufacturers, are entering completely new markets that do not necessarily correlate with their core business.

  • Current connected services focus on the individual vehicle buyer, which inevitably leads to data protection concerns. It is currently not considered how anonymized vehicle life cycle data could be used in other contexts that do not affect the individual driver.

  • The associated risk of cooperation between competing OEMs with regard to a common, standardized provision of vehicle life cycle data in a digital ecosystem is a major hurdle why such a system has not yet been established.

Case studies for quantified car ecosystems

In the following, the business models of the three quantified car case studies Automatic, Mojio and Dash are analyzed according to the methodology of Stähler [7], which business models are roughly differentiated according to the three aspects of value proposition, architecture of value creation and income model.

Quantified Car Case Study 1: Automatic

Value proposition: According to the motto "Connect your car to the rest of your digital life", the company offers Automatic LabsFootnote 8 from San Francisco offers applications for end customers and business users. In order to be able to use this, in a first step a special adapter must be operated on the standard diagnostic interface (OBD) of a vehicle, which makes vehicle data available to various apps as a database via a smartphone paired via Bluetooth, which is available via its own marketplace, the Automatic Gallery , can be obtained.

Architecture of value creation: Automatic has been offering a range of services for private users since 2011. These include, for example, extensive statistics on trips on the smartphone and in a browser dashboard, functions for diagnosing engine and control unit problems, feedback on the respective driving style, functions for finding a parked vehicle again, collision detection with emergency services, and even the option of using IFTTTFootnote 9 (If This, Then That) Linking vehicle functions to other digital services from the web via Automatic. Automatic Labs also offers services for business customers. These include, for example, the operation of an automotive cloud, cloud-based vehicle insurance, services for the intelligent maintenance of vehicles and for increased customer loyalty in after-sales, fleet management and data analysis. Specific solutions for OEMs are being planned.

Income model: The Automatic OBD-II adapter is available in the USA for USD 99.95 and is required for entry into Automatic's quantified car ecosystem (lock-in effect). All services are limited to the USA. Business solution prices are not actively communicated on the Automatic Labs website.

Quantified Car Case Study 2: Mojio

Value proposition: Similar to Automatic, Mojio also wantsFootnote 10 "Empower" the driver when he can connect to his vehicle at any time with a single device, the smartphone. With Mojio, too, the interface between smartphone and vehicle is an adapter operated on the vehicle's OBD-II port.

Architecture of value creation: Mojio also offers a wide variety of apps and services for drivers. These range from location tracking, vehicle diagnostics, driving analytics, driving style analysis and also include the use of mobile apps that are provided by third parties in Mojio's digital ecosystem. With the Developer Center, Mojio also offers an open connected car platform that has application programming interfaces (APIs) and software development kits (SDKs) so that third parties can also develop apps as easily as possible.

Income model: Mojio operates an online shop in which the OBD-II adapter including a built-in SIM card for the AT&T cellular network can be purchased for USD 149 in the USA. With Mojio, there is direct connectivity between the vehicle and the Internet - and not just via a paired smartphone, as with Automatic Labs.

Quantified Car Case Study 3: Dash

Value proposition: According to the motto "Smarter.Driving.Everyday.", The app from the New York company DashFootnote 11 With the help of an OBD-II adapter, vehicle life cycle data was collected again in order to inform drivers in real time about interesting events. In the business variant, vehicle life cycle data is also presented in aggregated form for fleets.

Architecture of value creation: The Dash app is freely available for Android and IOS and can communicate with several OBD-II adapters available on the market, which can be obtained from the Dash online shop, for example.Dash highlights include analyzes of driving behavior and vehicle condition, ratings and rankings (community functions), personal trend analyzes and a map function to help you find the parked vehicle. Dash also offers a platform for developers with the Dash Chassis API under the heading "Internet of Cars".

Revenue model: TechCrunch, a popular online news portal for technology and internet companies, called "FitBit for Cars", wants Dash to generate interesting insights from the collected vehicle life cycle data via its own analytics platform ("Dash IQ"), which also offered to other organizations. Dash is also a project partner of the DriveSmart projectFootnote 12 of the New York City Department of TransportationFootnote 13, which is about the fact that the feedback from the app saves drivers time and money while driving even more safely. For example, drivers receive a reward when they drive in New York outside of rush hour or use less congested routes. However, information on specific sources of revenue is not available on the website.

