A line graph (line plot, line chart) shows data as a series of points connected by straight lines. These are commonly used to visualize changes in data over a period of time or to compare different sets of data, like the changes in a company's stock price.
Different types of line graphs include:
While a bar graph shows the differences between different metrics, a line graph shows changes in a metric over time. That’s why a line graph is best used to show trends and changes over time, while a bar graph is best used to compare a metric across different categories.
In some cases, both a line graph and a bar graph can be used to show the same data, but the choice of which one to use depends on the specific data and the message the graph is intended to convey.
Line graphs are used in RevOps to visualize and analyze data related to revenue operations, like tracking changes in revenue, customer acquisition, and churn over time.
Sales and marketing teams may also use line graphs when comparing the performance of different marketing campaigns or analyzing the impact of changes to pricing or product features on revenue. Here, line graphs can help RevOps teams identify trends and patterns in the data, allowing them to make data-driven decisions more easily. Tools like Polymer Search support simple line graphs and multiple line graphs to help users compare multiple metrics in the same line plot.
Unlike other types of charts or graphs, like bar graphs or pie charts, a line graph can provide a more intuitive and contextual way of displaying changes in data.
Overall, the main purpose of using a line graph is to visualize changes in data over time or to compare different metrics. Line graphs are great tools when you need to show changes in your data over periods of time. Unlike bar graphs, line graphs are better at showing multiple smaller changes and trends.
Some common use cases for a line graph include tracking the performance of a business over time, comparing the performance of different products or teams, and analyzing the impact of changes to business processes or strategies.
When creating a line graph, it is important to choose the period that better represents your data and the message that needs to be conveyed.
Other best practices for using a line graph include using clear labels and axes, choosing an appropriate scale, and adding a legend to explain the different metrics.
Some common pitfalls to avoid when creating a line graph include choosing a misleading period from your data, using an ambiguous scale, and failing to label the axes or add a legend.
Line graphs may not be the best choice for visualizing the data, and other types of charts or graphs, like bar graphs or scatter plots, may be more appropriate. When in doubt, it is important to carefully consider the data and the message that needs to be conveyed and choose the chart accordingly.
Additionally, it is important to avoid over-interpreting the data shown in a line graph and to keep in mind that the graph only shows a limited view of the data. You may need to zoom out and compare it with other data points. This will help you get a more complete picture of the underlying trends or patterns.
When using a line graph, it’s vital to consider the type of data being shown, the time period being considered, and the intended audience for the graph.
In some cases, data preparation may be necessary before creating a line graph by cleaning or transforming the data to make it more suitable for visualization.
Different types of line graphs may require different resources or tools, like specialized software or data analysis skills.
Some examples of ways to use a line graph include tracking changes in revenue over time, comparing the performance of different marketing campaigns, and analyzing the impact of changes to pricing or product features on customer acquisition.
To create a line graph using Polymer Search, follow these steps:
To learn more about line graphs and how they can be used to visualize and analyze data, check out these resources: