The Average Position metric in Google Search Console (GSC) is often misunderstood or misused.
For many, it's seen as an indicator of SEO success, but without proper context, it can lead to misleading conclusions. By filtering your data intelligently within GSC or external platforms like Looker Studio, you can transform this metric from a vague and often unhelpful number into a powerful tool for decision-making.
Let's explore how you can apply specific filters to Average Position to gain more actionable insights and drive better results.
1. Filter by Device
Device-specific performance is one of the most significant factors affecting your site’s traffic. As mobile traffic and conversion potential differs from desktop visits, the device filter in GSC is crucial for understanding how your site performs across platforms. More importantly, viewing both devices combined means you miss out on device-specific observations.
Given that the click-through rate differs across devices (see https://www.advancedwebranking.com/free-seo-tools/google-organic-ctr to explore the data for yourself), the difference between having an average position three on desktop is potentially more meaningful than mobile.
Each site/industry has a different mobile/desktop split however, and your user's goal may be very different when searching on a mobile from a coffee shop vs a laptop at work. The average you get from merging the two is well... not super-useful!
2. Filter by Location - Prevent non-target regions changing Averages
Understanding how your site performs in different geographic locations is key, but even more important is understanding how geo-ranking differences impact your averages. By filtering by location or country, you can really understand your visibility in different markets.
In the above example, the average is on page 3 or 4 - a meaningful difference for traffic/click through. rates!
Different regions may have distinct search behaviours, preferences, and competition levels. Filtering by location helps you identify areas where you’re excelling or struggling, so you can fine-tune your approach to different markets.
If your site ranks well in certain countries but poorly in others, it might be time to adapt your strategy - or not! If your revenue is generated from one particular market, excluding other markets from your average position will make the data more useful.
3. Filter by Brand vs. Non-Brand - Brand rankings differ heavily to non-brand
Knowing the distinction between branded and non-branded search traffic can provide invaluable insights into your audience's behaviour. Branded searches are those where the user is already familiar with your brand, while non-branded searches usually come from potential customers who may be less familiar with your site.
The average position for branded and non-branded queries can differ significantly. See below for the example (brand name excluded, but the brand term ranks far better).
By separating them, you can gain a clearer insight into whether you are attracting existing customers or new ones. Typically branded terms would be expected to be somewhere <5 average position, whereas non-brand could be anywhere.
If you are trying to diagnose the possible cause of traffic/ranking drops, splitting brand/non-brand can help speed up the process considerably.
4. Filter by Intent Type - You have different competitors by intent
Search intent plays a pivotal role in determining how competitive a keyword or query will be. By filtering your data based on intent type (informational, navigational, transactional), you can refine your strategy to target users at different stages of their journey.
This can be done in the same way as the brand/non-brand queries above. For a quick way to classify queries you can use this handy guide - it won't be perfect, but it will help show you how you can achieve this effect with your own data. Tweak to make it more useful for your use-case.
Ranking for different intents requires different strategies and the outcomes of users searching around "information" searches may be distinctly different to "transactional". If you rank high for information searches, but low for transactional (for example) your overall average position will look better, but commercially you may still suffer.
Your business model/website type will impact what is useful to you here - but being aware of the differences helps you develop a more useful understanding.
5. Filter by Page Segment - Not all pages are equal
Not all pages on your site are created equally. By grouping and filtering your data by page segments—such as blog posts, product pages, or landing pages—you can identify which areas of your site are performing best and which need further optimisation.
In the example below we're (again) using regex to compare blog pages with product pages:
Each type of page on your site serves a different purpose, and the way users interact with them varies. By analysing performance by page segment, you can tailor your SEO strategy for each section of your site.
If your blog pages are performing well but your product pages aren’t, focus your efforts on improving the content, UX, or SEO of your product pages to drive more conversions. Larger organisations may even have separate teams managing different sections of a site, so this kind of segmentation may also make the data more useful/relevant for various teams you need to collaborate with.
6. Understanding the Impact of New Pages/Content on Average Position
When you launch new content or pages, you may notice a dip in your overall average position. This is because new pages often take time to gain traction and rank well, and therefore your average position gets dragged down.
Recognising this dynamic helps set realistic expectations - and most importantly stops you rushing to unhelpful conclusions and assuming new content has been a bad thing (which it seldom is).
It’s essential to understand that new content may initially perform below your site’s overall average. This is not necessarily a sign of failure; it’s a normal part of the content lifecycle as search engines index and rank the new page over time.
When launching new content, be patient. Over time, new content (done well) will likely boost your rankings and drive additional traffic. Ensure you set realistic expectations for stakeholders and clients.
If you want to understand if the new content is likely having a positive/negative impact, you can exclude (regex does not contain filter) new pages from the overall data and see if the overall average position is impacted over time.
Making Avg Position a More Powerful Metric
By applying these filters—device, location, brand vs. non-brand, intent type, page segment you can turn the Average Position metric into a much more powerful tool. These insights will allow you to make informed decisions that lead to sustainable growth in search visibility and engagement.
What's more, consider Average Position as a metric to use with clicks and click through rate. Another great way of identifying problems quickly if you monitor these three metrics together, any changes that impact one over another may help you see what is happening.
For example, average position doesn't change, but clicks do - user intent may be the cause. Average position and clicks are the same, but CTR drops - then you might be ranking for a highly irrelevant term. There are many such combinations to give you more insights.
Don’t just rely on a generic view of your Average Position—filter, segment, and analyse to uncover deeper insights that drive your SEO strategy forward. With the right approach, you can optimise your site more effectively and set yourself up for long-term success.
P.S. Stay tuned for a Looker Studio Report to help you achieve this outside of the GSC User Interface!
