Data Analysis In Paid Media

While I’m sure most professionals that work in paid media are no stranger to spending hours in Excel, I believe that there’s a benefit to taking one’s data analysis skills to the next level.

I’ve found that even a basic understanding of data structure (building tables and joining data) has helped me to uncover deeper insights for clients which ultimately help to improve performance.

In this post I’ll dive into:

• One common scenario most paid media practitioners face where data analysis skills come in handy

• My favorite analysis tools that I use in my day-to-day

New Measurement Models Require More Data Knowledge

As Google plans to remove third party cookies on its Chrome browser, marketers will be required to use multiple data sources to (most) accurately measure performance.

Relying on the data within Google Ads or Meta won’t be enough anymore given that these platforms will most likely have greater gaps in their own measurement attribution. This means advertisers will need to rely on multiple data sources to paint a clear picture of what’s actually happening.

In-platform data on its own is useful to get a good understanding of leading indicator metrics like CTR, but cost data from ad platforms needs to be combined with lead and/or revenue data from another source to get a sense of true media ROI. This other source is usually a brand’s CRM, or potentially a data warehouse if lead and revenue data is being modified based on incrementality test or MMM results.

Regardless, CRM data doesn’t always match up nicely in a 1:1 fashion with ad data. These two sources might need more than a VLOOKUP to piece them together.

This is where understanding how to build tables and join data can come in handy. I’ve found this to be helpful when setting up reports for my clients, and analyzing any lead or revenue data on an ad hoc basis.

I learned a lot about these concepts and processes through the intro SQL courses on Datacamp. Even if you don’t plan to work in a data warehouse and actually use SQL, the concepts are highly transferable to work that can be done in other tools like Excel.

My Favorite Business Intelligence Tools

Every tool has its time and place, but the two that I find myself using most frequently (that aren’t a spreadsheet) are Looker Studio and Power BI.

I use Looker Studio for my routine client reports because:

• It’s a free tool that anyone can access

• The visualization features are intuitive and great for providing context over time

• There’s an easy integration with HubSpot that lets me embed a Looker Studio report within a HubSpot dashboard

Each of these features help me to provide my clients with impactful insights in a place that’s easy for them to access.

In addition to Looker Studio, I also love nerding out in Power BI. The big catch here is that Apple users can’t access Power BI on their desktops (thanks Microsoft), so I like to use this tool for analyses where I don’t always necessarily need to share the file itself with clients.

I like to use Power BI when working with multiple datasets that are a bit more complex. The benefit of Power BI over Looker Studio is that Power BI allows users to create relationships between multiple tables, which unlocks an additional layer of insights when working through analyses.

I’ve found that a quick video outlining my analysis methodology when using Power BI is helpful to provide my clients with the context needed to understand where my recommendations are coming from.

Wrapping Up

I believe that as the industry continues to change, working through more advanced data challenges will become a common practice among advertisers. While I don’t think we’ll be required to jump into any heavy data science, a basic understanding of building and joining tables has been helpful in my own day-to-day work.

Have questions, considerations, or critiques? I’d love to hear them! If you’re reading this via email, just hit respond. Otherwise, you can find me on LinkedIn.