Summary and discussion

After an introduction to the quantified car phenomenon and the efforts of startups from the USA to establish digital ecosystems in this area, the business models of the quantified car case studies, Automatic, Mojio and Dash, were based on the methodology of Stähler [7 ] described. It is noticeable that all three players pursue similar application scenarios. They aim to generate relevant information from the vehicle life cycle data collected during the usage phase and to visualize this accordingly for the driver. According to an analysis of the download numbers of Android installations on Google Play, Dash (100,000-500,000 downloads) appears to be the most widespread, ahead of Automatic (10,000-50,000 downloads) and Mojio (1,000-5,000 downloads). The following figure shows sample screenshots of the respective basic apps, whereby the similarities of the application scenarios can also be deduced from the design of the user interface (Fig. 2).

In summary, it can be said that the fuel for successful quantified car ecosystems is the vehicle life cycle data provided by the driver. Only if a critical mass of drivers voluntarily provide a critical mass of data can these digital ecosystems arise in the first place. But this requires a variety of incentives, which can probably only be generated through interesting and free services with added value for drivers.

The chicken-and-egg problem is shown by the fact that drivers provide a large amount of data so that third parties can even develop applications based on them, but many drivers will probably only provide data when interesting and useful services already exist. Parallels can be drawn between the chicken and egg problem of the “Web of Cars” and the chicken and egg problem of the Web of Data [2, 8]. In order to be able to overcome such a chicken-and-egg problem at all, some of the startups researched in the article started with very simple applications, which should allow travelers to quickly perceive a benefit.

Compared to the already established quantified self movement, the quantified car movement is still in its infancy in Europe. The topic of data protection is very firmly anchored in politics, society and industry, especially in German-speaking countries. Projects related to aspects relevant to data protection must therefore be carried out with a very sensitive approach. Not only because of this, it can be assumed that a radical data-driven innovation in the quantified car environment will not necessarily take place by the European vehicle manufacturers from the perspective of technology development.

Nevertheless, the first activities to collect and transfer data in connection with new vehicles have already taken place in Europe, as described in an article by Heise Online reporting on an ADAC experiment.Footnote 14 From these activities, however, it is not clear how the collected vehicle life cycle data should generate a benefit for the driver, nor how drivers can configure or prevent partial or total data transfer through selective data protection settings.

In such a sensitive environment, startups from the USA that do not suffer from a “privacy burden” rush forward with fresh ideas. Similar to the activities of IT giants such as Google, Apple and Facebook, the subject of data protection is being pushed into the background “for the time being”. The competition between IT companies and established industry giants from the automotive industry for dominance in the establishment of digital ecosystems around vehicle life cycle data will certainly be exciting, as a current discussion paper by the BVDW describesFootnote 15. Certainly the long-term exploitation strategy of some quantified car startups is to sell technology and its user base to big names in the automotive or IT industry. Such a strategy already suggests the very high risk capital investments on the one hand and the opaque revenue models of the startups on the other.

Finally, it should be explicitly pointed out at this point that a number of startups with a focus on the development of services related to mobility and smart cities have also emerged in Europe in recent years. However, these have far less risk capital than their "competitors" from the USA and are therefore probably not competitive in the long term. For example, there is a park bob in AustriaFootnote 16 also an innovative manufacturer of a smart parking application, which earned an investment of EUR 250,000 in 2016. German vehicle manufacturers have already recognized the relevance of the topic of smart parking. For example, the German premium manufacturer BMW is working together with the US company and traffic data analyst INRIX as part of the ConnectedDrive initiativeFootnote 17 ($ 143 million in 7 rounds from 6 investors according to Crunchbase.com) on a corresponding solution for intelligent parking.

Notes

  1. 1.

    What is the Quantified Self? www.quantifiedself.com/2011/03/what-is-the-quantified-self, last accessed on July 25, 2016.

  2. 2.

    Adidas Group acquires Runtastic: www.adidas-group.com/en/media/news-archive/press-releases/2015/adidas-group-acquires-runtastic, last accessed on July 25, 2016.

  3. 3.

    www.runtastic.com/mediacenter/corporate-assets/german/company-overview/20160405_corporate_overview_de.pdf, last accessed on July 25, 2016.

  4. 4.

    Recon Instruments: www.reconinstruments.com, last accessed on July 25, 2016.

  5. 5.

    Automotive Big Data Marketplace for Innovative Cross-sectorial Vehicle Data Services: http://www.automat-project.eu/content/about-automa, last accessed on July 25, 2016.

  6. 6.

    ISO 11898-1: 2015 "Road vehicles — Controller area network (CAN) —Part 1: Data link layer and physical signaling".

  7. 7.

    Automat project: www.automat-project.eu/content/objectives, last accessed on July 25, 2016.

  8. 8.

    Automatic Labs: www.automatic.com, last accessed on July 25, 2016.

  9. 9.

    IFTTT: ifttt.com, last accessed on July 25, 2016.

  10. 10.

    Mojio: www.moj.io, last accessed on July 25, 2016.

  11. 11.

    Dash: www.dash.by, last accessed on July 25, 2016.

  12. 12.

