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Monetization of Machine-generated Online Data — Cross-industry Opportunities and Challenges

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The Monetization of Technical Data

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Abstract

Companies can nowadays use advanced forms of data analysis to gain insights that can be directly translated into competitive advantages. This is made possible in particular by an exponentially increasing amount of machine-generated online data and improved possibilities for linking and processing data. A solid and at the same time adaptable infrastructure for data collection and storage is therefore one of the most relevant pillars for economic success in the coming decades. This chapter highlights the most relevant internal and external data sources for companies from a business perspective: What special features exist? What added value can be generated? Which trends can be identified? In particular, it will be examined to what extent machine-generated online data can contribute to segmenting customer groups, more adequately addressing specific target groups and binding won customers more closely to the company. In addition, opportunities for optimizing product portfolios, improved quality management and efficient resource planning are to be taken up.

The goal is to turn data into information, and information into insight.

Carly Fiorina, ex-CEO HP

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References

  1. Thommen J-P (2008) Lexikon der Betriebswirtschaft. Managementkompetenz von A bis Z, 4th edn. Versus, Zürich

    Google Scholar 

  2. Thommen J-P, Achleitner A-K, Gilbert DU, Hachmeister D, Jarchow S, Kaiser G (2020) Allgemeine Betriebswirtschaftslehre. Umfassende Einführung aus managementorientierter Sicht, 9th edn. Springer Gabler, Wiesbaden

    Google Scholar 

  3. RashediI J (2020) Datengetriebenes Marketing. Wie Unternehmen Daten zur Skalierung ihres Geschäfts nutzen können. Springer Gabler, Wiesbaden

    Google Scholar 

  4. Mulvenna M, Norwood M, Büchner A (1998) Data-driven marketing. Electron Markets 8(3):32–35

    Article  Google Scholar 

  5. Bundesverband E-Commerce und Versandhandel Deutschland e. V. Jahrespressegespräch 2020 – E-Commerce – Rekordwachstum, Nachhaltigkeit, Globalisierung & Plattformen. https://www.bevh.org/fileadmin/content/05_presse/Pressemitteilungen_2020/200121_-_Pra__sentaion_fu__r_PK_FINAL.pdf. Accessed 20 Oct 2020

  6. HDE Handelsverband Deutschland (2020) Online monitor 2020 https://einzelhandel.de/component/attachments/download/10433. Accessed 20 Oct 2020

  7. Boßow-Thies S, Hofmann-Stölting C, Jochims H (2020) Das Öl des 21. Jahrhunderts – Strategischer Einsatz von Daten im Marketing. In: Boßow-Thies S, Hofmann-Stölting C, Jochims H (eds) Data-driven marketing. Springer Gabler, Wiesbaden, pp 3–26

    Google Scholar 

  8. Büst R (2013) Daten sind das neue Öl. Wirtsch Inform Manag 5(2):40–46

    Article  Google Scholar 

  9. Spitz M (2017) Daten – Das Öl des 21. Jahrhunderts? Nachhaltigkeit im digitalen Zeitalter. Hoffmann und Campe, Hamburg

    Google Scholar 

  10. Perrey J, Spillecke D, Umblijs A (2013) Smart analytics: how marketing drives short-term and long-term growth. To become an engine for growth, marketers neet to make „smart analytics“ their new best friend. In: McKinsey on marketing and sales, pp 25–28

    Google Scholar 

  11. Klaus L (2019) Data-driven marketing und der Erfolgsfaktor Mensch. Springer Gabler, Wiesbaden

    Google Scholar 

  12. Arora N, Dreze X, Ghose A, Hess JD, Iyengar R, Jing B, Joshi Y, Kumar V, Lurie N, Neslin S, Sajeesh S, Su M, Syam N, Thomas J, Zhang ZJ (2008) Putting one-to-one marketing to work: personalization, customization, and choice. Mark Lett 19:305–321

    Google Scholar 

  13. Brüne K (2008) Lexikon Kommunikationspolitik. Werbung, Direktmarketing, integrierte Kommunikation. Deutscher Fachverlag, Frankfurt a. M.

    Google Scholar 

  14. Horsley M (2014) Eye tracking as a research method in social and marketing applications. In: Horsley M, Eliot M, Knight B, Reilly R (eds) Current trends in eye tracking research. Springer Cham, pp 179–182

    Google Scholar 

  15. VuMA Touchpoints (2020) Verbrauchs- und Medienanalyse

    Google Scholar 

  16. Hopf G (2020) Psychografisches Targeting – Wirkung und Funktionsweise als eine besondere Form des Micro-Targetings in den sozialen Medien. In: Boßow-Thies S, Hofmann-Stölting C, Jochims H (eds) Data-driven marketing. Springer Gabler, Wiesbaden, pp 79–103

    Google Scholar 

  17. Husain S, Ghufran A, Chaubey DS (2016) Relevance of social media marketing and advertising. Splint Int J 3(7):21–28

    Google Scholar 

  18. Hutter K, Hautz J, Dennhardt S, Füller J (2013) The impact of user interactions in social media on brand awareness and purchase intention: the case of MINI on Facebook. J Prod Brand Manag 22:342–351

    Article  Google Scholar 

  19. Ghose A, Yang S (2009) An empirical analysis of search engine advertising: sponsored search in electronic markets. Manag Sci 55(10):1605–1622

    Article  Google Scholar 

  20. Lammenett E (2019) Praxiswissen online-marketing. Affiliate-, influencer-, content- und e-mail-marketing, Google Ads, SEO, social media, online- inklusive Facebook-Werbung. Springer Gabler, Wiesbaden

