Chart Types Overview

Creating custom reports helps you better understand how well your contact center is operating. A powerful next step is to create compelling charts, which can help us visualize relationships among the data, in order to transform raw data into actionable insights.

Like with reports, building a chart involves assembling a collection of fields, and then applying one or more groupings to the data. This enables comparative and contextual analysis of available data.

There are many chart types available within Insights, so you should always be able to find the right chart type for whatever kind of insight is needed. 

By experimenting with different data sources, chart types, fields, groupings, and summary functions, countless insights are waiting to be uncovered.

Chart types

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A pie chart is an ideal way to represent distribution/parts of a whole (adding up to 100%). 

This example shows the breakdown of incoming interactions (by type) into a queue.

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Bar charts provide an easy visual comparison between different datasets/values.

This example shows a day-by-day breakdown of how many of each interaction type a specific queue received.

A stacked bar chart combines characteristics of a standard bar chart with those of a pie chart.  

Like a bar chart, they provide an easy visual comparison between different datasets/values—and, like a pie chart, each individual bar shows not only how many interactions, but also the breakdown by type. This example shows each agent's total number of interactions (bar height), as well as the breakdown of those interactions by type.

Line charts display data values connected by line segments, making trends easy to recognize. This example shows daily outbound dials by sales reps.  

A spline chart is essentially a line chart, but instead of connecting the data points with straight line segments, a curved line of “best-fit” connects the data points.

The example spline chart shows the same dataset as the Line chart (on the left), but the line is smooth and curved, rather than pointed.

Area charts are just like line charts, with one exception—the area between the axis and line is filled/shaded in, for increased legibility. Typically, area charts are ideal for showing volume.

This example, again, is the same data set as the Line and Spline examples above.

An area spline chart is an area chart, but with the line formatting of a spline chart. 

Stacked area charts are similar to stacked bar charts, in that they illustrate both overall trends as well as the distribution of parts to a whole. They “stack” area charts on top of each other.

This example shows the total number of interactions that were handled each day (the main area), as well as how those are distributed among communication types (the stacked colors).   

A stacked area spline chart is just like a stacked area chart (see above), except with curved lines of “best-fit” rather than straight lines connecting the data points.

Combination charts allow multiple chart types to overlay each other.

Upper left | This example combines an area chart (number of interactions handled) with a bar chart (daily average wait time).

Upper right | This example combines a bar chart (number of interactions handled) with a line chart (daily average wait time).

Lower | A combination chart that includes three components (an area chart of number of interactions handled, and a pair of line charts depicting average daily wait time and average daily talk time).

A Big Number Stat is a unique way to boldly display a single statistic, mainly ideal for including in a custom dashboard. Here, we are seeing how quickly agents in the Support Q are answering customer inquiries. 

Using groupings in charts

Grouping considerations for pie charts

In a majority of applications, a pie chart can only really use a single grouping. Since a pie chart shows part of a whole, the grouping determines what is being measured/displayed.

It is possible to apply multiple groupings to a pie chart—if one of them is a date/time grouping (which is shown in the second image below).

Above (top): An Incoming Support Interactions pie chart, grouped by Agent Name. This chart shows that Matt handled the most interactions within the charted timeframe.

Above (bottom): The same Incoming Support Interactions pie chart, with two groupings—Agent Name and date/time (by day).

In the lower screenshot, the inner pie is the distribution of interactions by date (June 14 had the most) and the outer ring is the distribution of each day's interactions by agent (on the 14th, James handled the most interactions).

Grouping considerations for area, area spline, stacked area, line, spline, bar stacked bar, and combination charts

When considering how to group the data, then, the options with these chart types would be:

• one grouping (e.g., Agent Name)

• two standard groupings (e.g., Agent Name, Communication Type),  but no date/time grouping

• one standard grouping (e.g., Agent Name) and a date/time grouping (e.g., by day)