If you aren’t already exploring Google Data Studio, you definitely should be—this free tool empowers marketers with awesome visual reporting capabilities. Previously, we discussed generating Google Data Studio reports for high-level decision makers. This post will dig into examples of detailed reports for marketing teams to evaluate the success of your efforts and allocate resources accordingly.
After defining the Audience for your reports and determining the kind of information to monitor, an appropriate graph should be chosen. Each type of graph has different strengths. Line charts are good for comparing changes over a period of time, while bar charts demonstrate differences among groups. Scorecards summarize a single metric and tables are beneficial when comparing related items with one or more metrics.
Here is an example of a detailed report prepared for Stanford School of Engineering. It contains two tables packed with information on the performance of their content goals.
Email Campaign performance is a crucial KPI. Judging the effectiveness of your email campaigns can help you make adjustments to your strategy and maximize results. Below is a report used by the Texas A&M Foundation to track their Open Rates and Click Thru Rates over time.
Take into account how these wonderful charts are going to be viewed by your Audience. Will they be emailed and viewed on a computer or projected onto a large screen in a presentation? If this information will be projected consider making everything as large as possible. Don’t forget to label the chart and the data! The size and style of any labels should be clear and the font very readable. People with dyslexia can benefit from serif fonts and avoiding true reds and greens will aid those affected by color blindness.
Take advantage of some of the special add-ons that Google Data Studio offers. Using both the right and left axis on a bar chart can be very effective. Don’t let the fact that the scale can be different throw you off. Keeping two lines on a chart allows you to see any sudden spikes or dips in the relative performance. Below is an example of a line chart with two different axes as depicted by the red arrows.
The left axis represents Sessions in light blue and the right axis represents total Pageviews. While the scales are different, you should consider presenting the chart in this manner. The fact that the two lines closely mirror each other shows that the website material is driving Pageviews at a consistent level. Any striking differences in the two lines would call for further investigation as to the cause. If you are prepared to explain the two axis lines, this is a better choice than choosing a single axis for the chart.
Above we see the result if we only choose a left axis. The relationship between Sessions and Pageviews is less clear. It can appear that Pageviews are performing at a higher rate than they truly are.
You can access this capability in the Style panel of the report.
If you have been using Google Data Studio, you may have seen an option in the Style panel titled “Log Scale” like this:
What on earth is this? So glad you asked! A log transformation is a statistical method for dealing with data that consists of several items, one of which is much larger than the others. It maintains the relationship between the different numbers while dampening the appearance of the largest category and amplifying the smaller ones. Here is an example of an email analysis that includes # Sent, Open Rate, Click Rate, and # Unsubscribers on a normal scale.
The chart is dominated by the number of emails sent and it is nearly impossible to view the relative performance of the Unsubscribers. But if we check the Log Scale box we see:
With the labels added to the chart, we can still see the actual numbers of emails in each category, but we can also get a better interpretation of any spikes in the relative performance of Unsubscribers by using Log Scale.
Google Data Studio is a wonderful tool for exploring and presenting your data. The more you use it the more insights you can derive! Looking for more information on high-level reports for decision makers? Read Part I of this series on Google Data Studio for higher education.