Creating an outline for niche topics can be very labour intensive. Especially when writing about a difficult topic like bus topologies. Niche topics are mainly picked up by experts or people with great interest on a specific topic, so it's important that the content resonates with the reader. In this use case you will read about how Josef Moucachen from Wevolver used KeyWI for his keyword research. The following topics will be covered:
- What are bus topologies?
- Starting with keywords
- Saving time with search intent insights
- Adjusting the outline according to search engine logic
What are bus topologies?
A bus topology is a connection through a single cable with multiple devices. IoT devices are connected through a single cable, which is called a bus. This network transmits data to any other connected device. The advantage of bus topology compared to other topologies is the price of deployment, easy to understand and flexible to expand. This is how a bus topology looks like:
Starting with keywords
Usually when I start writing about a niche topic I analyse the top 10 SERP pages. I look at the content of other articles to find inspiration and insights for the outline. The issue here is that It’s extremely labour intensive to create an outline this way. Especially in combination with the natural difficulty of topics, like bus topologies. Questions I need to answer as quickly as possible are:
- Which keywords are relevant to the topic according to the user?
- What questions are important search queries?
With KeyWI’s feature called Enhance, I was able to add more longtail keywords to my keyword set. Normally I would check the SERP for more longtail keywords, but with Enhance I was able to add relevant longtail keywords in a few minutes. The only thing I had to do is upload my set and select the number of total longtail keywords to add.
Saving time with search intent insights
Analysing search intent per keyword is an important, but very time consuming task when applying keyword research. A lot of valuable time gets saved with the insights provided by KeyWI. Before I started with KeyWI I manually checked the SERP for a keyword to learn more about what the search intent is. Checking the SERP for every keyword in a set is valuable for defining the content structure, but also time consuming. And time is a valuable source, especially when working in a startup environment. The search intent provided by KeyWI on cluster and keyword level gives me the flexibility to make the right choices in terms of strategy.
Adjusting the outline according to search engine logic
Looking at the clusters that KeyWI generated I could identify which keywords I didn’t consider for some topics. The cluster visual provided insights in some very important topics which I didn’t consider when creating the first outline, such as network topology, physical topology and logical topology. It was clear to me I needed to adapt my first outline according to KeyWI’s data-driven insights. The final outline is both logical for the user and the search engine.
