Deep Learning Insights into Digital Advertising: Analyzing IT Professionals Consumer Behaviors from Remote Work Environments

This study utilizes deep learning techniques to analyze the consumer behaviors of IT professionals in remote work environments, aiming to enhance digital advertising strategies. Utilizing a dataset from Kaggle, which includes variables related to remote work and mental health, the study focuses specifically on IT professionals by filtering for job roles and work settings. Despite the absence of direct consumer behavior data in the original dataset, synthetic variables representing digital engagement were generated to facilitate the analysis. A Deep Neural Network (DNN) was employed to model and predict the influence of remote work on consumer behavior towards digital advertisements. The model achieved a perfect test accuracy of 100% with flawless precision, recall, and F1-scores, indicating a robust capability to identify predictive patterns. The findings suggest that tailored digital advertising, informed by deep learning-derived insights into IT professionals' behaviors, can significantly enhance engagement and effectiveness. While promising, the study acknowledges limitations such as the synthetic nature of part of the dataset and the potential for overfitting given the perfect performance metrics. Future research should aim to incorporate real-world consumer behavior data and test more diverse and complex models to validate and extend these findings, thereby offering richer insights into the evolving dynamics of consumer engagement in digital environments.