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How do related metrics work?
How do related metrics work?

Key and correlated metrics explained

Updated over a week ago

Staying on top of important anomalies for any given metric is awesome. But what's even cooler is knowing what happened with other correlated metrics!

System-defined related metrics

Each ticket on Baresquare refers to an anomaly recorded for a specific key metric and a combination of dimensions. Our algorithm also performs a correlation analysis on the fly, taking into account the historical patterns of correlations between the key metric and all other metrics.

If a strong correlation is identified with one or more other metrics, additional information is provided in the related metrics tab. This shows the changes and the comparisons of the correlated metrics providing more context to the main finding.

In the example above, a correlation has been identified in the historical data between the key metric ("Entrances") and one metric ("Bounce rate"). The tabular view in the 'Related metrics' tab includes the metric "Bounce rate".

User-defined related metrics

But there is more to that! You now have the option to define the metrics that you would like to see in this Table, on top of the ones our algorithms identified. These are called the "user-defined" metrics, compared to the "system-defined" ones that were discussed above.

You can contact our amazing team to learn more and set your "user-defined correlated metrics" up!


  1. The sensitivity of the correlation analysis and thresholds are configurable.

    Contact us to learn more about this.

  2. The additional data in the 'Related metrics' section (both the "system-defined and the "user-defined" metrics) refers to the exact same segment (dimensions) of the key metric.

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