Choosing metric aggregators

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Metric aggregators define how the data queried by a metric is calculated. For example, the metric Full resolution time (days) defaults to showing the median (MED) of the time in days it took to resolve tickets, but you can change the aggregator to show the average time, the minimum time, the maximum time and more.

Use this article to learn about the different aggregators you can use, and how to use them.

Note: For custom metrics, you can change the default aggregator it will use (see Setting a metric's default and visible aggregators).

This article contains the following sections:

• Changing the metric aggregator
• Metric aggregators reference
• Choosing the right metric aggregator

Changing the metric aggregator

To change the metric aggregator

1. In the report builder, add a metric to the Metrics panel.
2. Click the metric you added.
3. From the list, select a new aggregator.

   

   The available aggregators will differ depending on the metric you choose. Use the Metric aggregators reference to find out more about each aggregator.

4. To save the new metric aggregator, click Apply.

Tip: You can also select multiple aggregators to use with a metric. When you add the metric to the Metrics panel, click the down arrow next to the metric, and select the aggregators you want to use.

Metric aggregators reference

Use this table to learn about the different aggregators and when to use them.

Aggregator	Description	When to use
COUNT	Counts all values of an attribute. If the same value is recorded more than once, each occurrence is counted.	Used for counting objects like tickets, calls, chats, users, organizations, etc.
D_COUNT (distinct count)	Counts the number of different (unique) values. Values recorded more than once are counted only once.	Used for distinct counting objects like tickets, calls, chats, users, organizations, etc.
SUM	Adds up all numeric values.	Used for database counted, arithmetical operation and time duration metrics like Chat Messages, % Satisfaction Score, Resolution Time.
AVG (average)	Calculates the arithmetic mean of numeric values.	Used for time duration and database counted metrics like Resolution Time and Chat Messages.
MED (median)	Identifies the numeric value that separates the higher half of the values from the lower half.	Used for time duration and database counted metrics like Resolution Time and Chat Messages.
MIN (minimum)	Identifies the lowest numeric value.	Used for time duration and database counted metrics like Resolution Time and Chat Messages.
MAX (maximum)	Identifies the highest numeric value.	Used for time duration and database counted metrics like Resolution Time and Chat Messages.
VARIANCE STD_DEV (standard deviation)	Measures how far numeric values are spread out from their average value. If it is low the numeric values are close to the average. If it is high the numeric values are spread out.	Used for time duration and database counted metrics like Resolution Time and Chat Messages.
VALUE	Returns the exact value of a metric without any additional manipulation	Used only in the formula editor when creating calculated metrics or attributes. Can be applied only to database-level metrics, not to other calculated metrics. See the VALUE topic below.

Choosing the right metric aggregator

By default, metrics use the most relevant aggregator. In some situations, a different aggregator might better suit your reporting needs or you might need to decide which aggregator to use on a new calculated metric. Use the following sections to help you to decide which metric aggregator to use.

COUNT and D_COUNT

Often, you need to count specific objects (tickets, calls, chats, users) or events that relate to these objects. The metric aggregators you use to count are COUNT and D_COUNT.

• COUNT: Counting all recorded values.
• DCOUNT: Counts only unique values.

In many cases, both aggregators will return the same results because the values they are counting are not repetitive. For example, if you count Ticket IDs in the Tickets dataset, the results for both aggregators are equal, because the ID of each ticket appears only once in this dataset. If you do the same in the Updates history dataset, the COUNT aggregator returns a much higher result, because Ticket ID is recorded for each update.

For more information, see What is the difference between COUNT and D_COUNT?

SUM

This aggregator is often used for database counters and arithmetical operation metrics, but it also can be used for duration metrics, for example, to calculate the total time spent by agents on calls or chats.

The database counted metrics are computed outside of Explore and appear in Explore as ready to use metrics. They normally measure the number of specific events or objects like Agent Replies, Assignee Stations, Reopens, or Chat Messages.

The arithmetical operation metrics are calculations created in Explore that perform numerical operations with existing metrics. For example, SUM should be used for % Satisfaction Score metric, which is a division of Good Satisfaction Tickets by Rated Satisfaction Tickets.

AVG, MED, MIN, MAX, VARIANCE and STD_DEV

Team efficiency and performance is analyzed in Zendesk using time duration and database counted metrics aggregated using AVG, MED, MIN, MAX, VARIANCE and STD_DEV.

AVG and MED play a similar role in understanding the tendency of a metric. If the data you are comparing is mostly uniform then the AVG aggregator can be used. However, in many cases, Zendesk data has a high number of outliers, meaning that MED is the best option. See Average vs Median for a closer look at these aggregators.

The MIN and MAX aggregators can be used to identify the highest and lowest metric outliers.

VARIANCE and STD_DEV are more advanced analytical aggregators and are rarely used, but they are helpful in measuring how uniform or spread out the metric values are. If you never used these aggregators start with STD_DEV. Standard deviation is expressed in the same units as average and median aggregators and can be easily compared with them.

VALUE

The VALUE aggregator is different from other aggregators in that it’s available only inside of formulas. This means that you can’t set this aggregator from the Metrics panel as described above, but you can use it in the formula editor when creating a calculated metric or attribute.

The VALUE aggregator returns the exact value of a metric without any additional manipulation. In other words, it returns exactly what’s stored in the database for the metric it’s being applied to.

For example, in the following formula, each value of the First reply time (min) metric is evaluated. If that value is less than 5 minutes, the formula returns TRUE. If that value is greater than 5 minutes, the formula returns FALSE.

IF VALUE(First reply time (min))<5       
THEN TRUE     
ELSE FALSE       
END

Additionally, it’s important to understand that the VALUE aggregator is intended to be used inside of formulas as part of a calculated metric or attribute—but within a formula, it can be applied only to database-level metrics, not to other calculated metrics.

To see what this means, consider the example above alongside a new example:

IF VALUE(Comments)<5 
THEN TRUE
ELSE FALSE
END

In the first example, VALUE is being applied to the First reply time (min) metric in the Support: Tickets dataset. Looking at the formula for that metric, you’ll see that it’s simply (First reply time (min)). Because there’s no calculation applied to that metric, it’s considered a database-level metric.

In the second example, VALUE is being applied to the Comments metric in the Support: Updates history dataset. The formula for that metric is IF ([Comment present] = TRUE) THEN [Update ID] ENDIF. Because additional calculation is applied to that metric, it’s considered a calculated metric, although one that’s built into Explore. This means that VALUE can’t be used with the Comments metric inside of a formula.

For more help, see Troubleshooting errors in Explore formulas.