Synthetic Metric

Why Synthetic Metrics are Important in User Research

Synthetic metrics play a crucial role in user research by providing a more holistic view of user experience. They allow researchers to:

• Combine multiple data points into a single, meaningful measure
• Create custom metrics tailored to specific research goals
• Simplify complex data sets for easier interpretation and decision-making

How Synthetic Metrics are Used in User Research

Researchers employ synthetic metrics in various ways:

1. Performance Evaluation: Combining speed, accuracy, and user satisfaction scores to create an overall performance metric.
2. User Engagement: Merging metrics like time spent, interaction frequency, and content consumption to measure engagement.
3. Product Success: Integrating user adoption rates, retention, and feature usage to assess overall product success.

Examples of Synthetic Metrics in User Research

1. User Success Score: Combines task completion rate, time on task, and user satisfaction rating.
2. Engagement Index: Merges daily active users, session duration, and feature interaction frequency.
3. Customer Health Score: Integrates customer support interactions, product usage, and renewal likelihood.

Frequently Asked Questions about Synthetic Metrics

• What's the difference between a synthetic metric and a regular metric?: A synthetic metric combines multiple regular metrics or data points into a single, more comprehensive measure.
• How do you create a synthetic metric?: To create a synthetic metric, identify the key components you want to measure, assign weights to each component, and combine them using a predetermined formula or algorithm.
• Are synthetic metrics always better than individual metrics?: Not necessarily. While synthetic metrics can provide a more comprehensive view, they may also obscure important details. It's often best to use them in conjunction with individual metrics for a balanced analysis.
• Can synthetic metrics be standardized across different products or companies?: While some synthetic metrics can be standardized, many are custom-created to address specific research goals or product characteristics, making direct comparisons challenging.