Clemens Brackmann

Academic Staff

Clemens Brackmann, M. Sc.

R09 R03 H16
+49 201 18-334304

Curriculum Vitae:

Oktober 2017 – März 2021: Studium der angewandten Informatik (M. Sc.) an der Ruhr-Universität Bochum

Oktober 2017 - Dezember 2020: Wissenschaftliche Hilfskraft an der Universität Duisburg-Essen in der Arbeitsgruppe Mensch-Computer Interaktionen (Unterstützung der Vorlesung und des Übungsbetriebes, Entwicklung von Anwendungen, Studiendesign und Durchführung)

Oktober 2017 - Oktober 2018: Wissenschaftliche Hilfskraft am Paluno Institut in Essen in der Arbeitsgruppe Software Engineering (Unterstützung des Projektes iObserve 2)

April 2016 - Oktober 2017: Studentische Hilfskraft am Paluno Institut in Essen in der Arbeitsgruppe Software Engineering (Unterstützung des Übungsbetriebs, Unterstützung des Projektes iObserve 2)

Oktober 2014 – Oktober 2017: Studium der angewandten Informatik (B. Sc.) an der Universität Duisburg Essen 



  • Brackmann, Clemens; Wulfert, Tobias; Busch, Jan; Schütte, Reinhard: The Art of Retail Pricing: Developing a Taxonomy for Describing Pricing Algorithms. 2024. CitationDetails

    The price is the most important determinant for product sales and is highly influential for a company's success. Nevertheless, price determination often follows individuals’ rules of thumb augmented with product and economic performance indicators. With the increasing dissemination of artificial intelligence in organizations and society, the accuracy of price determination in retail might be

    enhanced by sophisticated pricing algorithms. Technological developments further increase the number of pricing algorithms and pricing tools available. Against this backdrop, we applied Nickerson et al.’s (2013) approach, proposing a taxonomy for describing pricing algorithms in retail. The taxonomy

    consists of 19 dimensions and 59 characteristics. Analyzing 70 pricing tools revealed a high specialization for selected retail domains, a focus on competitor monitoring and dynamic pricing, and a minor use of current machine learning techniques. This is a first attempt at structuring pricing algorithms and developing a price management toolbox that constructs artificial intelligence-enabled pricing algorithms.

  • Brackmann, Clemens; Huetsch, Marek; Wulfert, Tobias: Identifying Application Areas for Machine Learning in the Retail Sector. In: SN Computer Science, Vol 4 (2023), p. 426. doi:10.1007/s42979-023-01888-wCitationDetails

    Machine learning (ML) has the potential to take on a variety of routine and non-routine tasks in brick-and-mortar retail and e-commerce. Many tasks previously executed manually are amenable to computerization using ML. Although procedure models for the introduction of ML across industries exist, the tasks for which ML can be implemented in retail need to be determined. To identify these application areas, we followed a dual approach. First, we conducted a structured literature review of 225 research papers to identify possible ML application areas in retail, as well as develop the structure of a well-established information systems architecture. Second, we triangulated these preliminary application areas with the analysis of eight expert interviews. In total, we identified 21 application areas for ML in online and offline retail; these application areas mainly address decision-oriented and economic-operative tasks. We organized the application areas in a framework for practitioners and researchers to determine appropriate ML use in retail. As our interviewees provided information at the process level, we also explored the application of ML in two exemplary retail processes. Our analysis further reveals that, while ML applications in offline retail focus on the retail articles, in e-commerce the customer is central to the application areas of ML.

Tutored Theses:

  • Intelligent chatbots in the field of customer service - A critical analysis (Bachelor Thesis Business Information Systems)
  • AI techniques for visual product searc h in supermarkets: a critical evaluation (Bachelor Thesis Business Information Systems)
  • Akzeptanz und Vertrauen von KI-Anwendungen in der Medizin am Beispiel der Onkologie: Evaluation der Potenziale und Risiken (Bachelor Thesis Business Information Systems)