• explain digital marketing research techniques and differentiate between them
• apply suitable marketing research techniques to collect and analyze data
• interpret the outcome of analyses and explain the managerial implications
• select a suitable marketing research technique for a given marketing decision problem
• evaluate the ethical implications of digital marketing research

Due to the ongoing digitalization of business models and consumer interactions there is an abundance of online data available that can provide valuable marketing insights if collected and analyzed properly. Moreover, the online environment facilitates digital testing and experimentation to inform marketing decision making. This course will focus on digital marketing research techniques for collecting and analyzing such data. The research techniques can be broadly separated into two categories: 1) digital analytics and 2) digital experimentation.

Digital Analytics:

• Web analytics, analysis of user generated content

• Text analysis, analysis of unstructured data, sentiment analysis

• Social network analysis

• Online customer journeys, attribution modeling

Digital Experimentation:

• Matching approaches

• A/B tests, explore & exploit

• Discrete choice experiments

• Choice modeling

The course focuses on the application of these techniques and how they can inform decision making in marketing, for example, regarding advertising effectiveness, targeting, product development, or pricing. The course will also address ethical implications of digital marketing research regarding data privacy and algorithmic biases and the concept of Corporate Digital Responsibility.

This course addresses several of the Nordic Nine principles. As the course is centered around analytical and experimental approaches that need to be translated into managerial implications it covers "Knowledge: you are analvtical with data and curious about ambiguity." While the digital environment offers many benefits, digitalization also creates concerns and potential threats for consumers, such as violation of data privacy and algorithmic biases. In that regard, the course addresses the "Values: you understand ethical dilemmas and have the leadership values to overcome them; 

vou are critical when thinking and constructive when collaborating." The result, being digitally responsible, leads to the "Action: you produce prosperity and protect the prosperity of next generations."

The literature consists of state-of-the-art journal articles and book chapters as listed below. This list may be updated at the start of the course. 

Digital Analytics:

• Boegershausen, J., Datta, H., Borah, A., & Stephen, A. T. (2022). Fields of Gold: Scraping Web Data for Marketing Insights. Journal of Marketing, 86(5), 1-20.
• Berger, J., Humphreys, A., Ludwig, S., Moe, W. W., Netzer, O., & Schweidel, D. A. (2019). Uniting the tribes: Using text for marketing insight. Journal of Marketing, 84(1).
• Hartmann, J., Huppertz, J., Schamp, C., & Heitmann, M. (2019). Comparing automated text classification methods. International Journal of Research in Marketing, 36(1), 20-38.
• Hennig-Thurau, T., Wiertz, C., & Feldhaus, F. (2015). Does Twitter matter? The impact of microblogging word of mouth on consumers’ adoption of new movies. Journal of the Academy of Marketing Science, 43(3), 375-394.
• Hartmann, J., Heitmann, M., Siebert, C., & Schamp, C. (2022). More than a feeling: Accuracy and application of sentiment analysis. International Journal of Research in Marketing.
• Hinz, O., Skiera, B., Barrot, C., & Becker, J. U. (2011). Seeding Strategies for Viral Marketing: An Empirical Comparison. Journal of Marketing, 75(6), 55-71. 

Digital Experimentation:

• Lambrecht, A. and Tucker, C. E. (2015). Field Experiments in Marketing, Available at SSRN: https:/​/​ssrn.com/​abstract=2630209
• Gordon, B. R., Zettelmeyer, F., Bhargava, N., Chapsky, D. (2019). A comparison of approaches to advertising measurement: Evidence from big field experiments at Facebook. Marketing Science, 38(2), 193-225.
• Feit, E. M., & Berman, R. (2019). Test & roll: Profit-maximizing a/b tests. Marketing Science, 38(6), 1038-1058.
• Timoshenko, A., Hauser, J. R. (2016). Mining and Organizing User-Generated Content to Identify Attributes and Attribute Levels.  In Proceedings of the Sawtooth Software conference.
• Eggers, F., Sattler, H., Teichert, T., Völckner, F. (2018). Choice-Based Conjoint Analysis. Handbook of Market Research, Springer.

Corporate Digital Responsibility: 

• Lobschat, L., Mueller, B., Eggers, F., Brandimarte, L., Diefenbach, S., Kroschke, M., & Wirtz, J. (2021). Corporate digital responsibility. Journal of Business Research, 122, 875-888.