Potential Benefits of New Online Marketing Approaches

  • Ralf-Christian HärtingEmail author
  • Christopher Reichstein
  • Andreas Müller
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 354)


This study examines the potential benefits of new approaches in online or digital marketing. In the course of this study, the research design and the new approaches in online marketing are considered. In a specially prepared quantitative study, experts were questioned about the individual approaches by means of a questionnaire. The questionnaire is based on derived hypotheses from the literature. The study focuses on the analysis of the survey results using the SmartPLS software. After data analysis using structural equation modeling, the results show that Mobile and Data-driven Marketing as well as Programmatic Advertising do have a significant influence on the potential of the new approaches in online marketing. The results are used to recommend actions for enterprises.


Marketing 4.0 Online marketing Quantitative study Expert survey 


  1. 1.
    Härting, R.-C., Mohl, M., Steinhauser, P., Möhring, M.: Search engine visibility indices versus visitor traffic on websites. In: Abramowicz, W., Alt, R., Franczyk, B. (eds.) BIS 2016. LNBIP, vol. 255, pp. 91–101. Springer, Cham (2016). Scholar
  2. 2.
    Härting, R., Mohl, M., Bader, S.: Digitalisierung als Treiber für Marketing 4.0. Industrie 4.0 und Digitalisierung – Innovative Geschäftsmodelle wagen! Ralf-Chrisitan Härting, Aalen (2016)Google Scholar
  3. 3.
    Lammenett, E.: Praxiswissen Online-Marketing – Affiliate- und E-Mail-Marketing, Suchmaschinenmarketing, Online-Werbung, Social Media, Facebook-Werbung, 6. Auflage, Springer Gabler, Wiesbaden (2016)Google Scholar
  4. 4.
    Wang, R.J., Malthouse, E.C., Krishnamurthi, L.: On the go: how mobile shopping affects customer purchase behavior. J. Retail. 91, 217–234 (2015)CrossRefGoogle Scholar
  5. 5.
    van Doorn, J., Mende, M., Noble, S.M., Hulland, J., Ostrom, A.L., Grewal, D.: Domo Arigato Mr. Roboto: emergence of automated social presence in organizational frontlines and customers’ service experiences. J. Serv. Res. 20, 43–58 (2017)CrossRefGoogle Scholar
  6. 6.
    Doyle, S.: The role of social networks in marketing. J. Database Mark. Cust. Strat. Manag. 15, 60–64 (2007)CrossRefGoogle Scholar
  7. 7.
    Brynjolfsson, E., Hitt, L.M., Kim, H.H.: Strength in numbers: how does data-driven decision making affect firm performance? (2011)Google Scholar
  8. 8.
    Järvinen, J., Karajaluoto, H.: The use of web analytics for digital marketing performance measurement. Ind. Mark. Manage. 50, 117–127 (2015)CrossRefGoogle Scholar
  9. 9.
    Akter, S., Wamba, S.F.: Big data analytics in E-commerce: a systematic review and agenda for future research. Electron. Mark. 26, 173–194 (2016)CrossRefGoogle Scholar
  10. 10.
    Lamas, A., Chevalier, P.: Joint dynamic pricing and lot-sizing under competition. Eur. J. Oper. Res. 266, 864–876 (2017)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Sato, K., Sawaki, K.: A continuous-time dynamic pricing model knowing the competitor’s pricing strategy. Eur. J. Oper. Res. 229, 223–229 (2013)MathSciNetCrossRefGoogle Scholar
  12. 12.
    den Boer, A.V.: Dynamic pricing and learning: historical origins, current research, and new directions. Eur. J. Oper. Res. 20, 1–18 (2015)MathSciNetGoogle Scholar
  13. 13.
    Davidson, A.: Pricing Strategy and execution: an overlooked way to increase revenues and profits. Strat. Leadersh. 33, 25–33 (2005)CrossRefGoogle Scholar
  14. 14.
    Xia, L., Monroe, K.B., Cox, J.L.: The price is unfair! A conceptual framework of price fairness perceptions. J. Mark. 68, 1–15 (2004)CrossRefGoogle Scholar
