Understanding Our Data Model

Correlated joins data from multiple sources to provide a centralized way for sales teams to see how the companies and users they’re tracking are using their product. Correlated accepts two general types of data - metrics and dimensions.


Check out our Glossary for key terms to know

For an overview of definitions, view our Glossary.


Metrics are always associated with a time stamp and either count events or measure a numeric value. For example, when we display a count of signins that’s a metric that counts events. Revenue is a metric that measures a numeric value that could change over time.


Dimensions describe each metric. Dimensions are grouped into four general categories -

  1. dimensions that describe the metric itself
  2. dimensions that describe the account associated with the metric
  3. dimensions that describe the user associated with the metric
  4. dimensions that describe the opportunities associated with the metric.


Your data is unique!

Please keep in mind that dimension grouping could be different on a case by case basis, but overall, most dimensions can be grouped into those four buckets.

One important thing to know about Correlated is that we track everything on a time basis, which means that we can identify and signal on changes over time. However, this also means that historical data is not loaded into Correlated, unless it is exposed to us via a data warehouse. If you’re only using Segment, we’ll only see data starting from when the integration was created. In practice, this means that if you were to look for the behavior of a user who hasn’t logged in since the Segment integration was connected, it will be as if that user was never a user. This typically does not cause problems for most users, as Correlated is focused on identifying which customers to talk to based on product usage, so users who have churned out of the product are no longer as interesting.