Statisticians often seek to measure the "interestingness" of a statistical result through metrics like "variance explained," with McFadden's pseudo-R² being a particularly useful tool for non-linear outcomes such as binary predictions. This post explores the practical application of pseudo-R² in Heap's Group Suggestions feature, which automatically recommends insightful groupings in funnel analysis by quantifying their potential impact. Pseudo-R² is appreciated for its ability to balance variation with group composition, rewarding groupings with common categories that have distinct success rates. Unlike other metrics like log-loss or binomial deviance, pseudo-R² normalizes results between 0 and 1, making it easier to compare models across different datasets. The metric's adaptability to handle multiple groups makes it particularly valuable in product analytics, where interpretability and robustness are crucial. The author's experience at Heap highlights the value of classical statistical methods in building scalable, automated solutions that deliver meaningful insights, underscoring the continued relevance of pseudo-R² in modern data science applications.