• Joseph Lowe

Presenting Your Marketing Data: Five Google Data Studio Pro Tips to Practice in 2020

When you imagine a business meeting, what comes to mind? Is it a one-on-one meeting with your manager? Perhaps it’s an internal meeting with your business partners or the direct members of your team.

That mental imagery is a tad different for me. From when I was a kid to when I started working as a marketer after college, my imagination always took me to what I would describe as a board meeting. Around a large conference table are my peers, superiors and the board of directors as I sit at the head of the table pointing to a large pie chart sitting on an iron easel. I’m nervously flipping through pages like a bad advertising pitch on Mad Men as I try to show the data that the team is attempting to comprehend. I’m sure you’re asking, “Why does this matter?”

My point is that data visualizations have evolved from paper or cardboard. We’ve advanced into easy-to-read digital data charts. In marketing, everyone loves data, whether you like it or not, and one of the most important skills as a marketer is the ability to track and display patterns of data on charts, graphs and the like. Now, it’s impossible for us to create a concise data set that includes every single data point that people will ask about a particular subject because odds are you’d be looking at a 100 to 200 page report. So today we’re going to talk about Google Data Studio and learn how to build marketing charts to satisfy your unique needs through five pro tips you can use as soon as today.

Before we start, I’d just like to thank Michele Kiss from Analytics Demystified as I gleaned some great information from her course on CXL Institute.

Choose your data wisely

Since we’ve been reminiscing lately, have you ever visited a buffet and been told to stop eating with your eyes by your parents? At that age, I’d typically ignore all warnings and dive into my favorite foods – from ribs, sweet mash and cornbread to strawberry shortcake, strawberry cheesecake or strawberry ice cream. Fast forward one hour and I’d probably be whining about my incredible stomach ache.

Well, when you’re creating a chart in Google Data Studio and ask your client, manager or coworker what needs to be in the chart, they will inevitably eat with their eyes too. You can’t fit every data comparison into one report, so the first rule of thumb is to know what type of report you’re aiming to create and which charts best depict the data comparisons you’d like to make.

Check out the graphs above. Perhaps you’re trying to compare the overall amount of conversions against the amount of one specific conversion across a stretch of time, which could be displayed with a scorecard (if you’re more interested in the number or a line graph (if you’re more interested in the trend). Below I’ve listed a few of the most commonly used Data Studio graphs and their use cases. Remember that more is not always better here, and it’s important to think about how the user prefers to interact with the data.

Line charts – Emphasize a trend over time

Bar charts/Column charts – Show the differences between metrics and dimensions

Area charts – Show the differences between metrics and dimensions over time

Sparklines – Convey trend data in a limited amount of space

In-table bars – Convey trend data in a limited amount of space within a table chart

Scorecards – Highlight singular, important metrics

Pie charts/Donut charts – Useful for comparison of three or less things

Map charts – Tell a story of data, though it’s a bit difficult to visualize and understand

Pivot tables – Allows users to show one dimension by another dimension and interact with it

Tap the power of filters

We’ve talked about how filtering can unlock a lot of data within your Google Analytics account, but we haven’t mentioned that filters reach far beyond your Analytics account.

In Google Data Studio, we can filter charts at the report-level, the chart-level and across specific charts within the report. The report-level filter saves you a bit of time if you know the entire report needs to filter out a specific segment of traffic. Rather than creating a chart-level filter for say desktop traffic and applying that one chart-level filter to each chart in the report, you can quickly apply one blanket filter to the entire chart and call it a day. Now – I’ve personally never used a report-level filter because it’s rare that you’re filtering an entire report like that, but if you’re reporting to a certain team within your company this could come in handy.

What’s more common is the chart-level filters that you might see on reports. In the instance above, we’ve got a filter on the left bar chart that only allows traffic with the source google, and it compares to goal completions from the top 5 sources on the right. An important note to keep in mind about these filters is that they only belong to the one report you’re using. That is you will have to recreate these filters if you decide to create another report you’d like to use it on. The workaround for that is copying the report you’ve originally created the filter on and editing from there. The caveat with that is those filters hold true for every report you’ve copied, meaning if you change the filter on one report, all report filters will subsequently change as well. As you start out, this might not affect your reporting too much, but as you expand your Data Studio usage this can quickly become a big issue.

Perhaps the neatest filtering ability in Data Studio is filtering via chart. When editing your chart, if you scroll all the way down your chart editing menu and press “apply filter” on the chart you’d like to choose from, you can interact with the chart and have it filter the report based upon the dimension you’ve clicked. In the chart above, the goal completions line graph is very cluttered and difficult to read right? If you click the google cpc data on the bar graph, then we get that singular line graph to easily decipher what is going on with that specific source of traffic.

Tap the power of controls

Now creating filters is all fine and dandy, but what if your users aren’t as Google Data Studio-savvy as you are? That’s where controls come into play. Controls are essentially filters that you can insert as dropdown boxes within your reporting, so the user can easily choose what to filter out. There are three types: date control, filter controls and view controls.

Date controls - Date controls allow you to choose a certain period of time around the traffic you’d like to analyze.

Filter controls – Filter controls allow you to choose a dimension and metric that you’d like to filter for within the data. A good example of this for small businesses would be cities. If the business can only service a certain area, then typically form fills from random areas will not be relevant.

View controls – View controls are a bit tricky in that the user has to have access to the Google view to switch views in this control. Another caveat is that you can’t pick and choose which views to show on a report. All Google Analytics views that they can have access to will be seen.

Dip your toe into blending data sources

I will not dive too deep into blends, but this can be extremely useful if you’d like to combine data from multiple resources. This is most commonly used if you have more than two data sources (i.e. CRM data vs. GA data vs. keyword data, etc.) and you’d like to compare that data over time in one chart rather than multiple charts in a report. Remember that you can only have one data source per chart unless you blend more than one source together to show trends in traffic. Let’s look at a quick example.

For comparisons sake, let’s say you work for Google and you’ve noticed some odd discrepancies in goal completions by source/medium when looking at your master view and raw view. You decide to blend your data to ensure nothing fishy is going on. You could simply press the blend data button below your data source and get to blending! Two important notes here are that when blending data, Google is going to pay the most attention to the left data source and interpret it as the most important or full source of data and the join keys must match up. This means that the left source of data must be the most complete data source and those join keys must have the same naming convention to match up completely. In this case, we can see that there isn’t too much of a difference in data, so perhaps your discrepancies are related to something else.

Set report alerts

The last pro tip here is a simple one to save yourself some time and stress. We’re all forgetful, and I’ve certainly forgotten to send my fair share of emails. Within Google Data Studio, you can schedule automatic reporting sends on certain days of the week or month so that key stakeholders get their hands on the data on a steady cadence.

In the meantime, what reports are you using to show your data? Are you using filters in your reports? We’d love to hear how you’re optimizing your Data Studio account to better suit your business and clients. Thanks for reading and I look forward to sharing more insights next week! 

This post was created in an effort to complete my CXL Institute Mindegree Scholarship obligation and speak to the materials reviewed in the course. The information is a combination of my previous knowledge and excellent insights from a phenomenal program.

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