Table of Contents
- Design – make your reports sexy
- Add enough features, but not too many
- Learn how to filter reports
- Calculated fields & custom calculations
- Blending Multiple Data Sources
I thought I’d write up this post as I’ve received a few queries via email off the back of the Google Data Studio report I shared with everyone, as to how I’ve come about to creating Google Data Studio reports.
In all honesty, I did not start with creating reports that everyone would like to use, but were useful for me to use and eased up my reporting schedule. I’ve basically developed off the back of actually finding the reports useful myself and what’s saved me loads of time. Of course, what saves me loads of time might not save others loads of time, but on the reverse, it may also be useful to loads of people.
Here are my top tips on creating Google Data Studio reports:
1. Design – make your reports sexy
If there is anything to master in Google Data Studio, it’s designing a report that looks good and has a good user experience. I feel like design has been a key element in making reports that people will actually use, but obviously the underlying data needs to be trustworthy and accurate.
I’ve created some nice looking reports and I’d say my Google Data Studio reports that look at actual trends look pretty good (not yet released this design), but you can get a good feel for design.
Lee Hurst, who I credit as being an expert with Google Data Studio, has created a directory of Google Data Studio reports, and his website, Helpfullee.com is full of information. You’ll be able to get a good idea of how you should design your reports by looking at all the various options in the directory of existing reports. I’m also looking to create & share more templates that I’ve built personally, which I think look amazing (one for everyone to decide on though).
2. Add enough features, but not too many
Include enough features in your report that you’re not bloating your report and therefore your consumers with too much information. I do like to add a lot of things to these reports, so much so that you can identify the problem and also the solution to the problem — due to the amount of data contained within the report. But that’s a given and I’ve balanced that up with what’s relevant. I tend to add as many features as I can think of, then cut the fat at a later stage when I realise that not many people are using various aspects or when I think they don’t really add that much value.
Filtering within Google Data Studio is key. I’ve used filtering to such an extent that I’ve written up a blog post on how you can go about filtering your reports within Google Data Studio.
Pre-filtering reports allows you to create reports that don’t require much change upon load. If you, for example, create a report that filters to show only organic search traffic (assuming we’re talking about a Google Analytics Data Studio report), then the consumer of that report won’t have to change much if all they’re interested in is organic search traffic.
It’s all about making things easier and more accessible in Google Data Studio, as you can easily replicate some of these reports in Google Analytics directly, so filtering effectively allows you to tailor your report to certain audiences — this is how I view and use filtering within Google Data Studio. I would also say that the dashboards look better, are easier to share, and more configurable (drag and drop) in Google Data Studio, so many, many benefits there.
You can find more here about filtering in Google Data Studio.
4. Custom Calculated Fields
Calculated Fields are a way where you can dissect your data for later use, they allow you create custom filters from within your reports and to even filter the data. For example, using a bit of simple regular expression (regex), we’re able to segment our Query data in Google Search Console by Branded searches and Non-Brand search queries:
How, you might ask? You can visit Google’s official guide here on Calculated Fields.
In the above, I’ve used the CASE function that exists within Google Data Studio. It’s a very basic version of SQL really, but it is powerful and effective. You can see the code and syntax I’ve used to group my branded search terms, and then everything else that falls outside of that, goes into the ‘Non-Branded’ category:
CASE WHEN REGEXP_MATCH(Query, ('.*(?i)MSE.*|.*money?saving?expert.*|.*money ? saving ? expert.*|.*Martin Lewis.*') ) THEN 'Branded' ELSE 'Non-Branded' END
This basically will pick up all of the branded search queries that I know of. I’ll be creating a seperate tutorial on how I’ve done the above in due course. Perhaps in the new year or just before.
I’ve then verified this within the code editor – the area where you can create Custom Fields:
This adds the ability to create a drop down that looks at your Brand and Non-Branded Queries. This is just one example of what can be done if there are limitations with the default options. You can see this below:
There are way more examples of what can be done with Calculated Fields – a few come to mind:
- Branded vs Non-Brand
- Commercial vs Non-Commercial
- Long-tail vs Short-tail search terms
- Grouping a set of landing pages on your website together vs another set of landing pages
The options with this are limitless, extensive and exhaustive.
5. Blending data sources
Blending data is super useful for a variety of reasons. I’m still waiting on the Google Data Studio team to fix a bug with Chart Specific Fields when blending data sources (54 people have starred this issue).
Beside the point, the blending option is fantastic for marrying two data sources together. For example, I’ve managed to combine Google Ads and Google Search Console data together. I’ll be putting together a tutorial on how I’ve done this, but in terms of visualisation, you can create something quite remarkable. I’ve managed to get this to work on a search term, country, and device level.
I’ll create a full tutorial on this, as blending these two data sets together requires creating a Calculated Field for device to rename the device names in Google Ads, as they’re named oddly/differently and I’d say more specifically in Google Ads. You can admire the screenshot of the report below:
Admittedly, this part of Google Data Studio is still rather buggy. I’m hoping in 2019, the Google Data Studio team will repair and fix many of the bugs that exist, and in particular the bug with blending two data sources together.
To end with…
Google Data Studio only came out of beta on September 20th, 2018 (and in fact has only been around since 2016), so there is still a long way to go with new feature developments, platform maturity with siphoning off bug fixes, new community connectors and more. I’m excited about new developments and I’ll be able to do with Google Data Studio next year. Stay tuned for more tutorials. 🙂