🔎SERP Analysis
Last updated
Last updated
Interpreting SERP Analysis
When you associate a keyword with a document in Neuraltext, our system automatically conducts a Google search for that keyword, meticulously analyzing the content of the pages ranking in the top 20 positions.
This deep dive into the SERP (Search Engine Results Pages) is neatly summarized in a dedicated SERP Analysis section, accessible from the sidebar. In a SEO Brief you still have the AI Smart Editor.
Here's how you can leverage this analysis to enhance your content strategy.
On the editor's side, you will have three sections:
Write: to use the AI Writing Assistant or write your own article/report.
Overview: SERP(s) overview with Volume data, similarity across SERPs (if you selected more than 1 keyword), SERP widgets, intents, and the results found on the Search Engine Results Page, along with their word count and content score.
Headings: a concise view on all the headings of the SERP. Pretty useful if you want to build your own outline. You can also click on an heading to add it to your own Outline builder.
The outline builder is found under Outline on the right sidebar.
The Explore section, on the right sidebar, is pretty useful to understand:
Competitor's questions: This area curates questions derived from the top-ranking pages, offering you a direct line to understanding your audience's queries and pain points, which is crucial for creating resonant content.
Gain insights from key statistics extracted from the top 20 search results. Incorporating these statistics into your content not only enriches it but also boosts reader engagement by providing compelling, data-backed arguments.
Google's People Also Ask questions
Questions coming from Reddit and Quora, pretty useful to understand what people are asking in the forums and take advantage of this to improve your articles by answering to those questions.
Neuraltext analyzes and extracts relevant topics from titles, headers, and the body of content across the SERP, assembling a comprehensive topic model.
The color coding within this section serves as an intuitive guide:
Grey indicates topics not yet covered in your document.
Green signifies adequate coverage of a topic.
Red warns of potential keyword stuffing, indicating overuse of a topic.
Delve into a detailed list of topics and their corresponding subtopics, ranked by their significance within the overall topic model. This feature not only identifies what main and subsidiary topics competitors are focusing on but also groups these subtopics into clusters for a bird's-eye view of the competitive landscape.
In this section you will see all subtopics extracted from the search results grouped together. The topic cluster are ordered by total contribution to the overall topic model. Clicking on a children topic will show how competitors have used that sub topic. This is extremely useful to quickly see what are the main topics covered by competitors.
As you can see, what you write in the editor will be scored against the topic model. In the example above, you can clearly see the word city is overused.
SERP Scores
The SERP Scores metric (which you can find both in the overview and serp section) evaluates how well competitors have optimized their content based on topic coverage, offering you a benchmark for your own content optimization efforts.
