Generatiivisen tekoälyn hyödyntäminen kuluttajatuotteiden markkinoinnin tuottavuuden tehostamisessa
Year: 2024
Type: dissertation
Abstract: The study explores the integration of Generative Artificial Intelligence in marketing processes, intending to provide actionable insights and strategic recommendations for maximizing the potential benefits of these technologies. The research focuses on three key objectives: identifying use cases for Generative AI in marketing, developing an evaluation framework for these use cases, and understanding the practical considerations involved in their implementation. The analysis combines an extensive literature review with specific insights from interviews with marketing professionals, positioning the findings within a broader industry context. The research reveals significant opportunities for enhancing marketing productivity, creativity, and personalization through Generative AI technology, especially in content creation, customer engagement, and digital strategy optimization. A structured evaluation framework is introduced, helping prioritize AI initiatives based on key attributes like productivity gains, revenue growth, and cost efficiency. Furthermore, practical recommendations address implementation challenges, including technological readiness, staff training, and change management. The study findings are applicable to other organizations in the consumer goods sector seeking to leverage Generative AI. The results underscore the transformative impact of AI on marketing strategies while also addressing critical issues related to data privacy, ethical considerations, and organizational change management. Ultimately, this study aims to assist companies in adopting Generative AI effectively, fostering long-term value through increased marketing efficiency and personalized consumer experiences.
Source: Aaltodoc (Aalto University)
Author: Homola, Adam
Institution: Technology Innovation Institute
Language: English
Topic: AI in Service Interactions
Subfield: Marketing
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W7047350071
