Team Members: Babli Dey, Selenge Tulga, Shakleen Ishfar, Yiyao Tao, Yuxin Sa
Marketing agency Butler/Till had insufficient real-world data to use for advertising analytics due to privacy regulations, technical constraints, and resource limitations. Therefore, the goal of this project was to generate synthetic tabular data to enhance the predictive accuracy of Butler/Till’s models. Students utilized advanced data synthesis techniques, including CTGAN, TVAE, CopularGAN, PAR, and GaussianCopula to create high-quality, representative datasets. The team also created a robust validation framework using a 10-fold time series cross-validation approach to assess the impacts of synthetic data on model performance. The results demonstrated that incorporating synthetic data significantly improves model reliability and accuracy, with methods such as CTGAN outperforming others across multiple metrics. The project ultimately addressed Butler/Till’s data scarcity issues and delivered a scalable solution to improve predictive analytics for advertising, paving the way for better-informed marketing strategies and decisions.