Engage Voice | Intro to measures and attributes

Last updated on April 19, 2022

Table of contents

Measures and attributes are used in units of data used to create reports and dashboards in Historical Analytics.
  • Measures: Computational expressions of numerical or quantitative data.
  • Attributes: Non-measurable descriptors used to break down metrics and measures. Attributes represent qualitative data.
This means that measures are data that can be ‘sliced’ by attributes.
For example, Agent Count — the number of agents logged in during a time interval — is a measure. Agent Location is an attribute. If you were to create a report, Agent Count can be ‘sliced’ by Agent Location to display a bar graph of the number of agents per location.

Note: Not all measures can be ‘sliced’ by all attributes, and certain attributes don’t work on certain measures. 

Logical data models

The data used in creating measures and attributes pulled from your contact center’s database are organized into datasets. In Historical reports, a dataset is a basic organizational unit for these data. It is a set of related measures, a set of attributes, or a set of both.
Datasets are connected to one another to create exclusive relationships. For example, Dataset A is connected to Dataset B, but is not connected to Dataset C. Dataset B, on the other hand, is connected to Dataset C. This means that A can interact with B but not with C and vice versa. However, B can interact with both A and C. 
Try to imagine each dataset as boxes with data in them. Datasets connected to one another constitute a logical data model (LDM). The LDM in Historical reports is used to determine which data can interact with another data. 
In Historical reports, there are two important datasets for data: segment and agent state. Some data are tagged and can be found under those categories, while some are not. When you drag a measure or an attribute to a section on the canvas, the data catalog will repopulate and only data compatible with the one on the canvas will appear in the catalog. 
It is also important to understand the concept of the segment and agent state datasets, so you don’t confuse compatible data with incompatible data when conceptualizing reports you want to create.


Segments or call segments are the smaller portions of an interaction between a call’s consecutive states. These consecutive states are parts of a call associated with a specific ‘product’ like an IVR or a queue. Each portion or segment is independent of another segment of that call. To better understand this, read the example below.
Let's look at calls from the perspective of the caller. Let’s say you call a contact center. You get an IVR (Segment 1) which then transfers you to a queue for an agent (Segment 2). After talking to that agent, it turns out you chose the wrong queue so the agent transfers you to the right queue. After waiting for a long time in that queue, you hang up (Segment 3). From your perspective as a caller, you consider all those transfers and queues as one long call.
However, from the contact center’s perspective, that wasn’t just a long call but rather a series of segments independent from one another. Getting an IVR (Segment 1) is independent from getting on a queue and talking to an agent (Segment 2), and so on.
Segment diagram

Agent state

Agent states include both call-related and non-call-related agent time. Agent states are the agent’s status like Available, On Break, Engaged, and so on. The sum of all agent states, when logged in, is called ‘Login’ in this LDM. 
Agent state diagram
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