Age-Specific Model Segmentation

Clarifying Questions

• User Definition: How do we define "older" vs "newer" users? Is this based on the length of time they've been using the service, or their age?
• Dataset Changes: Have there been any recent changes in the subscription model or external factors (e.g., pricing changes, promotional events) that might have influenced user behavior?
• Feature Relevance: Are there specific features in the dataset that are more relevant to older users versus newer users?
• Performance Metrics: What metrics are being used to evaluate the model's performance, and are these metrics sensitive to user segmentation?

Assessment

• Historical Data Analysis: Evaluate past data to identify any trends or patterns that suggest different behaviors between older and newer users.
• Feature Engineering: Assess whether specific features can be tailored to each user group to improve model accuracy.
• User Behavior Insights: Determine if user behavior significantly differs between the two groups, warranting separate models.