Visualize Your Ahrefs Content Gaps: Competitor Research Guide

Ahrefs content gap analysis helps you see the keyword combinations used to find the websites of your competitors that you don't rank for. Identifying those keywords can help you understand how you should improve your content to rank higher than your competition for specific search queries. 

However, the results are presented as a table and that's where you can dramatically improve the quality of the insights using the InfraNodus content gap visualization. This will help you see the most prominent keyword patterns you should target.

For example, here is the Ahrefs table of the search queries used to find the top 8 pages for "ahrefs content gap analysis" where those pages appear on the first 2 pages:

As you can see, it's a list of 49 keywords, which is already quite hard to read and to get patterns from. This gets even harder if the number of keywords increases to 1000s. Using InfraNodus, we can visualize this list and quickly see the most important patterns in those search keywords.

1. Visualize the content gap keywords as a graph

In order to visualize the content gap keywords report as a graph in InfraNodus, we can export the table as a UTF-8 CSV file. We can then import this CSV file with InfraNodus and visualize it as a graph.

The keywords are the nodes, their co-occurrences are the connections between them. We apply graph theory algorithms to identify the main topical clusters and rank the keywords by their influence (the bigger the keyword, the more different topics it connects).

As a result, the prominent keyword combinations become much more visible. We can also see the AI-generated names for the clusters, which helps us have a high-level overview of the main topics:

We can see that the main topics that those pages rank for are "Content Insights", "Competitor Analysis", "Search Strategy" and "Website Evaluation".

Therefore, if we wanted to rank for this particular search query ("content gap analysis"), we also need to use "keyword analysis", "competitor research", "content strategy" and other keyword combinations on our website. 

2. Reveal the context around the content gaps

We can explore the content gap topics in mode detail by removing the top search terms from the graph to see the context where they are used, and then highlighting the search query combinations with a higher search volume (from 800 and above).

When we remove the top keywords from the graph, all the metrics is automatically recalculated, so the latent topics that would not be visible otherwise emerge.

We then apply the Filter by "Tag volume: 800 and above" and "Tag volume: 100 to 700", which helps us see which latent keyword combinations have a relatively high search volume:

We can then see that if we want to rank for "ahrefs content gap analysis", we also need to focus on "ahrefs competitor research" to signal to Google that we cover those important concepts as well. There is another cluster on analyzing the website's traffic, which we can target as well to show how this approach could also be applied to understanding which pages underperform in Google search results. 

Usually, it will also help to perform further content gap analysis to identify the gaps in search intent and SERP to see the other topics you can include to maximize informational gain. 

3. Find what your competitors are missing

Finally, you can use the graph that you built from the Ahrefs content gap keywords to identify the topics your competitors are not talking about. Bridging those topics could help to improve the rankings for your pages on Google because you'd be covering the important topical clusters but adding something unique to the discourse.

In order to do that, load the graph of the Ahrefs CSV file imported in InfraNodus and go to Analytics > Content Gaps > Highlight to see the topics that could be be bridged:

In our case, we could focus on linking "Traffic analysis" and "SERP ranking check" — for instance, making a blog post on the various tools that could be used to analyze traffic to websites, comparing the data to websites' ranking on search engine result pages, and finding interesting outliers.