    Google Scholar 

  21. Fasel D (2014) Big data – Eine Einführung. HMD 51(4):386–400

    Article  Google Scholar 

  22. Urbach N (2020) Marketing im Zeitalter der Digitalisierung. Chancen und Herausforderungen durch digitale Innovationen. Springer Gabler, Wiesbaden

    Google Scholar 

  23. Farkisch K (2011) Data-Warehouse-Systeme kompakt. Aufbau, Architektur, Grundfunktionen. Springer, Berlin

    Google Scholar 

  24. Miklosik A, Evans N (2020) Impact of big data and machine learning on digital transformation in marketing: a literature review. IEEE Access 8:101284–101292

    Article  Google Scholar 

  25. Tam KY, Ho SY (2006) Understanding the impact of web personalization on user information processing and decision outcomes. MIS Q 30(4):865–890

    Article  Google Scholar 

  26. Homburg C, Giering A, Hentschel F (1998) Der Zusammenhang zwischen Kundenzufriedenheit und Kundenbindung. Universität Mannheim, Mannheim

    Google Scholar 

  27. Homburg C (2017) Grundlagen des Marketingmanagements. Springer Gabler, Wiesbaden

    Google Scholar 

  28. Zander-Hayat H, Reisch PLA, Steffen C (2016) Personalisierte Preise – Eine verbraucherpolitische Einordnung. Verbraucher Recht 31(11):403–409

    Google Scholar 

  29. Tan KH, Zhan Y (2017) Improving new product development using big data: a case study of an electronics company. R&D Manage 47(4):570–582

    Article  Google Scholar 

  30. Yu S, Yang D (2016) The role of big data analysis in new product development. In: 2016 international conference on network and information systems for computers (ICNISC). IEEE, pp 279–283

    Google Scholar 

  31. Gantz J, Reinsel D, Rydning J (2018) The digitization of the world. https://www.seagate.com/files/www-content/our-story/trends/files/idc-seagate-dataage-whitepaper.pdf. Accessed 10 Oct 2020

  32. Datalandscape http://datalandscape.eu/european-data-market-monitoring-tool-2018. Accessed 10 Oct 2020

  33. Europäische Kommission (2020) Mitteilung der Kommission an das europäische Parlament, den Rat, den europäischen Wirtschafts- und Sozialausschuss und den Ausschuss der Regionen. Eine europäische Datenstrategie. https://eur-lex.europa.eu/legal-content/DE/TXT/?qid=1593073685620&uri=CELEX%3A52020DC0066. Accessed 10 Oct 2020

  34. Arute F et al (2019) Quantum supremacy using a programmable superconducting processor. Nature 574:505–510

    Article  Google Scholar 

  35. Speicher C (2019) IBM-Forscher bezweifeln, dass der Quantencomputer von Google Supercomputern jetzt schon überlegen ist. https://www.nzz.ch/wissenschaft/quantencomputer-ibm-bezweifelt-durchbruch-von-google-ld.1517268. Accessed 10 Oct 2020

  36. Postinett A (2019) Quantencomputer sind einfach noch zu langsam. https://www.handelsblatt.com/technik/digitale-revolution/james-s-clarke-quantencomputer-sind-einfach-noch-zu-langsam/25079098.html?ticket=ST-44486-SGGN3ZxdDzKYqu7jawds-ap1. Accessed 10 Oct 2020

  37. Bauckhage C et al (2020) Quantum Machine Learning. Eine Analyse zu Kompetenz, Forschung und Anwendung. Fraunhofer, Sankt Augustin

    Google Scholar 

  38. Majaski C (2020) Distributed Ledgers. https://www.investopedia.com/terms/d/distributed-ledgers.asp#:~:text=A%20distributed%20ledger%20is%20a,to%20have%20public%20%22witnesses%22. Accessed 20 Oct 2020

  39. Bosch Stories. https://www.bosch.com/de/stories/distributed-ledger-technologie/. Accessed 20 Oct 2020

  40. Leible S, Schlager S, Schubotz M, Gipp B (2019) A review on blockchain technology and blockchain projects fostering open science. Front Blockchain 2:2–16

    Article  Google Scholar 

  41. Mitrus P (2020) https://lingarogroup.com/is-data-fabric-the-future-of-data-management-platforms. Accessed 14 Oct 2020

  42. Talend https://www.talend.com/resources/what-is-data-fabric/. Accessed 14 Oct 2020

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Acknowledgements

Sponsored by the German Research Foundation (Deutsche Forschungsgemeinschaft - DFG) in the context of the strategy of excellence of the federal government and states – EXC-2023 Internet of Production – 390621612.

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Authors and Affiliations

  1. RWTH Aachen University, Aachen, Germany

    Malte Brettel, Thorsten Beule, Markus Rey & Nathalie Huber

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  1. Malte Brettel
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  2. Thorsten Beule
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  3. Markus Rey
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  4. Nathalie Huber
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Correspondence to Malte Brettel .

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Editors and Affiliations

  1. Senseering GmbH, Köln, Nordrhein-Westfalen, Germany

    Daniel Trauth

  2. Lehrstuhl für Technologie der Fertigungsverfahren, Werkzeugmaschinenlabor WZL der RWTH Aachen, Aachen, Nordrhein-Westfalen, Germany

    Thomas Bergs

  3. Fraunhofer-Institut für Angewandte Informationstechnik FIT, Sankt Augustin, Nordrhein-Westfalen, Germany

    Wolfgang Prinz

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Brettel, M., Beule, T., Rey, M., Huber, N. (2023). Monetization of Machine-generated Online Data — Cross-industry Opportunities and Challenges. In: Trauth, D., Bergs, T., Prinz, W. (eds) The Monetization of Technical Data. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-66509-1_6

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