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Synthetic metric

What is a synthetic metric?

A synthetic metric is a single measure derived from multiple individual metrics, providing a comprehensive view of complex systems or scenarios. It simplifies data interpretation, aids decision-making, and enhances understanding of correlations and trends. 

Synonyms: synthetic indicator, synthetic measure

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When will you use synthetic metric?

A “synthetic metric” is used when you need a single, comprehensive value to represent multiple related measures. For example:

  • In User Experience (UX) design, you might combine ratings for interface attractiveness, ease of use, and user interest to create a synthetic metric representing overall user satisfaction.
  • In system performance, a synthetic metric might combine CPU usage, memory consumption, and network latency to provide an overall performance score.
  • In marketing, you might combine customer acquisition cost, conversion rate, and customer lifetime value to generate a comprehensive metric for campaign success.

Benefits of synthetic metric

  1. Simplification: A single value representing multiple variables can make data more understandable and manageable.
  2. Comprehensive Evaluation: It provides a broad, holistic view of a system, design, or user experience, instead of focusing on isolated elements.
  3. Decision-Making: It can streamline decision-making by highlighting overall trends or performance, aiding in resource allocation and strategy development.
  4. Benchmarking: It allows for easy comparison against competitors or previous performances.
  5. Detecting Correlations: It can help identify patterns and correlations between different metrics that might be missed when looking at them separately.

How to create synthetic metric

Creating a synthetic metric requires careful planning and execution. Beyond identifying key metrics and gathering data, one must consider other factors like the relevance of each metric to the end goal, potential bias in data collection, and ensuring the consistency of data over time.

Creating a synthetic metric involves the following steps:

  1. Identify Key Metrics: Start by identifying the individual metrics that matter most for the analysis or evaluation you’re conducting. These should be quantifiable and relevant to your goals.
  2. Collect Data: Gather data for each of these metrics. This could involve user surveys, performance tracking, analytics tools, etc.
  3. Normalize Data: If the metrics aren’t on the same scale, normalize them so they can be fairly compared and combined. This could involve converting all metrics to a scale of 0-1, or standardizing them to have a mean of 0 and a standard deviation of 1.
  4. Combine Metrics: Decide on a method to aggregate the data. This could be a simple average, a weighted average if certain metrics are more important, or a more complex mathematical formula depending on your needs.
  5. Test and Refine: Evaluate the effectiveness of your synthetic metric. Does it give a useful overview? If not, refine the metrics you’re including or how you’re combining them.
  6. Monitor Over Time: Keep track of your synthetic metric over time to spot trends, patterns, and changes.

💡 Remember, the goal of a synthetic metric is to provide a single, manageable value that offers insight into a larger, more complex situation or system. It’s important that your synthetic metric is carefully designed to accurately represent the factors you’re interested in.


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What is a Synthetic Metric Format?

A synthetic metric format refers to a standardized method of representing and measuring performance or quality metrics in synthetic monitoring. It involves collecting metrics like response time, availability, error rates, and presenting them in a structured way.

This structured format includes information such as timestamps, specifics of the test, location, performance metric value, and other relevant metadata. This standardized format allows for easy analysis, comparison, trend identification, and data-driven optimization.

What is Synthetic Monitoring?

Synthetic monitoring uses scripts or bots to simulate user actions and measure a website or application’s performance. This approach helps identify and resolve issues before they affect users. It’s useful for testing new features or updates and for monitoring during off-peak hours. However, it doesn’t replace real-user monitoring for a complete understanding of application performance.


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