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Aggregation-level functions let you specify which attributes slice the metrics in your report, giving you more control over your Explore reports.

Normally, when you add a metric and multiple attributes to a report, the metric is sliced by all of those attributes. You can’t prevent the attributes from slicing the metrics, nor can you add attributes that slice the metric in the background without being present in the report. With aggregation-level functions, however, you can specify exactly which attributes will slice your metrics.

This article contains the following topics:

  • Available aggregation-level functions
  • Using the ATTRIBUTE_FIX function
  • Using the ATTRIBUTE_ADD function

Available aggregation-level functions

Explore features the following aggregation-level functions.

Function Description Example
ATTRIBUTE_FIX(metric, attribute1, attribute2, ...)

Slices the metric by the attributes specified in the function only.

Attributes added to the report (in the Columns or Rows panel) will not slice the metric, but any filters applied will be taken into account.

Can be used to keep a report’s results from being affected by specific attributes, which is especially useful for calculating percentages.

ATTRIBUTE_FIX(MED(First Reply Time (min), [Ticket created - Year], [Ticket created - Month])

In this example, the median first reply time is returned for each month of the year, regardless of the attributes added to the report.
ATTRIBUTE_ADD(metric, attribute1, attribute2, ...)

Extends the metric aggregation to the attributes specified in the function in addition to those added to the report.

Attributes added to the report will slice the metric, and any filters applied will be taken into account.

Can be used to calculate metric averages or medians, pre-aggregated, based on attributes not present in the report.

ATTRIBUTE_ADD(MED(First Reply Time (min), [Ticket created - Year], [Ticket created - Month])

In this example, the median first reply time is returned for each month of year, before being aggregated by attributes from the report.
Note: Calculated attributes can’t be used inside of the ATTRIBUTE_FIX and ATTRIBUTE_ADD functions.

Using the ATTRIBUTE_FIX function

The first example below shows how the ATTRIBUTE_FIX function works, and the second and third show potential use cases.

Find the total number of tickets per group

This example illustrates how the ATTRIBUTE_FIX function works. You’ll create a metric that returns the total number of tickets in each group without being sliced by the attributes from the report.

To find the total number of tickets per group

  1. Create a new report in the Support - Tickets dataset.
  2. Create a standard calculated metric named Tickets fixed to group with the following formula:

    ATTRIBUTE_FIX(COUNT(Tickets), [Ticket group])

  3. In the Metrics panel, add the Tickets and the Tickets fixed to group metrics. Use the SUM aggregator for the second metric.
  4. In the Rows panel, add the Ticket group attribute. At this point, both metrics show the number of tickets in each group.

  5. In the Rows panel, add the Ticket status attribute. Now, the Tickets metric is sliced by ticket status and group, but the Tickets fixed to group metric ignores the ticket status and is sliced only by the ticket group.

Find percentage of tickets from the quarterly volume

In this example, you'll create a report that shows the percentage of tickets created each month from the total number of tickets created each quarter.

To find percentage of tickets from the quarterly volume

  1. Create a new report in the Support - Tickets dataset.
  2. Create a standard calculated metric named % of quarterly volume with the following formula:

    COUNT(Tickets) / ATTRIBUTE_FIX(COUNT(Tickets), [Ticket created - Year], [Ticket created - Quarter])

  3. Open the calculated metric you just created, click Options > Edit display format, and select % in the first field.
  4. In the Metrics panel, add the Tickets and % of quarterly volume metrics. Use the SUM aggregator for the second metric.
  5. In the Rows panel, add the following attributes:
    • Ticket created - Year
    • Ticket created - Quarter
    • Ticket created - Month

Your report should look like the following:

Compare the yearly and overall full resolution time

In this example, you’ll create a chart that shows the average full resolution time for each year and compares it to the overall average resolution time.

To compare the yearly and overall full resolution time

  1. Create a new report in the Support - Tickets dataset.
  2. Create a standard calculated metric named Full resolution time (fixed) with the following formula:

    ATTRIBUTE_FIX(AVG(Full resolution time (min)))

  3. In the Metrics panel, add Full resolution time (min) and Full resolution time (fixed).
  4. In the Columns panel, add Ticket created - Year.

