The rise of digital and social platforms has unlocked new opportunities for businesses to optimize their abilities within personalization and target advertisement. These opportunities have also brought ethical issues regarding the use of consumer data across digital platforms. Thisstudy aims to investigate how user-generated data is deployed to profit on advertising platforms through machine-learning techniques. Specifically, it examines data on Facebook and Google Ads to identify the most relevant features for determining how users interact with ads on their platforms. In this context, features refer to age, gender, etc., which are personal information. The study was conducted with the help of secondary data from Facebook and Google Ads. Its conceptual framework is based on the CRISP-DM model. Four supervised machine-learning techniques were applied to the collected data. The four data models were K-Nearest-Neighbor, Logistic Regression, Random Forest and XGBoost. An exploratory and predictive analysis was performed on the two different datasets. The study found that advertising on Facebook and Google Ads operates using user-generated data and its findings suggest that Google Ads runs on a Pay-Per-Click business model that results in a transaction between the platform and the advertiser. Through the application of machine-learning techniques on the data, it was evident that information such asimpressions, age, gender and advertising spend are key to determining clicks. Random Forest and XGBoost were the best-performing data models overall. Through further research it was evident that the elimination of third-party cookies will highly influence platforms, especially Facebook and Google Ads.
|Educations||MSc in Business Administration and Information Systems, (Graduate Programme) Final Thesis|
|Number of pages||95|
|Supervisors||Niels Buus Lassen|