You can use Relevant Fields to find data fields that show a high Relevance Score. A high relevance score indicates these fields are significantly more common in your filtered dataset than in the entire dataset and may be particularly useful to analyze. Relevant field investigation may provide valuable insights and enable you to conduct better root cause analysis.

Relevant Fields investigation can be used with all event types.

As an example, you can use the following suggested workflow to investigate poor user experience.

  1. From the Analytics Search page, select the Transactions event type and the Data tab. 
  2. Click Add Criteria and select an application to investigate.
  3. Click User Experience or another field in the Fields list to see the Top Values. If you see lots of error and very slow transactions, you realize it is not easy to investigate hundreds of error and very slow transactions.
  4. Add Error and Very Slow values to your search criteria to narrow the total number of transactions to a smaller dataset. You can add the values quickly by hovering over the field in the Top Values list until the + sign appears at the right. Click the + to add to the search criteria. Now the focus is only on error/very slow transactions.
  5. Click the Relevant Fields label and now you can see where to focus your investigation. In this example, you might start with the Checkout business transaction as the relevance score is 87. You can also see that Platinum and Silver customers have the two highest scores. The relevance score is an indication of where the highest proportion of error and very slow transactions are occurring.
  6. Click a field in the Relevant Field list to see more details. You can continue to add relevant fields to your search criteria to keep narrowing the dataset. This process should enable you to find issues of significance for investigation.