User Engagement Analysis & Personalized Recommendation Modeling

Background

Digital health applications rely on user engagement data to better understand how individuals interact with health tracking tools and how those interactions relate to health outcomes. Peachy Day collects user-generated tracking data and app interaction metrics that can provide valuable insights into migraine patterns and user behavior.

To improve the platform’s ability to deliver personalized recommendations, Peachy Day sought to analyze engagement patterns within its application and explore predictive modeling techniques. This project focused on examining user interaction data and developing clustering approaches to identify distinct user profiles that could inform tailored guidance for migraine management.

Project Details

Our team worked with Peachy Day to analyze user engagement data and explore machine learning approaches for personalized recommendation development.

Workstream 1: User Engagement & Behavior Analysis

• Analyzed user interaction data to understand engagement patterns within the Peachy Day application.
• Examined metrics such as tracking frequency, session duration, and how often users log migraine-related information.
• Investigated metadata including device type, geographic location, and other contextual factors influencing user engagement.
• Identified behavioral trends that could inform improvements in user experience and platform engagement strategies.

Workstream 2: User Segmentation & Personalized Recommendation Modeling

• Applied clustering techniques such as K-Means to identify distinct groups of migraine patients based on symptom tracking and usage patterns.
• Used synthetic datasets that mimic user behavior to explore scalable modeling approaches while maintaining data privacy.
• Developed user segmentation profiles to support the creation of personalized health tips and recommendations within the app.
• Evaluated how predictive modeling could enhance the platform’s ability to deliver tailored insights to users.

Deliverables

• User Engagement Dashboard
  An interactive dashboard displaying key engagement metrics, behavioral trends, and insights into how users interact with the Peachy Day application.

• User Segmentation Analysis
  A clustering analysis identifying different migraine patient profiles based on behavioral and tracking patterns.

• Personalized Recommendation Framework
  A report outlining approaches for generating personalized tips and guidance based on user behavior and symptom tracking data.

• Final Presentation
  An executive presentation summarizing insights, modeling approaches, and recommendations delivered to the Peachy Day team.

Outcomes

This project provided Peachy Day with a deeper understanding of how users interact with the migraine tracking platform and how engagement data can support personalized health insights. By identifying distinct user behavior patterns and exploring predictive modeling techniques, the project lays the groundwork for future recommendation systems that can help users manage migraines more effectively through tailored guidance.