Enhancing Online Brand Awareness through Machine Learning: Optimizing Digital Marketing Strategies

Ahmed Ahmed Atef Ahmed Fouda

Bakalářská práce

Abstract:

The objective of this thesis is to optimize digital marketing strategies using machine learning (ML) algorithms to enhance online brand awareness. It comprises a theoretical review and an empirical analysis. The theoretical part reviews literature on digital marketing and ML applications, establishing an understanding of trends and challenges. The empirical part applies ML algorithms to analyze data from digital marketing campaigns, focusing on predictive models for consumer behavior and strategy effectiveness. Data sources include public datasets and anonymized data from marketing agencies. Python, with libraries like scikit-learn, TensorFlow, and Pandas, will be used for data processing and ML implementation, bridging theoretical insights with practical applications to propose innovative solutions for digital brand enhancement.

Keywords

Machine Learning., Digital Marketing., Predictive Analytics., Consumer Behavior., Marketing Automation., Data Analytics., Brand Awareness., AI in Marketing., Consumer Engagement., Algorithmic Advertising.

Jazyk práce: angličtina

Datum vytvoření / odevzdání či podání práce: 17. 3. 2026

Obhajoba závěrečné práce

• Vedoucí: Ing. Kristýna Kučírková, MSc
• Oponent: Jared Daniel Jacques

Česká zemědělská univerzita v Praze

Provozně ekonomická fakulta

Bakalářský studijní program:
Informatics