Keyword Research: Study Demand via Related Search Queries on Google
Posted Sunday, April 15, 2023
Keyword research is a powerful tool to study search intent, which represents the current informational demand for a topic. Once you know what people are searching for, you can have a much better understanding of the market and what is lacking within.
You can then use this insight to create targeted content in to attract bigger audiences or to better understand the structure of demand for developing products and services.
In this tutorial, we will present a workflow for conducting keyword research with InfraNodus search engine optimization app. This workflow is also available in-app as a step-by-step guide to all the subscribers.
InfraNodus has a huge advantage in comparison to other keyword research tools. It doesn't just show you tables and rows of data, but it reveals connections and patterns between the keywords. This way, you can see which combinations of words tend to occur together more often in users' search intent. More importantly, text network representation will also reveal the gaps between clusters: keyword combinations that are not used that often, which may indicate an interesting niche or a potential for innovation.
Step 1: Choose a Keyword and Study the Context
The best way to begin is to use the "Contextual Demand" or the "Keyword Research" apps in InfraNodus. Enter a search query and both apps will import related search queries from suggested search queries in Google. (For those of you who don't know, these are the auto-complete propositions that Google provides when you start typing a search query and they generally show what other people who search for this topic also search for.)
For instance, if we perform a search for "keyword research", we will get the following graph:
InfraNodus extracts the top 200 related search queries (search phrases used in the context of "keyword research" — shown on the left) and shows which ones are used more often together in the same context. This is indicated spatially on the graph (the more related the terms, the closer they are to each other) and colors. The main topics, derived using GPT AI, are shown in the Analytics > Main Topics panel on the right. As we can see, the 4 top keyword clusters are:
- • 1. youtube analytics (youtube search)
- • 2. google ads (google planner)
- • 3. SEO tools (free tools)
- • 4. website design
These results provide a pretty good idea about the current informational demand for this topic. People are interested in keyword research for YouTube and Google, also the Google Ad planner tool, and various website optimization techniques. If we are to create relevant content, we should target all of those clusters. However, our competitive advantage will be in connecting those ideas differently than the rest, so we can provide unique content and stand out from the rest to Google crawler algorithms.
Step 2: Enhance Your Data with Google Ad Words Planner Results
To enhance the results obtained above, we can add more related search queries (for instance, taking the inspiration from the clusters above we can search for "youtube keyword research"). We can also add the results from Google Ads Keyword Planner. These results are slightly different to related search queries. Related search queries are the search phrases people use when they search for a certain topic. Google AdWords Planner suggestions are, rather, related topics derived by Google's AI which also cover adjacent topics that may be interesting to the same audience. This is a great way to expand your reach and to understand what else your target customers are interested in.
Once you add a few more search queries, you can filter each of those imports to view them separately using the Statements Filter panel at the top left. This filter also allows you to filter the results by the number of queries, so you can view only the moderately popular results (recommended, as they point to less competitive keywords):
Interestingly, now the results include "Semrush" and "Fiverr", which gives us an indication that our audience may also be interested in keyword research tools for Fiverr (which is a completely unexpected result for us) and also SemRush (a popular SEO tool), how it works, and its alternatives.
In fact, it's a good moment to dig in deeper and explore what exactly people are searching for.
Step 3: Explore the Context around a Search Query
The visualization of keyword clusters generated by InfraNodus is interactive. This means you can jump in any topic and explore it in more detail. For instance, if we're interested to see what people are searching for in relation to Semrush, we can click on Semrush and see all the search terms used with this keyword:
For instance, we can see that people are particularly interested in Semrush alternatives and we can use built-in GPT AI to generate a statement that enhances suggested keyword phrases that contain those terms (see below) — now we know what other competitors exist and may have an idea for the content that could be interesting for our audience (e.g. comparing different SEO tools).
This same process can be done for a few iterations. Study the graph, add some more results, filter, explore, write some keyword ideas down in the Project Notes field of InfraNodus, move on and add more keywords from more sources.
The graph above shows keyword visualization for multiple search queries that we identified during the research. As you can see an interesting topic of "competitive research" came up, which was not evident at the beginning but can be very useful for choosing a possible niche for our content or product idea.
Step 4: Reveal Structural Gaps and use GPT AI to Generate Ideas
One of the advantages of network representation is that you don't only see the relations and patterns but also the gaps between them. It is those gaps where the new ideas are hiding and they are particularly interesting if you would like to not just fit into the existing demand but also produce something completely new.
In order to do that, we can use the Text Analytics > Missing Content (Gap Insight) panel. This functionality looks for the two topical clusters that could be better connected and identifies a gap betweem them, highlightining them on the graph. You can then think of a possible connection that would link this cluster in an interesting way. The idea that you come up with is guaranteed to be relevant as it touches upon the topics that are interesting for your target audience. Yet, you are connecting their needs in a different way, which means you offer something innovative.
For instance, in the example above InfraNodus identified two clusters: one about the "screaming frog" SEO spider (a popular desktop tool for crawling websites) and another one about "long tail keyword researcher tool". What if we combine this in an interesting way? Could we, for example, offer a workflow where you can crawl your competitors' websites to identify long-tail keywords, which are less popular (and, thus, less competitive) and then develop a content strategy that would target these long-tail words?
You can also identify those gaps yourself directly on the graph, select the topics, and use built-in GPT AI insight engine to generate content for you.
The graph above shows a structural gap between the "competitive intelligence" cluster and "youtube keyword research". Those two search intents are not very well connected, but they exist in the same context. So we can then use the built-in GPT AI to generate a business idea that would link them in an interesting way. In the example above, GPT proposes us to think of a service that would analyze content for YouTube channels and perform competitive analysis to identify the perfect niche for entering a certain market. A ready-made business idea on the fly!
Try It Yourself
Try this approach yourself using InfraNodus and your own data:
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