Yana Asenova started her PhD program at Kühne Logistics University under the supervision of Prof. Dr. Jan Becker and Prof. Dr. Christian Barrot in February 2023. Her research focuses on Marketing and Business Innovation within the entertainment industry, with specific focus on the music industry.
Yana completed her Bachelor and Master of Science in Management at Kühne Logistics University. During this time she developed an interest in the field of data-driven marketing and pursued a career as Analytics Expert in Datalogue GmbH. During her time with the company, she supported multiple clients with deriving valuable insights from their data to optimize their marketing and sales efforts. In addition, she established a strong approach in translating complex data science methods into accessible information that were used in strategic decisions in various industries.
Her strong interest in the Music Industry led her to seek deeper knowledge in this area and complete a second Master's degree in Music Business Innovation at IMB International Music Business School in Barcelona. There she got the chance to work on real challenges companies like YouTube Music and Sony Music face daily and learn from dozens of professionals across the industry.
Contact
Education
| Since 2023 | PhD candidate at Kühne Logistics University, Hamburg, Germany |
| 2021 - 2023 | Master of Science in Music Business Innovation, IMB International Music Business School, Barcelona, Spain |
| 2016 - 2018 | Master of Science in Management, Kühne Logistics University, Hamburg, Germany |
| 2013 - 2016 | Bachelor of Science in Management, Kühne Logistics University, Hamburg, Germany |
Professional Experience
| 2015 - 2023 | Senior Analytics Expert, Datalogue GmbH, Hamburg, Germany |
| 2015 | Internship, Content Management, Achtung! GmbH, Hamburg, Germany |
| 2014 | Internship, Customer Relationship Management, Yourfone GmbH, Maintal, Germany |
Publications
(In press): Collecting and Analyzing User-Generated Content for Decision Support in Marketing Management: An Overview of Methods and Use Cases, Schmalenbach Journal of Business Research: 1-37.
Abstract: User-generated content (UGC) is generally understood as an expression of opinion in many forms (e.g., complaints, online customer reviews, posts, testimonials) and data types (e.g., text, image, audio, video, or a combination thereof) that has been created and made available by users of websites, platforms, and apps on the Internet. In the digital age, huge amounts of UGC are available. Since UGC often reflects evaluations of brands, products, services, and technologies, many consumers rely on UGC to support and secure their purchasing and/or usage decisions. But UGC also has significant value for marketing managers. UGC allows them to easily gain insights into consumer attitudes, preferences, and behaviors. In this article, we review the literature on UGC-based decision support from this managerial perspective and look closely at relevant methods. In particular, we discuss how to collect and analyze various types of UGC from websites, platforms, and apps. Traditional data analysis and machine learning based on feature extraction methods as well as discriminative and generative deep learning methods are discussed. Selected use cases across various marketing management decision areas (such as customer/market selection, brand management, product/service quality management, new product/service development) are summarized. We provide researchers and practitioners with a comprehensive understanding of the current state of UGC data collection and analysis and help them to leverage this powerful resource effectively. Moreover, we shed light on potential applications in managerial decision support and identify research questions for further exploration.
