Digital Data Analysis

mlsmm2231  2023-2024  Mons

Digital Data Analysis
5.00 credits
30.0 h
Hazée Simon;
Main themes
The digital environment is characterized by an abundance of data available in the company's systems, but also outside on social networks, ecommerce sites, or competitors sites. These data once collected, assembled, and analyzed appropriately can provide information on consumer behavior, activities of competitors, or companies' performance.
Today more than ever, It is essential to monitor the company's performance on its website, on social networks, across all its digital activities. The analysis of such digital data requires both technical and analytical skills, coupled with a strong business acumen and a sense of marketing and management.
One of the key skills of the (digital) marketer of tomorrow will be the ability to identify pertinent data that can help in its thinking, deploy the data collection tools, select the analytical method of this specific digital data, and to implement the analyses necessary to build actionable business recommendations.
The primary objective of the course is to provide the knowledge and tools to identify, collect, and analyze relevant and useful data to implement and use the knowledge and results to create or adapt the marketing strategy of the company:
  • On one hand, around its performance and its competitive position.
  • On the other hand, around clients' behavior in general and more particularly in the digital environment (e-behavior).
  • The course will also focus on understanding the opportunities and limitations of different web analysis tools available for the business.
Learning outcomes

At the end of this learning unit, the student is able to :

1 By the end of the class, students will have a thorough understanding of the methods taught and will be able to apply them to digital marketing issues in order to formulate pertinent managerial recommendations.
Recommended readings
  • Stokes, R. (2023), "eMarketing- The Essential Guide to Marketing in a Digital World", Red and Yellow Creative School of
    Business, LibreTexts.
  • Evans, J. (2020), “Business Analytics”, 3rd edition, Pearson Educations.
  • Provost, F., & Fawcett, T. (2013), “Data Science for Business – What You Need to Know About Data Mining and Data-Analytic Thinking”, O'Reilly Media Inc.
Scientific and managerial articles (exhaustive list available on Moodle):
  • Balducci, B., & Marinova, D. (2018), "Unstructured data in marketing", Journal of the Academy of Marketing Science, 46, 557-590.
  • Bradlow, E., Gangwar, M., Kopalle, P., & Voleti, S. (2017), “The Role of Big Data and Predictive Analytics in Retailing”, Journal of Retailing, 93(1), 79-95.
  • Gupta, S., Leszkiewicz, A., Kumar, V., Bijmolt, T., & Potapov, D. (2020), "Digital Analytics: Modeling for Insights and New Methods", Journal of Interactive Marketing, 51, 26-43.
  • Lobschat, L., Müller, B., Eggers, F., Brandimarte, L., Diefenbach, S., Kroschke, M. and Wirtz, J. (2020), “Corporate digital responsibility”, Journal of Business Research, forthcoming.
  • Van Auken, S. (2015), “From Consumer Panels to Big Data: An Overview on Marketing Data Development”, Journal of Marketing Analytics, 3(1), 38-45.
  • Villarroel Ordenes, F. and Silipo, R. (2021), "Machine learning for marketing on the KNIME Hub: The development of a
    live repository for marketing applications", Journal of Business Research, 137, 393-410.
  • Wedel, M., and Kannan, P.K. (2016), “Marketing Analytics for Data-Rich Environments”, Journal of Marketing, 80(6), 97-121
Faculty or entity

Programmes / formations proposant cette unité d'enseignement (UE)

Title of the programme
Learning outcomes
Master [120] in Management

Master [120] : Business Engineering

Master [120] in Management

Master [120] : Business Engineering