Spider Impact's Bullseye Chart visualizes how different measurements compare to their baselines. Each chart “slice” represents a different measurement, with colored threshold regions showing performance zones, and data points showing actual values relative to those thresholds.

Shared thresholds mode

By default, all positions around the bullseye chart share the same scale and thresholds. This works well when comparing scorecard items because they’re already normalized to 0-10 scores, or dataset values that share goals, like sales figures for various employees.

In this mode, you select "Set Data Source" on a series like you do on most other charts.

And the Set Data Source menu is the same as other charts as well. You can choose data from Scorecards, Initiatives, or Datasets.

Reference lines and bands

Like other types of charts, bullseye charts have reference lines and bands. When Independent Thresholds is off, you set lines to constant values.

In this example we’re comparing scorecard item scores, so we’ll choose to break up the 10 point scale into 3 equal pieces.

Just like with line, bar, and area charts, you can modify the reference lines and bands. You can choose each ring’s color, and you can set the opacity for inactive segments.

Reference lines are off by default, but you can choose to turn them on and set a color and opacity to draw attention to a special threshold.

Independent thresholds mode

When comparing measurements with different scales – like workplace injuries (per 1,000 employees) versus sales revenue (in dollars) – enable "Independent Thresholds" mode. This allows each “slice” to have its own scale while maintaining visual comparability.

When you enable Independent Thresholds, the series menu changes to display:

• Set Point Data Source - The data to plot (e.g., a specific employee’s sales)
• Set Threshold Data Source - The baseline for comparison (e.g., company-wide averages)

A typical Independent Thresholds bullseye chart uses the same dataset for both point and threshold data, with the point data having additional filters. For example:

• Threshold Data Source: Sales data averaged for all salespeople
• Point Data Source: Sales data filtered to "Salesperson = Russell Corrick”

This configuration shows how Russell performance compares to the overall average across all metrics.

With Independent Thresholds enabled, the threshold lines change to be a percentage of each slice’s Threshold Data Source value. For example, setting a line at 200% creates a threshold at twice the Threshold Data Source values. If your baseline for injuries is 5 per 1,000 employees, that line would appear at 10 injuries per 1,000. In this example the green segment starts at 100% of the threshold data source value.

Let’s further explore independent threshold mode with this example. The bullseye chart on the left shares the same thresholds across all slices. It compares each employees’s average sale size against the average for all employees. Two employees jump out with massively larger sales than the other employees. That, however, is because Kym and Russell do corporate sales, and their deal sizes are always going to be larger than the employees doing retail sales. In this example, the average sale prices for the different employees have different acceptable scales, making this kind of comparison not very helpful.

The bullseye chart on the right has independent thresholds turned on, and instead compares each employee against only the other employees in their department. We can instantly see that Russell, Edmond, and Issac are having good months, regardless of their department.

Repeating bullseye charts

Bullseye charts support repeating values to create small multiples, displaying the same set of metrics across multiple entities simultaneously. In this example, we’re showing the same sales data for United States, Australia, and United Kingdom.

Bullseye charts on dashboards

When you add a dataset filter to a dashboard, it applies to both the threshold and point data in your bullseye charts. This allows you to explore dataset data as you would with any other chart. For example, here we’re looking at each employee’s average sale price this month, and each slice is using a scale of that employee’s average sales for the current year. It allows you to see if the employee is having an above or below average month relative to their past performance.

When we apply a filter for Sales Department, we now see the exact same chart filtered to only show only show Corporate sales. Here it’s showing only Russell and Kym had Corporate sales this month.

Similarly, we can apply a filter to North America, and we now see the exact same chart filtered to only show North American data. Note that filters apply to both Point and Threshold data by default. That means this chart is comparing each salesperson’s average North American sale this month against their average North American sale this year.

If you only want the Point or Threshold data to ignore a dashboard filter, you can just apply a filter for that field to the data source. That’s because any dashboard filters that conflict with point or threshold data will be ignored. Here we’re applying a filter to the Threshold Data Source saying that the country is anywhere on earth.

Now when we set the dashboard to filter on country, that filter only applies to the points. This chart is comparing each salesperson’s average North American sale this month against their average sale this year regardless of country. Now adding a filter changes the points without changing the background thresholds.

Having dashboard filters apply to points, thresholds, or both are all completely valid use cases, and the new Bullseye charts allow you to do them all.