In a previous post, we discussed the importance of monitoring ad campaign metrics and trends, especially during the COVID-19 pandemic. Quickly identifying these trends can help you stay ahead of your competition. Automation is a great way to identify and stay on top of these trends. This will allow you to focus on the more important aspects of analysis and strategy development.
When most people think of automation and paid search campaigns they think of two things: reporting automation and automated bidding. At Statwax, we use both of those strategies, but they only scratch the surface of the potential of automation in advertising campaigns. The third pillar of automation utilization is to drive actionable insights and strategy development. The less time you spend gathering and visualizing data, the more time you have for analysis and campaign strategy. In this post, we’ll walk you through four advertising automation best practices and the tools to help you implement them.
Automation Best Practices
Consistent and documented naming conventions
One of the most important aspects of automation comes before any data pull. To pull and filter data in the most efficient way possible, there must be systematic naming conventions in your campaigns. It’s also important to document all of your naming conventions. That way there is no overlap in the future.
In the table below, there are five paid search campaigns listed. See how they can be named to allow for advanced filtering and segmentation.
|Nursing||Non-Branded – Nursing – Search|
|Branded||Branded – Search|
|Nursing #2||Nursing – Display|
|Schools||Non-Branded Nonprogrammatic – Search|
|Competitor||Competitor – Search|
Define the problem
The data team at Statwax knows that time is valuable so that’s why we spend significant time defining the problem before diving straight into a data project. A clearly defined problem helps avoid wasted time, confusion, and incomplete data.
Checks and balances
Automation can save endless amounts of time, but it cannot be solely relied upon to catch all problems. There must be checks and balances put into place to catch any errors made by the automation tools or from missed campaign naming conventions. These errors are rare but they do happen and missing just one can undermine the integrity of the project. The best way to accomplish these checks is through additional automated checks, as well as occasional manual checks.
Build for the future
Nothing is more frustrating than completing a data project and then realizing two weeks later that it’s irrelevant. This is why it’s important to think weeks, months, and even years down the road when creating an automated data project. Your spreadsheets must be able to be easily maintained when a campaign name is changed, a campaign is added or removed, or your efforts are expanded to a new advertising platform.
The first step in data automation is having someplace to gather and store data. At Statwax, our primary tool to do this is Google Sheets. The integrations and add-ons that Google Sheets has make it the most efficient way to gather data. There are limitations to the amount of data that can be stored in Google Sheets but we very rarely hit this limit.
Perhaps the most powerful tool that we use at Statwax is Supermetrics. Supermetrics is a paid license that allows you to pull data to Google Sheets from a number of platforms including, but not limited to, Google Ads, Google Analytics, Microsoft Advertising, Facebook, Twitter, and Salesforce. Supermetrics allows us to focus on analysis instead of manually pulling in endless data reports from the platforms.
Google Data Studio
Now comes the fun part of when we get to visualize the data. The platform that we use is Google Data Studio. It’s similar to other data visualization platforms such as Power BI and Tableau. The two primary benefits that Google Data Studio offers over its competitors is the ability to easily share externally and it’s completely free to use.
To utilize and visualize data in Google Data Studio, we typically gather and organize data using Supermetrics and Google Sheets. Supermetrics also has a direct connection and integration with many platforms straight to Google Data Studio. Data Studio is a powerful platform that allows our Ads and Data Teams to quickly analyze data to inform future campaign strategies.
There are a plethora of coding languages that can be used to automate your digital ads data analysis. Here at Statwax the data team primarily uses R. Data gathering is automated by either using an API to the ad platform or using an R package to pull data from Google Sheets. R allows Statwax to perform analysis that goes far beyond basic Excel.
R can be used for one-off projects, but the primary use is making internal tools. These tools are set up to refresh data every day and are distributed to the Ads Team. The Ads Team saves time by not having to pull the data themselves and it gives them deep insight into each account so that they can make the right optimizations. Some of the internal tools we’ve built in R include n-gram analysis, a budget optimizer, and lead forecasting.
Using the principles and tools listed above, the Statwax Data Team has created a robust number of automated data projects that help both the Ads Team and clients implement actionable insights. Some of the projects include dayparting, device breakdowns, demographic analysis, and n-gram analysis. If you’re interested in your team taking the next step in data analysis, reach out to us, we can help you implement these automation strategies.