The Data Economy

Monetization Strategies and Business Models Behind Consumer Data

My master thesis is finally done! If you are interested in building a sustainable business model with consumer data as one of the core resources, this is the right thesis. No matter if you like to approach monetization and online business models from a technical or business side, I hope to have written in a concise and understandable manner for all audiences.
This is also the place where I would like to thank all my friends who helped me with their input, feedback and countless discussions. Nothing can be more important to me than thanking my family for their unconditional support.

In order to make the thesis a bit more accessible, I've split it up into eight different parts, all of which you can find here:

Data is considered to be the oil of the 21st century while businesses are still failing to generate lasting profits with digital ventures. How can companies actually build business models and monetization strategies to benefit from the increasing availability of data?
This conceptual master thesis proposes three different solutions: selling consumer data, using consumer data to enable transactions such as advertising, or improving and building products in return for direct revenue. To successfully implement these data-based business models, the thesis presents a new understanding of consumers as data-models and a refined data value chain with due consideration of core challenges in privacy, financials and HR.

1. Introduction

Data are to this century what oil was to the last one: a driver of growth and change.
- The Economist (2017)

In the newly developed data economy, businesses have to rethink their business models, consumers have to understand their newly gained value and new players are entering markets at unprecedented speed. The rapid rise of businesses that have put the use of data at the very core of their business activities has reshaped the commercial landscape. Five out of the six most valuable companies in 2018 would not be able to operate without products built on consumer data (Carr, 2018).

Consumers can see this change in a very different way: almost every part of our lives is quantified and data is collected by digital devices at every possible point of interaction. Your favorite social network knows your friends, feelings, preferences, vacations, relationships, location, political attitude and identity. Your smart home devices collect data about your room temperature, movements and daily habits. Your fitness tracker knows your health, heart rate, activities and even your nightly pleasures. This data enables companies to generate an unprecedented trove of information about individuals. Nonetheless, companies are struggling to generate profits in the online world (Demos, Ovide & Pulliam, 2015; Hook, 2017) where consumers have shown less willingness to pay for products (Regazzi, 2014, p. 68; Teece, 2010, p. 172).

almost every part of our lives is quantified and data is collected by digital devices at every possible point of interaction

In this competitive environment, current business literature has set a foundation of business model innovation in online business outlining the competitive advantage of specific business models (Teece, 2010). In addition, the interplay between new technologies and business models has been described (Chesbrough, 2010), which has led to authors analyzing the potential of data driving business success (Sorescu, 2017). At the same time, literature has looked at potential applications of big data to enhance business outcomes in marketing (Erevelles, Fukawa & Swayne, 2016), consumer tracking (Swan, 2013), processes and analytics (Gandomi & Haider, 2015). The most comprehensive analysis of how companies can capture value by employing consumer data was done by Hartmann et al. (2016) with a clear focus on companies that are data-driven. Here, the authors analyze companies based on data sources and key activities in the data-value chain, but do not focus on companies creating new products or monetization opportunities based on consumer data.

The goal of this master’s thesis is to identify business models that utilize the increasing amount of consumer data in order to enable a company to reap profits from the data-based value they are creating. Ideally, these business models simultaneously provide users with innovative, new and enhanced products and reduce the financial cost the user has to bear.

This thesis consists of three main components. First, a theoretical foundation of online business models and monetization strategies is established, leading to the identification of core challenges and opportunities in online monetization. The second part focuses on consumers and data, where digital data and its collection and composition into a consumer data model are outlined. The third and most essential section of this thesis combines the insights gained in the two previous parts in order to identify data-based business models. The models are derived from ten select cases of business models that have successfully embedded data as a core value stream. The final chapters deal with possible challenges data-based businesses are likely to encounter, highlight the limitations of the thesis and give an outlook for both commercial business and future research.

Continue with:


Carr, F. (2018). "Amazon Is Now More Valuable Than Microsoft and Only 2 Other Companies Are Worth More". Retrieved from (accessed March 31, 2018).

Chesbrough, H. (2010): "Business Model Innovation: Opportunities and Barriers", in: Long Range Planning, Vol. 43 (2-3), pp.354–363.

Demos, T., Ovide, S. & Pulliam, S. (2015, June 9). "Tech Startups Woo Investors With Unconventional Financial Metrics — but Do Numbers Add Up? Critics say the practice is inflating some companies’ valuations", in: The Wall Street Journal. Retrieved from (accessed January 30, 2018).

Erevelles, S., Fukawa, N. & Swayne, L. (2016): "Big Data consumer analytics and the transformation of marketing", in: Journal of Business Research, Vol. 69 (2), pp.897–904.

Gandomi, A. & Haider, M. (2015): "Beyond the hype: Big data concepts, methods, and analytics", in: International Journal of Information Management, Vol. 35 (2), pp.137–144.

Hartmann, P., Zaki, M., Feldmann, N. & Neely, A. (2016): "Capturing value from big data – a taxonomy of data-driven business models used by start-up firms", in: International Journal of Operations & Production Management, Vol. 36 (10), pp.1382–1406.

Hook, L. (2017, June 23). "Can Uber ever make money? Ride-hailing company’s new CEO will have to stem billions of dollars in losses", in: The Financial Times. Retrieved from (accessed January 30, 2018).

Regazzi, J. (2014): "Open Source and Open Access". In Regazzi, J. (Ed.), Infonomics and the Business of Free (pp. 67–89). IGI Global.

Sorescu, A. (2017): "Data-Driven Business Model Innovation", in: Journal of Product Innovation Management, Vol. 34 (5), pp.691–696.

Swan, M. (2013): "The Quantified Self: Fundamental Disruption in Big Data Science and Biological Discovery", in: Big data, Vol. 1 (2), pp.85–99.

Teece, D. (2010): "Business Models, Business Strategy and Innovation", in: Long Range Planning, Vol. 43 (2-3), pp.172–194.

The Economist (2017). "Data is giving rise to a new economy: Fuel of the future". Retrieved from (accessed March 31, 2018).