3 Google Data Studio Shortcomings for PPC Analysts
Since coming out of beta, Google Data Studio (GDS) has become a popular tool that allows for beautiful data visualization and dashboarding. As digital marketers, more specifically pay-per-click analysts, it’s imperative that we communicate our impact and results in a compelling, attractive, and concise fashion.
Before getting into the weeds, I was GDS’s biggest fan. Gorgeous and collaborative reports, stellar data displays—what else could you need? At first glance, it appears to be a user-friendly, intuitive tool that allows you to create branded and interactive reports and share them with others. And in some cases, this holds true. For basic PPC reporting, this platform is extremely useful and can save time and prevent unnecessary communication.
However, once you’re faced with more complex reports, Google Data Studio falls short of the user-friendly tool it claims to be.
After countless hours vetting the tool and seeking support, it seems there simply aren’t enough resources to help inform a decision about whether or not GDS can actually fit your needs—which is where this post comes in handy. Hopefully, I can share what I’ve learned and provide insight on a few rather big gotchas that are worth considering before you get consumed in the labyrinth that is Google Data Studio.
One of the biggest downfalls of GDS is also one of the most important and consistent tasks of reporting on PPC marketing results: data blending. Coming from a multi-faceted agency that offers multiple service lines, it’s critical that I provide my clients with valuable insights about each service in an organized and efficient manner. One of the most basic ways GDS falls short in this arena is only supporting up to four data sources.
For a client who has Google Ads, Google Analytics, Microsoft Advertising, and social ads, connecting a customized Google Sheet—or including additional insights about call tracking—is simply not an option. In this case, you’re left with no option but to break out tables and charts, leading to unnecessary clutter and confusing displays of information.
While the limit on data sources can be tedious and inconvenient, much more severe are the flaws within the grueling process of data joining. More specifically, it is necessary that all dimensions in non-primary data sources join with the primary source as part of the data linking, or data gets aggregated incorrectly. GDS uses a left outer join, creating the possibility that data could be excluded if the desired dimension values don’t have a match across all data sources, or if they don't exist in the primary data source.
For example, this shortfall can become obstructive when you need to use Google Analytics and Microsoft Advertising data, or when you have not set an ads campaign name exactly as your campaign name in Google Analytics. For tasks such as calculating a Google Analytics value or aggregated cost metric for a certain set of campaigns, it would be necessary to join with both the date and the campaign, and if your campaign names don't exactly match what's in Google Analytics, the filtered data won't be accurate.
Replicating Blended Data
Another nuisance of data blending with GDS? After finally persisting through the data joining process and creating a functional, blended data source, that source is only available in the report in which it was created. To use the same data source in other reports, you must recreate it from scratch or copy the connections—which opens a whole new can of worms.