Driving visitors to your site is about much more than just rankings, branding is playing a larger and larger role in acquisition.

In this article we look at the importance of branded searches and how to calculate the split between brand and non-brand search traffic.

Branded traffic is very rarely traffic you’d be happy to have leak through to another site. Aside from ‘reviews’, you’d hope that 100% of branded searches land on your site.

Unfortunately we see that this is not always the case.

Between the floorboards of SEO and PPC there are cracks that rob you of valuable visits, sometimes deliberately, often by simple chance.

In a recent talk at BrightonSEO Rand Fishkin of SparkToro (and previously Moz) suggested that the future of SEO is in the SERPs and less ‘on the site’. Whilst there is a growing trend in enhanced search results, data cards and featured snippets, there will always be a place at the table for digital (organic) brand reputation.

Branded vs non-branded traffic

If I were to offer you 100 new visitors that come via a branded search or 1,000 who come via a generic non-branded search term – which would you chose? I suppose the answer will depend on a range of factors, one of which is your conversion rate. Regardless, it’s likely that exposure to the brand will have increased their likelihood to either convert or to investigate… and then convert!

Understanding what percentage of your traffic is branded will help you to understand just how much effort you have to put in to brand protection and brand reputation management.

Measuring your brand/non-brand split

We’ll walk you through how to see your brand vs non-brand split without any need for paid tools or insane extrapolations of Google Analytics data.

1. Start by ensuring you are logged into a Google account with access to your Google Search Console (formerly Webmaster Tools).

2. Following this, visit https://datastudio.google.com in a separate tab and create a fresh blank report…

start new report

The phrase: “It’s so fine and yet so terrible to stand in front of a blank canvas” comes to mind!

3. Now, click in the bottom right of your screen to create a new data source.

create new data source

4. Select the Google Search Console connector as shown below.

connect to search console

5. You may need to provide authorisation to Google Data Studio.

data-studio-connect

6. Now find your site in the list and select URL Impression and the ‘Connect’ button in the top right.

url-impression

7. You should now be presented with a list of fields, however, we want to make a new one! Simply click the ‘add a field’ button.

8. The code you need can be seen below. The expression we have used will look for any search term containing either “zaz” or “le me” (from Zazzle Media) – you’ll need to replace these with your brand terms. Keep this simple and short.

CASE WHEN REGEXP_MATCH(Query, (".*zaz.*|.*le me.*") ) THEN "Branded" ELSE "Non-Branded" END

You can add more between the speech marks with a |.*text here.* expression.

9. Give the field a name (such as ‘Branded Split’), save it and we’re almost there!

10. You may still need to add the data source to the blank template, select it from the list to the right and click “Add to Report”

add-data-source

11. Your report will change into a grid and you can now make your chart show brand vs non-brand.

12. Select the type of graph you want to use (I favour area graphs personally) and draw an appropriately sized rectangle. When the graph is selected you’ll need to adjust the ‘Data’ menu to show:

  • Time Dimension: Date
  • Breakdown Dimension: Branded Split (or whatever you called your field)
  • Metric: URL Clicks

13. You can adjust how the chart displays in the ‘Style’ menu. Below is my example where I have disabled stacking to show separate lines.

url-clicks

14. Changing your metric to ‘Impressions’ can allow you to quickly see the difference between the two, it often helps to highlight where you may rank for a huge keyword that is unrelated such as a celebrity or a similarly named brand.

impressions

15. While this information is useful you may find it difficult to understand how the data averages out – as such, a pie chart may provide you with a clearer ratio.

pie-chart-split1

If you’ve enjoyed this dabble into Data Studio, let us know in the comments and we’ll be sure to produce more insightful posts using it.

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