Highlights from the Taipei Data Science Meetup: Exploring User Flow Practice through Metrics Systems

On December 24, 2023, Taipei hosted an insightful Data Science Meetup. The highlight was a presentation by the renowned data analyst, “Mi Gu”, titled “Exploring User Flow Practice through Metrics Systems”. Mi Gu’s depth of knowledge and insight left a profound impression on the attendees.

This event was part of our regular series of meetups in Taipei, aimed at sharing fresh thoughts and innovative perspectives in data science and analytics. Here are some key takeaways from the session.

The Importance of Metric Systems

• Metric systems aid in understanding product operations and guide decision-making.
• They allow for tracking user behavior and analyzing product performance.

Common Challenges and Issues

• Difficulty in interpreting data such as sales volume and churn rate.
• Measuring the impact of new products or features.
• The complexity of analyzing due to an abundance of metrics, highlighting the need to focus on key indicators.

Establishing a Metrics System

• Identifying product goals and understanding the product’s stage.
• Choosing a core framework and organizing the context.
• Arranging metrics to ensure directionality and action orientation.

Application of Metric Systems

• Used in daily data analysis to track product health.
• As decision support, providing clear data interpretation.
• Dashboard design to display critical data for easy understanding and operation.

Experience Sharing

• Recommended reading: “Lean Analytics”.
• Case study analysis, like that of a capsule toy shop owner, demonstrating the application of metric systems.
• Analyzing user sources, conversion rates, and churn rates to identify problems and propose solutions.

Lean Analytics: Use Data to Build a Better Startup Faster (Lean (O'Reilly))

Promoting Internal Metric Systems in Companies

• Starting with small-scale products and gradually expanding to others.
• Avoiding disputes over metrics between departments, focusing on business goals.
• Building trust in data among decision-makers.