Improving Subscriber Churn Models with Synthetic Data

Improving Subscriber Churn Models with Synthetic Data

Subscriber churn is one of the most pressing challenges for subscription-based businesses — and predictive modeling is key to staying ahead of it. In this webinar, we’ll introduce a practical churn analytics framework designed to identify at-risk subscribers and guide retention strategies.
We’ll then demonstrate how synthetic data can be used to enhance model performance, especially when real-world data is sparse, imbalanced, or biased. Learn how synthetic data can help uncover hidden patterns, improve generalization, and ultimately lead to smarter, more proactive churn management.
Whether you're in media, telecom, SaaS, or nonprofits, this session will offer actionable insights and examples to help you rethink your approach to churn prediction.