Understanding report visualizations in analyst copilot (EAP)

Visualization type	Description	Uses
Area	Displays data points (often time-based) along an X-axis, and a Y-axis, such as counts or sums. The points are connected by a line, and the area below the line is shaded, helping you see overall trends and total amounts over time, such as ticket volume or sales. A simple area chart shows just one series, such as total tickets per month, and visualizes overall growth. A stacked area chart displays multiple series, such as tickets from different support channels so you can see both each group’s contribution and the total trend over time.	Tracking cumulative or total values over time. Visualizing how different sources contribute to total ticket volume, or looking for patterns in usage or growth. Stacked area charts are best used when you want to emphasize both overall volume trends and the breakdown by sub-groups. They are less suited for precise comparison between individual series when values fluctuate a lot.
Bar	Displays data using rectangular bars, where the length of each bar represents the value of the data for a specific category. In a bar chart, categories are typically displayed on the Y axis (for horizontal bar charts) and metrics (values) are shown on the X axis.	Comparing the performance or value of individual categories (for example, teams, product types, regions). Situations where long category names occur and need space to be legible. Data sets with more than 5–7 categories or negative values. Visualizing distributions (such as histograms) when showing counts per bucket or group.
Chord	Displays entities (such as teams, regions, article topics, or departments) on the edge of a circle. Each curved line, also called a chord or ribbon, spans from one entity to another, indicating an interaction or flow (for example, traffic, shared users, or resource exchange). The thickness of each chord visually communicates how significant that link is compared to others.	When the relationships between categories are critical for your analysis, especially if the relationships are many-to-many or bidirectional. When you want to see both the structure and the intensity of how different entities interact rather than just comparing totals. For uncovering hidden clusters, patterns of overlap, or imbalances in interconnected data. Visualizing agent handoffs in support processes, cross-team ticket movement, or which articles are most often read in sequence. Chord charts are less suited for presenting time series, exact values, or datasets with a very large number of categories, where visual clarity may suffer.
Combo	Combines two or more chart types, most commonly bars and lines, into a single view. This allows you to display multiple metrics, even those with very different value ranges (like revenue and ticket volume), on the same chart. By using both a primary and a secondary Y axis, you can make each metric quickly comparable and visually distinct, making it easier to compare data within a single report. Combo charts require at least two metrics to display.	Comparing and correlating metrics with widely different ranges in a single, consolidated report. Seeing how performance indicators interact, such as linking operational numbers (tickets solved) with customer outcomes (CSAT score). Building dashboards and reports that demand layered insights, without cluttering your report with separate charts.
Donut	A variation of a pie chart that displays data as proportional segments of a circular ring, rather than a filled circle. Each segment represents a category’s value in relation to the whole, making it easier to see the composition and share of each part.	Conveying relationships for a metric such as the percentage of tickets closed. Seeing both the breakdown of components and the overall number or percentage. Donut charts are not recommended when you have many small, similarly-sized categories, or when you need to compare values in detail. Bar charts or tables are better for those scenarios.
Heatmap	Visualizes quantitative values using a matrix of colored cells. Each cell’s color intensity reflects the magnitude of the value at that position. For example, in analyst copilot, a heatmap might show ticket creation counts, where the X-axis is days of the week, the Y-axis is hours, and cell color indicates ticket volume. Darker or more saturated colors represent higher values, while lighter colors indicate lower values.	Identifying when the highest numbers of tickets are submitted, so staffing can be optimized. Pinpointing times when response times are slowest, revealing opportunities to adjust SLAs or agent scheduling for a better customer experience. Any metric that varies by two categorical or time-based dimensions can leverage a heatmap for clarity, for example, solved tickets by hour and day.
KPI (Key Performance Indicator)	A value that indicates how effectively an organization, team, or individual is achieving a critical business objective. KPIs are used to evaluate success at reaching specific targets and inform decision-making, operational improvements, and strategic priorities within a company. Examples include metrics like customer satisfaction score (CSAT), first response time, ticket volume, sales conversion rate, or system uptime, each tied to a business goal or expected outcome.	Maintaining CSAT above 90%. Keeping average first response time below two hours. Achieving 75% first contact resolution rate for all support inquiries.
Line	Represents how metric results change over time or another continuous dimension. It displays data points connected by straight or curved lines, making it ideal for visualizing trends, patterns, or changes in values over a period.	Visualizing how many tickets are created, solved, or closed each day. Showing how response times or satisfaction rates evolve over time. Monitoring daily, monthly, or quarterly sales growth and dips.
Network	Shows how different things (like teams or systems) are connected. Each dot is a node, and lines (called edges) show how they are linked or depend on each other. This helps you quickly see clusters, spot the most important nodes, and understand how everything fits together.	Mapping dependencies between teams or services. Visualizing communication, transaction, or workflow patterns. Analyzing the connectivity and structure within a technical infrastructure or organizational chart.
