Customer Insights visualizations

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This documentation applies to the following Customer Insights roles: Developer

Make sense of your website data by using one (or more) of these Customer Insights visualizations. A visualization is simply a way to present data, be that in a chart, a graph, a table, or even a map or word cloud.

• Legacy table visualization. Most useful when you need to present a large number of data points, and when the actual value of those data points is important.

• Bar visualization. Data is displayed as a set of horizontal bars, with the width of each bar proportional to the underlying data (the higher the number the longer the bar).

• Column visualization. Data is displayed as a set of vertical bars, with the height of each bar proportional to the underlying data (the higher the number the taller the bar).

• Scatterplot visualization. Shows the correlation between two variables; for example, a scatterplot might plot logins by time of day, with each dot representing an individual login.

• Line visualization. Displays information as a series of data points connected by lines. Typically used to illustrate data trends over time.

• Area visualization. Represents cumulated totals over time. The area chart is similar to a line chart except that the space beneath the line is filled in to help emphasis the overall totals.

• Pie visualization. Shows numerical proportions. For example, a pie chart might compare such things as traditional login; social logins; and single sign-on logins.

• Map visualization. Plots data based on geographic region.

• Single value visualization. Emphasizes a single value in your dataset (for example, the total number of users who have registered in the past year).

• Funnel visualization. Typically used to show the reduction in data from one phase of a scenario to the next.

• Timeline visualization. Compares 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 visualization (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 zipcode datatype.

• [Static map visualization (regions)](doc:the-static-map-visualization-regions-1. Provides a way to map data by country or by US state.

• Donut multiples visualization. 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.

• Single record visualization. Displays all the field values for a single record. The displayed record is always the first record in the returned dataset.

• Boxplot visualization. Typically used to illustrate the distribution of data within a group. In turn, this is usually done by dividing that group into quartiles.

• Table visualization (new). An update to the legacy table visualization.

• Waterfall visualization. Shows how a set of values – some positive, some negative – combine to result in a final total.

• Word cloud visualization. Uses different font sizes to indicate the relative frequency of a term.