Customer Insights includes the visualizations shown below. Click any of the thumbnails for more information about the visualization and then, in the popup window that appears, click the visualization reference link to access detailed configuration options for the visualization.
Table visualizations are most useful when you need to present a large number of data points, and when the actual value of those data points is important.
Tables are good at reporting large quantities of data but are not as well-suited to reporting data trends: it can be difficult to look at a table of numbers of immediately grasp whether registrations have been increasing or decreasing.
Data is displayed as a set of vertical bars, with the height of each bar proportional to the underlying data (the higher number the taller the bar).
Similar to a column chart, but bars are displayed horizontally. As a general rule, you can display more data points in a bar chart than you can in a column chart.
Bar charts are also more suitable if your data points have long category names: United States of America fits better horizontally than it does vertically.
Shows the correlation between two variables; for example, a scatterplot might plot logins by time of day, with each dot representing an individual login.
Scatterplots do a particularly good job of helping you identify data outliers and anomalies.
Displays information as a series of data points connected by lines. Line charts are typically used to illustrate data trends over time; for example, you might compare the number of new registrations over the past year with the number of account deactivations.
Represents cumulative totals over time. The area chart is similar to a line chart except that the space beneath the line is filled in to help emphasize the overall totals.
Pie charts show numerical proportions; for example, a pie chart might compare such things as traditional login; social logins; and single sign-on logins.
When selecting a pie chart, keep in mind that you can only compare items on a single metric, and that the number of "slices" in a pie should be limited: if you have too many slices, you lose the ability to do the at-a-glance analysis that makes pie charts useful.
Pie charts are most meaningful when the slices in the pie add up to 100%.
Plots data based on geographic region. Mapped data is most useful when there are definite high data points and definite low data points: if all your data clusters in the middle, then you run the risk of showing a map that is basically all one color and doesn't communicate much in the way of useful information.
Emphasizes a single value in your dataset (for example, the total number of users who have registered in the past year).
Can optionally compare the displayed value to a second value; for example, to show the percent increase (or decrease) in registrations from one year to the next.
Typically used to show the reduction in data from one phase of a scenario to the next. For example, a funnel might track the number of people who log on to your web site vs. the number of people who register for your website vs. the number of people who verify their email addresses vs. the number of people who then log on to the web site for the first time.
Funnel charts are composed of a single set of numbers. If your dataset includes multiple columns, the funnel chart will use the values found in the first column.
Timeline charts compare items over time. For example, you might want to track the length the length of time between registration and first login for a set of users.
Static Map (Regions)
Provides a way to map data by country or by US state. To use the static map the first column in your dataset must meet one of the following criteria:
- It uses the zip code datatype (for the Austin, San Francisco, and New York City maps).
- It uses the string datatype and contains the names of US states (for the US map).
- It uses the string datatype and contains two-letter or three-letter ISO codes for each country (for the world map).
Static Map (Points)
Maps data by postal code or by location. To use this map type, the first column in your dataset must use either the location datatype or the zip code datatype.
Donut charts are simply pie charts with a hole in the center (the hole is typically filled with a label of some kind). The "multiples" part of donut multiples comes from the fact that a visualization can contain more than one donut chart.
For example, you might have a series of donut charts showing registration and login activities, one for each day of the week, or one for each month of the year.
Displays all the field values for a single record. The displayed record is always the first record in the returned dataset.