These line chart examples show how to use annotations to not only show a trend whether in traffic, conversions or user behavior on your website, but to tell the full story about performance with your data.
I use line charts a lot in my reports since they are one of the best way to report and visualize trends, whether it is a trend in traffic, conversions or user behavior on your website.
When the trend is consistently showing an increase or decrease or staying stale it might be a little easier to tell a story with your data with a simple line chart and couple of comments. But when a trend is fluctuating as a result of marketing activity a chart line might look confusing and the data story difficult to understand.
Below is a line chart showing the trend for social media traffic for 2017 for a certain website:
Traffic from social media is heavily dependent on social media campaigns activity and trend has been fluctuating throughout the year.
In the line chart example I have simply visualized the numbers for traffic from Social media each month in 2017.
Upon exploring I understand that these fluctuation are resulting from advertising activity as well as success of the specific campaign.
In order to fully tell a story with the data and explain traffic increases and sharp drops an analyst might want to include annotation to explain each significant fluctuation in the trend.
Line Chart Examples With Annotations
Here is a line chart example of how I improved the visualization and insert explanation of why we are seeing big increases and decreases.
It might make more sense than the first example however after looking at it I thought that the background color was not a good idea. I also thought there are options to improve the annotations and make the main takeaways more visible at first glance.
So I made a few changes:
I changed the background to white and did some styling to the annotations text. I also decided to remove the data points and the data labels. All this is to try and draw the attention to the traffic fluctuations and the main reasons for these fluctuations which is what I am trying tell with this data.