Radar	A radar chart, also known as a spider chart, is designed to display multivariate data across at least three metrics, where each metric is represented as an axis radiating from a central point. Each data group is plotted across these axes and connected to form a polygon, often with lines of different colors representing additional groupings or categories.	Comparing three or more metrics for multiple groups side-by-side. Visualizing profiles of strengths and weaknesses. Comparing team, product, or market-key metrics simultaneously.
Sankey	A flow visualization that illustrates how data, resources, or values move between different categories or stages. Each node in the diagram represents a category or step in a process, while the links (arcs) connecting nodes show the flow between them. The width of each link is proportional to the amount or magnitude of the flow it represents, offering a direct visual representation of the volume moving through various paths.	Illustrating how support tickets move from New to Open, then to Assigned, and finally to Solved or Closed status. Each step’s flow width would represent the number of tickets transitioning at that stage, exposing bottlenecks or lost tickets at a glance.
Scatter	Plots pairs of numerical values, where each axis represents a different metric or variable. By evaluating the distribution of points, you can identify potential correlations (positive, negative, or none), groupings, or anomalies across your dataset. Unlike line charts or bar charts, scatter charts do not connect the points or aggregate over categories, they map every data pair, providing a direct view of distribution and relationships.	Visualizing the relationship or correlation between two variables, for example, customer satisfaction and resolution time. Identifying trends, clusters, or outliers in your data. Analyzing how changes in one variable may relate to changes in another.
Table	Organizes information into rows and columns, similar to a spreadsheet, making it easy to display detailed records and compare values across multiple categories or attributes. Each row typically represents an individual item or record, while each column displays a specific metric or attribute for those items. A table helps you to break down data (such as ticket volume, CSAT, or chats by country) into straightforward tables for easy analysis.	Comparing metrics for multiple categories or entities. Aggregating totals, averages, or other calculations by column. Drilling down and filtering to explore underlying data. Supports alignment with timezone, filters, and metrics definitions for accuracy. Exportable for additional offline analysis in other applications. Table charts are best suited for displaying categorical, granular, or high-detail data where other chart types (like a bar or line graph) would be too cluttered or imprecise.
Timeline	Arranges events in chronological order, often showing each event as a marker (dot, icon, or bar) positioned along a time axis. Events can represent instantaneous points in time (like a ticket being opened) or durations (like a deployment window or scheduled outage). Timeline charts display these events or periods above and below the central time axis, often with labels or descriptions attached.	Tracking milestones, phases, and dependencies between tasks (similar to Gantt charts. Visualizing when incidents start and end, how they overlap, and their impact sequences for platforms or services. Ordering approvals, deployments, and comments for process transparency. Visualizing completed tasks, weekly goals, or support activity over calendar time. Presenting key events in product or customer journey timelines.
Toggle	Allows you to switch between different grouped or segmented views of the data using a UI control directly on the chart or dashboard widget. Analyze the same metric across multiple groupings by switching between categories such as brand, group, or status, without building separate reports or charts for each view. Once you've selected the data you want, click Drill into selection. Analyst copilot adds a new filter that restricts the report to only the items you selected.	Switching between performance of each group or region. Reviewing individual or team-level statistics, such as first reply time and CSAT. Toggling between brands, products, or channels to see breakdowns in the same visual context. Checking different customer segments or ticket statuses.
Treemap	Displays hierarchical data using nested rectangles, where the size of each rectangle is proportional to a specific value or metric. Treemaps are helpful for comparing multiple categories and subcategories at once, allowing you to see both the overall distribution and the largest (or smallest) contributors in a single compact visual. In a treemap, each higher-level category contains smaller rectangles representing its subcategories. The larger the value, the bigger the rectangle. When you hover over or click a rectangle, you can see more detailed information.	Displaying multiple levels of grouping, such as teams within departments or topics within categories, making it easy to understand relationships between parent and child groups. The size of each rectangle in a treemap corresponds to the value of that item relative to the total, so you can quickly see which categories are largest or smallest. Unlike pie charts, treemaps remain readable even when there are many items, letting you see all results at a glance. Identifying where most of your volume or activity is concentrated, and making it easy to spot dominant or underrepresented segments. Since treemaps are compact and space-filling, they provide a dense overview when space is at a premium.
Waterfall	A waterfall chart visually represents a sequence where each bar indicates an increase or decrease from the previous total. The chart begins with a starting value (often called the baseline), then shows a sequence of additions (positive changes) and subtractions (negative changes), typically as floating bars, before arriving at the final total (ending value). Connector lines are often used to “bridge” these bars together, reinforcing the idea of a running sum.	Showing how a sequence of changes (both positive and negative) leads to a final value. Visualizing how individual components contribute to a total, such as breaking down ticket volume, revenue, or bookings by source or by time period. Comparing results across sequential time periods (like month over month), rather than showing overall time trends. Analyzing scenarios where you have multiple contributing metrics or need to highlight the impact of each on the outcome. Waterfall charts are less suited for visualizing trends over long periods or when you need to compare many series at once; other chart types like line, area, or column might be better for those cases.

Visualization type

Description

Uses

Area

Displays data points (often time-based) along an X-axis, and a Y-axis, such as counts or sums. The points are connected by a line, and the area below the line is shaded, helping you see overall trends and total amounts over time, such as ticket volume or sales.

A simple area chart shows just one series, such as total tickets per month, and visualizes overall growth. A stacked area chart displays multiple series, such as tickets from different support channels so you can see both each group’s contribution and the total trend over time.

Tracking cumulative or total values over time.
Visualizing how different sources contribute to total ticket volume, or looking for patterns in usage or growth.
Stacked area charts are best used when you want to emphasize both overall volume trends and the breakdown by sub-groups. They are less suited for precise comparison between individual series when values fluctuate a lot.

Bar

Displays data using rectangular bars, where the length of each bar represents the value of the data for a specific category. In a bar chart, categories are typically displayed on the Y axis (for horizontal bar charts) and metrics (values) are shown on the X axis.

Comparing the performance or value of individual categories (for example, teams, product types, regions).
Situations where long category names occur and need space to be legible.
Data sets with more than 5–7 categories or negative values.
Visualizing distributions (such as histograms) when showing counts per bucket or group.

Chord

Displays entities (such as teams, regions, article topics, or departments) on the edge of a circle. Each curved line, also called a chord or ribbon, spans from one entity to another, indicating an interaction or flow (for example, traffic, shared users, or resource exchange). The thickness of each chord visually communicates how significant that link is compared to others.

When the relationships between categories are critical for your analysis, especially if the relationships are many-to-many or bidirectional.
When you want to see both the structure and the intensity of how different entities interact rather than just comparing totals.
For uncovering hidden clusters, patterns of overlap, or imbalances in interconnected data.
Visualizing agent handoffs in support processes, cross-team ticket movement, or which articles are most often read in sequence.

Chord charts are less suited for presenting time series, exact values, or datasets with a very large number of categories, where visual clarity may suffer.

Combo

Combines two or more chart types, most commonly bars and lines, into a single view. This allows you to display multiple metrics, even those with very different value ranges (like revenue and ticket volume), on the same chart.