Using the ATTRIBUTE_ADD function

The first example below shows how the ATTRIBUTE_ADD function works, and the subsequent examples give you potential use cases.

Find the average number of tickets per group

This example illustrates how the ATTRIBUTE_ADD function works. You’ll create a metric that returns the average number of tickets per group without adding the groups attribute to the report.

To find the average number of tickets per group

  1. Create a new report in the Support - Tickets dataset.
  2. Create a standard calculated metric named Tickets per group with the following formula:

    ATTRIBUTE_ADD(COUNT(Tickets), [Ticket group])

  3. In the Metrics panel, add the Tickets metric.
  4. In the Rows panel, add the Ticket group attribute.
  5. Open the Result manipulation () menu, select Totals, and set Grand totals on to Rows with the AVG aggregator.
  6. In the Metrics panel, add the Tickets per group calculated metric you created. Use the AVG aggregator. Both metrics in the report show the number of tickets in each group, and the average per group is shown at the bottom of the table.

    However, in this example, you don’t want to display groups on the table, only the average number of tickets per group.
  7. In the Rows panel, remove the Ticket group attribute, and also remove the grand total result manipulation. The Tickets metric returns the total number of tickets in your account, but the Tickets fixed to group returns the average number of tickets per group.

  8. (Optional) In the Rows panel, add any additional attributes you want to slice the metrics. For example, add the Ticket status attribute. Now, the Tickets metric returns the total number of tickets in each status, but the Tickets per group returns the average number of tickets per group in each status.

Find the average and median tickets per month for each quarter or year

In this example, you'll create a report that shows the average and median tickets created per month.

To find the average and median tickets per month for each quarter or year

  1. Create a new report in the Support - Tickets dataset.
  2. Create a standard calculated metric named Tickets per month with the following formula:

    ATTRIBUTE_ADD(COUNT(Tickets), [Ticket created - Year], [Ticket created - Month])

  3. In the Metrics panel, add the Tickets and Tickets per month metrics. Use AVG and MED aggregators for the second metric. (To select multiple aggregators, expand the metric and select each aggregator you want to add.)

  4. In the Rows panel, add the Ticket created - Year and Ticket created - Quarter attributes. As a result, your report shows the average and median tickets per month for each quarter:

  5. (Optional) Remove the Tickets created - Quarter attribute to see the average and median tickets per month for each year.

Find the highest average resolution time per assignee

In this example, you'll create a KPI that shows the highest average resolution time across all assignees.

To find the highest average resolution time per assignee

  1. Create a new report in the Support - Tickets dataset.
  2. Create a standard calculated metric named Resolution time per assignee with the following formula:

    ATTRIBUTE_ADD(AVG(Full resolution time (days)), [Assignee ID])

  3. Open the calculated metric you just created, click Options > Edit display format.
  4. In the first field, select Custom, and then set Decimal place to 1 and Suffix to days (with a space before the word).
  5. In the Metrics panel, add the Resolution time per assignee metric. Use the MAX aggregator.
  6. In the Filters panel, add the Ticket solved - Date attribute and configure this to show tickets solved in the last 30 days.

Your report shows the highest average resolution time across all assignees over the past 30 days.

Find the average number of ticket comments per agent

In this example, you'll create a bar chart that shows the average number of comments submitted by agents per ticket.

To find the average number of ticket comments per agent

  1. Create a new report in the Support - Tickets dataset.
  2. Create a standard calculated metric named Comments per ticket with the following formula:

    ATTRIBUTE_ADD(COUNT(Comments), [Update ticket ID])

  3. Open the calculated metric you just created, click Options > Edit display format.
  4. In the first field, select Custom, and then set Decimal place to 1.
  5. In the Metrics panel, add the Comments per ticket metric. Use the AVG aggregator.
  6. In the Filters panel, add the following attributes:
    • Update - Date: Set this to show updates from the last week.
    • Updater role: Set this to exclude end users.
  7. In the Visualization type () menu, select Bar.

The report shows the average number of ticket comments for each agent.

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