Previously we discussed the best use case scenarios for some common chart types, such as the bar, line, and pie chart. This time we are going to discuss the best time to use for two of the more advanced and less common chart types: maps and scatter plots.
When you have any kind of location data, whether it’s your members’ location, event location, or customer location, your best option will be to display your data on a map. It is the most intuitive and visually compelling way to look at geographic information.
When to use it:
- To look at geocoded data
- To compare trends across geographic areas
Make it shine:
- Add color shading to your locations for more of an impact. Using color on your map will allow your eyes to quickly see the difference in sales between states, for example, in Virginia versus Colorado.
- Use your map as a filter for other visualizations. If you combine your map with other charts on a dashboard, when you click on a particular state/country, you will be able to see how location affects your different business drivers.
- Use a dual axis map. Let’s say you want to see how sales and profit differ by location. Start with a filled map showing sales, then then overlay that with a bubble chart showing profit. Now you can see how both measures differ by state and region.
Figure 1: Total membership sales is shown by the blue shading on each state. The profit is shown by the color and size of the bubble on each state. You can see that for some states, they have a high number of membership sales and profit, like California, Texas, and Illinois. Also some states managed to have a high profit with less membership sales, such as Oregon and Washington.
- Scatter Plot
Looking to dig a little deeper into some data and try to find relationships between data, such as membership and event attendance? Scatter plots are an effective way to give you a sense of trends, concentrations and outliers that will direct you to where you want to focus your investigation efforts further.
When to use it:
- Investigate relationships between different data.
Make it shine:
- Use the analytics tabs. The analytics tab allows you to add trend lines or average lines to your scatter plot. They can help you determine if there is any correlation among your data.
- Incorporate filters. Adding filters to your scatter plots allows you to drill down into different views and details quickly to identify patterns in your data.
Figure 2: The x-axis shows the totals profit and the y-axis shows the total sales for events. Each shape represents a different age range and each color represents the year of the event. Scatter plots are particularly useful in identifying clusters (similar performance) and outliers (significantly above or below average performance). By adding average lines, we can see many events perform below average while some events and age bins perform significantly higher than the average. For example, ages 50-59 in 2014 (the gray asterisk) had a high number of sales and a higher than average profit. In 2010, several age ranges returned more than 75% profit. The Events and marketing teams can now further investigate the reasons for these exceptional cases for ideas and actions to increase performance across all events.
In the upcoming blogs in this series, we’ll continue to review even more specialized chart types that can be extremely powerful in conveying the story your data is telling. Examples include histogram, text table, heat map, highlight table, bubble chart and treemap.