Track Brand and Non-Brand Traffic in Google Data Studio

Track Branded and Non-Branded Traffic in Google Data Studio

Few weeks ago, buzzing around on Twitter, I stumbled upon this thread by Adam Volk talking about brand search terms and traffic, and how to understand if the arrow up and green percentage in reports is because of you (aka SEO team) or brand dept.

For SEOs, understanding the difference between brand and non-brand traffic is critical (was that 10% bump because of your awesome SEO efforts or were more people just searching for your name?)

Fortunately, there's a way to do this in Google Search Console (GSC) and @semrush.
— Adam Volk (@serptwerp) January 27, 2022

Here’s where it all began

I really enjoyed his thread, and I think the solution Adam purposed is definitely useful.

Anyway, if you are collaborating with people not expert using Search Console, it could add some friction point; so I prefer keep track of the evolution and the segmentation between brand and non brand traffic and terms using Google Data Studio both for reporting and monitoring purposes. I’m really in love with GDS reporting because:

It uses GSC data, so you’re sure your data source is legit.
It’s set and forget: you set up the slide one time only, and then it’s indipendent and doesn’t require any other extraction or action from you, like going directly to GSC.
It comes to you every monday morning, if you set e-mail delivery. Or whenever you want.

So I decided to pack a little guide with examples and template on how to measure brand and non-brand traffic in Google Data Studio.

Definition

So, a very quick definition of branded and non branded to begin:

Branded Traffic: All the traffic formed by queries that contain your brand name or your products’ name. IE: [nike] or [iPhone 13]
Non-Branded Traffic: All the traffic that is NOT made of queries with your brand name or your products’ name mentioned. IE: [man shoes] or [smartphones]

Why understanding the ratio between branded and non branded traffic?

Well, there are at least three good reasons that pops in my mind:

It clearify how strong your brand marketing is, and how recognized your brand is. That’s for the branded traffic of course.
It helps you understand how effective your actual SEO efforts are. More on this here.
It trows lights on how much new users (and, then, customers) you can expect from SEO. That’s because, usually, non-branded queries are what people serach for when they don’t have a clear idea in mind about what company or product can fulfill their needs. So they’re problem-aware or solution-aware, but surely not product-aware.

Setting up the dashboard

First of all, collect all the varieties of your brand name you can find using Google Search Console, Google Ads Search Terms, or a tool like SEMRush, Ahrefs or Sistrix, and pack together in a regex.

Let’s say you’re doing this for Apple. It may turn in something like:

apple|aple|appple|applle

Then, import in GDS a Site Impression Table of the GSC property you want to put under the lens. Import the Web Property Parameter.

Then, it’s sufficient create a very simple calculated field . Here’s the code:

CASE
  WHEN REGEXP_MATCH(Query,".*(brand_name_1|brand_name_2|brand_name_3).*") THEN "Brand" 
  ELSE "Non Brand"
END

Pay attention here: you should use .* and () both to be sure you can get not only KWs that are exact match of the brand name, but queries that contain brand terms as well. I usually like to group them in “Brand” anyway, because those queries are related to the brand.

In Regex, .* means everything; so in this case means: “everything that’s before or after the capture group”.

() is a capture group. It makes Regex match the group of characters in the given order; so it’s necessary to exactly match the string “apple”.

The pipe |, in the end, means OR in Regex.

Insight: Regex lessons

A useful template in GDS

It’s time to share the template I set up every single time to keep track of the changings.

We have:

A date picker, to set the timeframe you will.
A Time Series chart to monitor the evolution of our previously created calculated field: Brand and Non Brand.
A Pie Chart to have a quick snapshot of the composition of the traffic in the chosen timeframe.
A table with two dimensions: Query and Brand vs Non Brand, and all 4 GSC metrics. I like to use the table for two different checks:
- Understanding the evolution of a specific term, using a comparison date frame.
- Uncovering the distribution of brand and non brand queries on pages, to understand how much brand traffic impact different pages and stages of funnel. Pay attention here: in order to achieve this, you need to set up a Brand vs Non Brand dimension in a URL Impression table, so you should create another table, and set another calculated field there.