By using both a primary and a secondary Y axis, you can make each metric quickly comparable and visually distinct, making it easier to compare data within a single report.

Combo charts require at least two metrics to display.

Comparing and correlating metrics with widely different ranges in a single, consolidated report.
Seeing how performance indicators interact, such as linking operational numbers (tickets solved) with customer outcomes (CSAT score).
Building dashboards and reports that demand layered insights, without cluttering your report with separate charts.

Donut

A variation of a pie chart that displays data as proportional segments of a circular ring, rather than a filled circle. Each segment represents a category’s value in relation to the whole, making it easier to see the composition and share of each part.

Conveying relationships for a metric such as the percentage of tickets closed.
Seeing both the breakdown of components and the overall number or percentage.

Donut charts are not recommended when you have many small, similarly-sized categories, or when you need to compare values in detail. Bar charts or tables are better for those scenarios.

Heatmap

Visualizes quantitative values using a matrix of colored cells. Each cell’s color intensity reflects the magnitude of the value at that position.

For example, in analyst copilot, a heatmap might show ticket creation counts, where the X-axis is days of the week, the Y-axis is hours, and cell color indicates ticket volume. Darker or more saturated colors represent higher values, while lighter colors indicate lower values.

Identifying when the highest numbers of tickets are submitted, so staffing can be optimized.
Pinpointing times when response times are slowest, revealing opportunities to adjust SLAs or agent scheduling for a better customer experience.
Any metric that varies by two categorical or time-based dimensions can leverage a heatmap for clarity, for example, solved tickets by hour and day.

KPI (Key Performance Indicator)

A value that indicates how effectively an organization, team, or individual is achieving a critical business objective. KPIs are used to evaluate success at reaching specific targets and inform decision-making, operational improvements, and strategic priorities within a company.

Examples include metrics like customer satisfaction score (CSAT), first response time, ticket volume, sales conversion rate, or system uptime, each tied to a business goal or expected outcome.

Maintaining CSAT above 90%.
Keeping average first response time below two hours.
Achieving 75% first contact resolution rate for all support inquiries.

Line

Represents how metric results change over time or another continuous dimension. It displays data points connected by straight or curved lines, making it ideal for visualizing trends, patterns, or changes in values over a period.

Visualizing how many tickets are created, solved, or closed each day.
Showing how response times or satisfaction rates evolve over time.
Monitoring daily, monthly, or quarterly sales growth and dips.

Network

Shows how different things (like teams or systems) are connected. Each dot is a node, and lines (called edges) show how they are linked or depend on each other. This helps you quickly see clusters, spot the most important nodes, and understand how everything fits together.

Mapping dependencies between teams or services.
Visualizing communication, transaction, or workflow patterns.
Analyzing the connectivity and structure within a technical infrastructure or organizational chart.

Radar

A radar chart, also known as a spider chart, is designed to display multivariate data across at least three metrics, where each metric is represented as an axis radiating from a central point. Each data group is plotted across these axes and connected to form a polygon, often with lines of different colors representing additional groupings or categories.

Comparing three or more metrics for multiple groups side-by-side.
Visualizing profiles of strengths and weaknesses.
Comparing team, product, or market-key metrics simultaneously.

Sankey

A flow visualization that illustrates how data, resources, or values move between different categories or stages.

Each node in the diagram represents a category or step in a process, while the links (arcs) connecting nodes show the flow between them. The width of each link is proportional to the amount or magnitude of the flow it represents, offering a direct visual representation of the volume moving through various paths.

Illustrating how support tickets move from New to Open, then to Assigned, and finally to Solved or Closed status. Each step’s flow width would represent the number of tickets transitioning at that stage, exposing bottlenecks or lost tickets at a glance.

Scatter

Plots pairs of numerical values, where each axis represents a different metric or variable. By evaluating the distribution of points, you can identify potential correlations (positive, negative, or none), groupings, or anomalies across your dataset.

