A marketing agency's different channels guide to AI and machine learning : how AI makes work more efficient vs. where you still need a human
Tirkkonen, Kimi (2024-05-02)
Tirkkonen, Kimi
K. Tirkkonen
02.05.2024
© 2024 Kimi Tirkkonen. Ellei toisin mainita, uudelleenkäyttö on sallittu Creative Commons Attribution 4.0 International (CC-BY 4.0) -lisenssillä (https://creativecommons.org/licenses/by/4.0/). Uudelleenkäyttö on sallittua edellyttäen, että lähde mainitaan asianmukaisesti ja mahdolliset muutokset merkitään. Sellaisten osien käyttö tai jäljentäminen, jotka eivät ole tekijän tai tekijöiden omaisuutta, saattaa edellyttää lupaa suoraan asianomaisilta oikeudenhaltijoilta.
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:oulu-202405023079
https://urn.fi/URN:NBN:fi:oulu-202405023079
Tiivistelmä
In today's dynamic digital marketing landscape, the advent of artificial intelligence (AI) and machine learning is reshaping the operational paradigms across various channels. "A Marketing Agency's Different Channels Guide to AI and Machine Learning: How AI Makes Work More Efficient vs. Where You Still Need a Human" explores the integration, effectiveness, and limitations of AI in different marketing channels ranging from digital advertising and social media marketing to search engine marketing. Through an exhaustive examination of current literature, channel-specific AI applications, and real-world case studies, this research seeks to demarcate the areas where AI significantly enhances efficiency and those realms where the intuitive and creative acumen of humans remains irreplaceable.
The theoretical framework of this thesis delves into the integration of Artificial Intelligence (AI) and Machine Learning (ML) within marketing agencies, analyzing their impact on operational efficiency against the irreplaceable value of human intuition and creativity. It explores the symbiotic relationship between big data and algorithms, emphasizing AI and ML's transformative role in refining marketing strategies and enhancing personalization. This framework critically assesses the equilibrium between employing AI for data-driven insights and preserving human judgment and emotional intelligence in creative and strategic decision-making, highlighting ethical concerns like privacy, transparency, and bias, thus stressing the need for responsible AI use in marketing practices.
The research methodology employs a survey-based approach to examine AI's practical application in marketing, gathering insights from a diverse group of marketing professionals on AI's impact, challenges, and opportunities. It utilizes purposive sampling for data collection through an online survey, aiming to capture a comprehensive view of AI's role in marketing tasks, its effectiveness, and ethical implications. The analysis, grounded in statistical evaluation, seeks to offer significant insights into AI's marketing integration, ensuring ethical conduct through participant anonymity and data security.
Combining the theoretical contributions and managerial implications, this study systematically analyzes AI's intersection with traditional marketing, highlighting AI's potential to boost operational efficiency and personalize consumer interactions. It underscores the importance of AI literacy and ethical use within marketing strategies, advising on staff skills development to overcome integration challenges and establish practices for addressing ethical issues. This approach not only aims to foster innovation and competitive advantage but also advocates for sustainable customer relationships in the digital marketing landscape, offering a nuanced understanding of AI's role in marketing and the need for a balanced integration of technology and human creativity.
The theoretical framework of this thesis delves into the integration of Artificial Intelligence (AI) and Machine Learning (ML) within marketing agencies, analyzing their impact on operational efficiency against the irreplaceable value of human intuition and creativity. It explores the symbiotic relationship between big data and algorithms, emphasizing AI and ML's transformative role in refining marketing strategies and enhancing personalization. This framework critically assesses the equilibrium between employing AI for data-driven insights and preserving human judgment and emotional intelligence in creative and strategic decision-making, highlighting ethical concerns like privacy, transparency, and bias, thus stressing the need for responsible AI use in marketing practices.
The research methodology employs a survey-based approach to examine AI's practical application in marketing, gathering insights from a diverse group of marketing professionals on AI's impact, challenges, and opportunities. It utilizes purposive sampling for data collection through an online survey, aiming to capture a comprehensive view of AI's role in marketing tasks, its effectiveness, and ethical implications. The analysis, grounded in statistical evaluation, seeks to offer significant insights into AI's marketing integration, ensuring ethical conduct through participant anonymity and data security.
Combining the theoretical contributions and managerial implications, this study systematically analyzes AI's intersection with traditional marketing, highlighting AI's potential to boost operational efficiency and personalize consumer interactions. It underscores the importance of AI literacy and ethical use within marketing strategies, advising on staff skills development to overcome integration challenges and establish practices for addressing ethical issues. This approach not only aims to foster innovation and competitive advantage but also advocates for sustainable customer relationships in the digital marketing landscape, offering a nuanced understanding of AI's role in marketing and the need for a balanced integration of technology and human creativity.
Kokoelmat
- Avoin saatavuus [32889]