Customer Insights Visualization Reference

Graphs, maps, tables, and other visualizations available in Customer Insights, the premier analytics tool for used with the Akamai Identity Cloud.
  • Table Visualization. Perhaps most useful when you need to present a large number of data points, and when the actual value of those data points is important (e.g., it’s not enough to know that the March registration numbers exceed the February numbers, you need to know how much those numbers changed.)
  • Column. 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).
  • 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.
  • Scatterplot. 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. 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.
  • Area. 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. Pie charts show numerical proportions; for example, a pie chart might compare such things as traditional login; social logins; and single sign-on logins.
  • Map. 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 essentially all one color and doesn’t communicate much in the way of useful information.
  • Single Value. Emphasizes a single value in your dataset (for example, the total number of users who have registered in the past year). The value displayed in the visualization is always taken from the very first row in your returned dataset.
  • Funnel. 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.
  • Timeline. 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.
  • 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 zipcode datatype.
  • Donut Multiples. 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.
  • Single Record. Displays all the field values for a single record. The displayed record is always the first record in the returned dataset.