    Drive Smart: www.drivesmartnyc.com, last accessed on July 25, 2016.

  13. 13.

    New York City DOT: www.nyc.gov/html/dot/html/home/home.shtml, last accessed on July 25, 2016.

  14. 14.

    ADAC investigation: Car manufacturers collect data on a large scale: www.heise.de/newsticker/meldung/ADAC-Untersuchung-Autosteller-sammeln-Daten-in-grossem-Stil-3227102.html, last accessed on July 25, 2016.

  15. 15.

    Connected Cars - a discussion paper on services from the BVDW - Bundesverband Digitale Wirtschaft: www.bvdw.org/presseserver/ConnectedCars/Finalversion_Diskussionspapier_Services_15.06.pdf, last accessed on July 25, 2016.

  16. 16.

    Parkbob: www.parkbob.com, last accessed on July 25, 2016.

  17. 17.

    INRIX: www.inrix.com, last accessed on July 25, 2016.

literature

  1. 1.

    Ammon, T., Brem, A. (2013): Digital ecosystems and their business models: Analysis and implications for classic book publishers. In Keuper, F. et al. (Ed.), Digitization and Innovation: Development Perspectives, Planning, Creation (pp. 93–121). Wiesbaden: Springer Gabler.

    Google Scholar

  2. 2.

    Latif, A., US Saeed, A., Höfler, P., Stocker, A., Wagner, C. (2009): The linked data value chain: a lightweight model for business engineers. In the Proceedings of I-SEMANTICS (pp. 568-575).

    Google Scholar

  3. 3.

    Matt, C., Hess, T., Benlian, A. (2015): Digital transformation strategies. Bus. Inf. Syst. Eng., 57 (5), 339-343. 2015.

    Article Google Scholar

  4. 4.

    McIntyre, A. (2013): Market trends: enter the wearable electronics market with products for the quantified self. Gartner report 2013. https://www.gartner.com/doc/2537715/market-trends-enter-wearable-electronics. Last accessed on July 25, 2016.

  5. 5.

    Porter, M. E., Heppelmann, J. E. (2014): How smart, connected products are transforming competition. In Harvard Business Manager.

    Google Scholar

  6. 6.

    Porter, M. E., Heppelmann, J. E. (2015): How smart, connected products are transforming companies. In Harvard Business Manager.

    Google Scholar

  7. 7.

    Stähler, P. (2002): Business models in the digital economy. 2nd edition Lohmar: EUL Verlag.

    Google Scholar

  8. 8.

    Stocker, A., Daughtermann, K., Scheir, P. (2010): The value chain of data: A basis for future economic considerations of the Web of Data. HMD, Prax. Wirtsch.inform., 47 (5), 94-104.

    Article Google Scholar

  9. 9.

    Swan, M. (2009): Emerging patient-driven health care models: an examination of health social networks, consumer personalized medicine and quantified self-tracking. Int. J. Environ. Res. Public Health, 2009 (6), 492-525.

    Article Google Scholar

  10. 10.

    Swan, M. (2013): The quantified self: fundamental disruption in big data science and biological discovery. Big Data, 1 (2), 85-99. 2013.

    Article Google Scholar

  11. 11.

    Swan, M. (2015): Connected car: quantified self becomes quantified car. J. Sens. Actuator Netw., 4 (1), 2-29. 2015.

    MathSciNetArticle Google Scholar

  12. 12.

    Yoo, Y., Henfridson, O., Lyytinen, K. (2010): The new organizing logic of digital innovation: an agenda for information systems research. Inf. Syst. Res., 21 (4), 724-735.

    Article Google Scholar

Download references

thanksgiving

This work was done at the VIRTUAL VEHICLE Research Center in Graz, Austria. The authors would like to thank the Austrian Federal Ministry for Transport, Innovation and Technology (bmvit), the Austrian Federal Ministry for Science, Research and Economy (bmwfw), the Österreichische Forschungsförderungsgesellschaft mbH ( FFG), the state of Styria and the Styrian Economic Development Agency (SFG).

Author information

Affiliations

  1. Virtual Vehicle Research Center, Inffeldgasse 21a, 8010, Graz, Austria

    Alexander Stocker & Christian Kaiser

Corresponding author

Correspondence to Alexander Stocker.

Rights and permissions

Open access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author (s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Reprints and Permissions

About this article

Cite this article

Stocker, A., Kaiser, C. Quantified Car: Potentials, Business Models and Digital Ecosystems. Electrotech. Inftech.133, 334-340 (2016). https://doi.org/10.1007/s00502-016-0429-3

Download citation

keywords

  • Quantified self
  • Quantified car
  • Digital ecosystems
  • Business models

Keywords

  • Quantified Self
  • Quantified Car
  • digital ecosystems
  • business models