🎯 Usage Data Collection and Analytics

Feature Detail | Generated by Eircodex v1.0.0

extracted Confidence: 100%

Usage Data Collection and Analytics

This feature focuses on capturing and analyzing quantitative data on how users interact with the activity dice. It tracks metrics such as number of spins, activities completed, session duration, and equipment usage patterns. The analytics system aggregates data to provide insights into user behavior, engagement levels, and the effectiveness of activity suggestions. This information supports evaluation of project goals, including increases in physical activity and equipment lending. The analytics also enable segmentation by demographics and location to tailor future enhancements.

0 Components
0 User Stories
Detailed

📄 Feature Analysis

🆔 Feature ID
usage-data-analytics
📊 Complexity
MEDIUM
💼 Business Value
Robust analytics are vital for measuring the impact of the activity dice and demonstrating value to funders and stakeholders. By understanding usage patterns, the project team can identify successful features and areas needing improvement, optimizing resource allocation. Analytics also support reporting on key performance indicators such as increased activity levels and lending rates, reinforcing the project's contribution to public health goals. Additionally, data-driven insights facilitate personalized user experiences and scalability to new regions or user groups.
🔧 Implementation Notes
Implementation will leverage existing BUA.no analytics infrastructure where possible, supplemented by custom event tracking for dice-specific interactions. Data pipelines will be designed for real-time and batch processing, ensuring timely insights. Privacy compliance is paramount; data will be anonymized and aggregated to prevent identification of individuals. Visualization dashboards will be developed for project managers and stakeholders. The system will support export of data for external research and evaluation purposes. Integration with feedback management will enable correlation of qualitative and quantitative data.

🧩 Components

No components have been generated for this feature yet. Components are created during the component extraction phase of the documentation process.

📖 User Stories

No user stories have been generated for this feature yet. User stories are created during the user story generation phase of the documentation process.