Unlike line charts or bar charts, scatter charts do not connect the points or aggregate over categories, they map every data pair, providing a direct view of distribution and relationships.

Visualizing the relationship or correlation between two variables, for example, customer satisfaction and resolution time.
Identifying trends, clusters, or outliers in your data.
Analyzing how changes in one variable may relate to changes in another.

Table

Organizes information into rows and columns, similar to a spreadsheet, making it easy to display detailed records and compare values across multiple categories or attributes. Each row typically represents an individual item or record, while each column displays a specific metric or attribute for those items.

A table helps you to break down data (such as ticket volume, CSAT, or chats by country) into straightforward tables for easy analysis.

Comparing metrics for multiple categories or entities.
Aggregating totals, averages, or other calculations by column.
Drilling down and filtering to explore underlying data.
Supports alignment with timezone, filters, and metrics definitions for accuracy.
Exportable for additional offline analysis in other applications.

Table charts are best suited for displaying categorical, granular, or high-detail data where other chart types (like a bar or line graph) would be too cluttered or imprecise.

Timeline

Arranges events in chronological order, often showing each event as a marker (dot, icon, or bar) positioned along a time axis. Events can represent instantaneous points in time (like a ticket being opened) or durations (like a deployment window or scheduled outage).

Timeline charts display these events or periods above and below the central time axis, often with labels or descriptions attached.

Tracking milestones, phases, and dependencies between tasks (similar to Gantt charts.
Visualizing when incidents start and end, how they overlap, and their impact sequences for platforms or services.
Ordering approvals, deployments, and comments for process transparency.
Visualizing completed tasks, weekly goals, or support activity over calendar time.
Presenting key events in product or customer journey timelines.

Toggle

Allows you to switch between different grouped or segmented views of the data using a UI control directly on the chart or dashboard widget. Analyze the same metric across multiple groupings by switching between categories such as brand, group, or status, without building separate reports or charts for each view.

Once you've selected the data you want, click Drill into selection. Analyst copilot adds a new filter that restricts the report to only the items you selected.

Switching between performance of each group or region.
Reviewing individual or team-level statistics, such as first reply time and CSAT.
Toggling between brands, products, or channels to see breakdowns in the same visual context.
Checking different customer segments or ticket statuses.

Treemap

Displays hierarchical data using nested rectangles, where the size of each rectangle is proportional to a specific value or metric. Treemaps are helpful for comparing multiple categories and subcategories at once, allowing you to see both the overall distribution and the largest (or smallest) contributors in a single compact visual.

In a treemap, each higher-level category contains smaller rectangles representing its subcategories. The larger the value, the bigger the rectangle. When you hover over or click a rectangle, you can see more detailed information.

Displaying multiple levels of grouping, such as teams within departments or topics within categories, making it easy to understand relationships between parent and child groups.
The size of each rectangle in a treemap corresponds to the value of that item relative to the total, so you can quickly see which categories are largest or smallest.
Unlike pie charts, treemaps remain readable even when there are many items, letting you see all results at a glance.
Identifying where most of your volume or activity is concentrated, and making it easy to spot dominant or underrepresented segments.
Since treemaps are compact and space-filling, they provide a dense overview when space is at a premium.

Waterfall

A waterfall chart visually represents a sequence where each bar indicates an increase or decrease from the previous total.

The chart begins with a starting value (often called the baseline), then shows a sequence of additions (positive changes) and subtractions (negative changes), typically as floating bars, before arriving at the final total (ending value). Connector lines are often used to “bridge” these bars together, reinforcing the idea of a running sum.

Showing how a sequence of changes (both positive and negative) leads to a final value.
Visualizing how individual components contribute to a total, such as breaking down ticket volume, revenue, or bookings by source or by time period.
Comparing results across sequential time periods (like month over month), rather than showing overall time trends.
Analyzing scenarios where you have multiple contributing metrics or need to highlight the impact of each on the outcome.

Waterfall charts are less suited for visualizing trends over long periods or when you need to compare many series at once; other chart types like line, area, or column might be better for those cases.