  The evaluation of synthetic data generation is crucial, especially in the
retail sector where data accuracy is paramount. This paper introduces a
comprehensive framework for assessing synthetic retail data, focusing on
fidelity, utility, and privacy. Our approach differentiates between continuous
and discrete data attributes, providing precise evaluation criteria. Fidelity
is measured through stability and generalizability. Stability ensures synthetic
data accurately replicates known data distributions, while generalizability
confirms its robustness in novel scenarios. Utility is demonstrated through the
synthetic data's effectiveness in critical retail tasks such as demand
forecasting and dynamic pricing, proving its value in predictive analytics and
strategic planning. Privacy is safeguarded using Differential Privacy, ensuring
synthetic data maintains a perfect balance between resembling training and
holdout datasets without compromising security. Our findings validate that this
framework provides reliable and scalable evaluation for synthetic retail data.
It ensures high fidelity, utility, and privacy, making it an essential tool for
advancing retail data science. This framework meets the evolving needs of the
retail industry with precision and confidence, paving the way for future
advancements in synthetic data methodologies.