  15. 15.
    Wang, F., Xu, B.: Who needs to be more visible online? The value implications of web visibility and firm’s heterogeneity. J. Inf. Manag. 54, 506–515 (2017)CrossRefGoogle Scholar
  16. 16.
    Binder, J., Weber, F.: Data Experience-Marktforschung in den Zeiten von Big Data, Marketing Review St. Gallen, vol. 32, pp. 30–39 (2015)CrossRefGoogle Scholar
  17. 17.
    Du, R.Y., Hu, Y., Damangir, S.: Leveraging trends in online searches for product features in market response modeling. J. Mark. 79, 29–43 (2015)CrossRefGoogle Scholar
  18. 18.
    Fantazzini, D., Toktamysova, Z.: Forecasting German car sales using Google data and multivariate models. Int. J. Prod. Econ. 170, 97–135 (2015)CrossRefGoogle Scholar
  19. 19.
    Chan, T.Y., Xi, Y., Wu, C.: Measuring the lifetime value of customer acquired from Google search advertising. J. Mark. Sci. 79, 757–944 (2011)Google Scholar
  20. 20.
    Withmer, C.: 5 Programmatic Mistakes That You’re Probably Making, American Marketing Association (2016).’re-Probably-Making.aspx. Accessed 12 Dec 2018
  21. 21.
    Seitz, J., Zorn, S.: Perspectives of programmatic advertising. In: Busch, O. (ed.) Programmatic Advertising: Management for Professionals, pp. 37–51. Springer, Cham (2016). Scholar
  22. 22.
    Bishop, T.: As programmatic advertising becomes the new normal, how can advertisers create greater consumer engagement and publishers ensure greater return? J. Digit. Soc. Media Mark. 5, 6–17 (2017)Google Scholar
  23. 23.
    Stevens, A., Rau, A., McIntyre, M.: Integrated campaign planning in a programmatic world. In: Busch, O. (ed.) Programmatic Advertising: Management for Professionals, pp. 193–210. Springer, Cham (2016). Scholar
  24. 24.
    Valle, M.: The Secret is Out, American Marketing Association (2014). Accessed 10 Aug 2018
  25. 25.
    Alhassany, H., Faisal, F.: Factors influencing the internet banking adoption decision in North Cyprus: an evidence from the partial least square approach of the structural equation modelling. Financ. Innov. 4, 29 (2018)CrossRefGoogle Scholar
  26. 26.
    Chin, W.W.: The partial least squares approach to structural equation modeling. In: Marcoulides, G.A. (ed.) Modern Methods for Business Research, pp. 295–336. Lawrence Erlbaum Associates, Mahwah (1998)Google Scholar
  27. 27.
    Ringle, C.M., Wende, S., Becker, J.M.: SmartPLS. SmartPLS GmbH, Boenningstedt (2015)Google Scholar
  28. 28.
    Ringle, C.M., Sarstedt, M., Straub, D.: A critical look at the use of PLS-SEM in MIS quarterly. MIS Q. (MISQ) 36(1), iii–xiv (2012). SSRN:
  29. 29.
    Hair Jr, J.F., Hult, G.T.M., Ringle, C., Sarstedt, M.: A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). Sage Publications, Los Angeles (2016)Google Scholar
  30. 30.
    Bitterich, B., Möhring, M., Härting, R.: Google Trends im Geomarketing – als Hebel für ein analytisches CRM. In: ERP-Management, February 2014, pp. 20–22. Sage Publications (2014)Google Scholar
  31. 31.
    Härting, R.: Digitalisierung und Smart Service World – Potenziale und internetbasierte Dienste am Beispiele Marketing. In: Borgmeier, A., Grohmann, A., Gross, S. (eds.) Smart Services und Internet der Dinge: Geschäftsmodelle, Umsetzung und Best Practices, München 2017, Carl Hanser Verlag GmbH und Co. KG, September 2017. ISBN 978-3-446-45184-1Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Ralf-Christian Härting
    • 1
    Email author
  • Christopher Reichstein
    • 2
  • Andreas Müller
    • 1
  1. 1.Business AdministrationAalen University of Applied SciencesAalenGermany
  2. 2.Business InformaticsBaden-Wuerttemberg Cooperative State UniversityHeidenheimGermany

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