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Content Marketing

Topical Authority Metrics to Watch

Is your topical authority strategy paying off? Learn which key metrics to track, like topic share, AI citations and engagement metrics, to see what’s working.

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Congrats, you’ve built topical authority for your website — now, how do you prove it’s working? 

Content marketers can spend months using this SEO strategy to build a website’s holistic trustworthiness, expertise, and credibility in a given content niche, but without the right metrics, it’s impossible to know if you’re on the right path to topical authority. 

In this article, we’ll discuss three key metric areas to track topical authority and how, like all content marketing analytics, they help marketers adjust and optimize content strategy. 

Key Takeaways (TL;DR)

  • Tracking topical authority metrics helps content marketers identify what’s working, what needs adjustment, and how to optimize content strategy.
  • Key metrics include calculating topic share, tracking AI Overviews, exploring engagement depth, and monitoring backlink quality and internal link performance.
  • Start with 2 to 3 key metrics, rather than trying to track everything at once.
  • Metrics should guide content strategy for topical authority, not dictate it.

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Why Measure Topical Authority?

Building topical authority on a strategic topic helps a website to establish the expertise, depth and relevance needed to address its audience’s search intent. 

When done well, it also signals to Google that a website should rank higher than others on that topic. 

Overall, topical authority can result in higher rankings across topic clusters, increased organic traffic and visibility, and stronger user trust and engagement. 

But without measurement, a content marketer (and their stakeholders) won’t know whether the strategy is working. 

Tracking key metrics related to topical authority can help a brand:

  • Determine the ROI on efforts
  • Identify content performance and gaps
  • Check progress against competitors
  • Pivot strategy as needed to reach established goals

While there is no distinct single metric for topical authority, there are three key areas to monitor for signals about your strategy’s effectiveness

  1. Traffic-based metrics
  2. User engagement and behavior
  3. External validation, like backlinks and AI citations

1. Traffic-Related Authority Signals

The most straightforward of topical authority indicators is the change in search traffic since you implemented your strategy, and how much attention you are capturing in the niche compared to your competitors. 

Topic Share 

Topic share measures what percentage of niche traffic your content captures compared to competitors. 

According to Search Engine Journal, you can use keyword research tools to find all relevant keywords for your main topic, then see how much traffic you are receiving related to that keyword cluster (you can also look at it in comparison to the cluster’s potential for traffic volume).

Organic traffic growth by topic cluster 

Track how your total topical content performs as a group rather than individual pages. Monitor the combined traffic growth for all topical pages and use Google Analytics to monitor this metric. 

Featured snippets and SERP features 

If your content appears in featured snippets, knowledge panels, People Also Ask, or AI Overviews, it’s a signal that your topical authority strategy is working, because it is being picked up by Google. Monitor this over time using tools or manual tracking.

2. User Behavior and Engagement Metrics

How users engage with your content provides insight into how relevant and trustworthy they view your site to be. In other words, how well your content answers their questions and solves the issues they are searching for.

Time on page and session duration 

People spend more time reading content from sources they trust. Monitor the time spent on the page, session length, and clicks to related articles, which signal engagement and authority to search engines. 

Internal link click-through rates

Strong internal linking between related topical pages creates a web of engagement opportunities for users that can strengthen your authority signals. Use Google Analytics to check which internal links are performing well and optimize accordingly.

Return visitor patterns 

Use Google Analytics to track what percentage of your topic-related traffic comes from returning visitors versus new visitors. When users return, it's a sign that they trust you and view you as an authority.

3. External Validation Metrics

Signals from outside of your own website metrics show you how often others, including AI systems, are recognizing your expertise in the area and referring users to your content. 

Topically relevant backlink quality 

Focus on earning links from websites within your topic area rather than just general, high-authority sites. Use backlink analysis tools to track the relevance and quality of the sites linking to your content over time. 

AI Overview and LLM citation tracking 

AI-powered searches are increasingly common, and they often cite authoritative sources. Look for your content in Google’s AI Overviews, ChatGPT responses, or other AI-generated answers, and track it over time.

“Brand + Topic” search query combinations

When people search for your brand name combined with topic-related keywords, it’s a strong sign that your brand is associated with expertise in that area. Track these searches with Google Search Console or Google Trends to monitor changes over time.

Final Thoughts on Measuring Topical Authority

When starting out, focus on two or three key metrics, rather than trying to track every possible detail at once. Look for trends over time, not daily fluctuations, and make small, trackable adjustments based on what you learn. 

As with any SEO-related content strategy, topical authority isn’t a stand-alone or a quick fix. It is part of a comprehensive, interconnected content marketing plan that builds credibility, trust, and authentic connection over time. 

Confidently publish high-quality content with the Originality.ai suite of editorial tools, including a Content Optimizer, AI Checker, and Plagiarism Checker.

Then, learn more about topical authority in our top guides:

Melissa Fanella

Melissa Fanella is a writer, editor, and marketing professional with over 15 years of experience in content and messaging for businesses and nonprofits. Her expertise is in crafting authentic, people-first content that is compelling and engaging for audiences and positioned for business goals.

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Frequently Asked Questions

Why is it important to check for plagiarism?

Tools for conducting a plagiarism check between two documents online are important as it helps to ensure the originality and authenticity of written work. Plagiarism undermines the value of professional and educational institutions, as well as the integrity of the authors who write articles. By checking for plagiarism, you can ensure the work that you produce is original or properly attributed to the original author. This helps prevent the distribution of copied and misrepresented information.

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Text comparison is the process of taking two or more pieces of text and comparing them to see if there are any similarities, differences and/or plagiarism. The objective of a text comparison is to see if one of the texts has been copied or paraphrased from another text. This text compare tool for plagiarism check between two documents has been built to help you streamline that process by finding the discrepancies with ease.

How do Text Comparison Tools Work?

Text comparison tools work by analyzing and comparing the contents of two or more text documents to find similarities and differences between them. This is typically done by breaking the texts down into smaller units such as sentences or phrases, and then calculating a similarity score based on the number of identical or nearly identical units. The comparison may be based on the exact wording of the text, or it may take into account synonyms and other variations in language. The results of the comparison are usually presented in the form of a report or visual representation, highlighting the similarities and differences between the texts.

String comparison is a fundamental operation in text comparison tools that involves comparing two sequences of characters to determine if they are identical or not. This comparison can be done at the character level or at a higher level, such as the word or sentence level.

The most basic form of string comparison is the equality test, where the two strings are compared character by character and a Boolean result indicating whether they are equal or not is returned. More sophisticated string comparison algorithms use heuristics and statistical models to determine the similarity between two strings, even if they are not exactly the same. These algorithms often use techniques such as edit distance, which measures the minimum number of operations (such as insertions, deletions, and substitutions) required to transform one string into another.

Another common technique for string comparison is n-gram analysis, where the strings are divided into overlapping sequences of characters (n-grams) and the frequency of each n-gram is compared between the two strings. This allows for a more nuanced comparison that takes into account partial similarities, rather than just exact matches.

String comparison is a crucial component of text comparison tools, as it forms the basis for determining the similarities and differences between texts. The results of the string comparison can then be used to generate a report or visual representation of the similarities and differences between the texts.

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Syntax highlighting is a feature of text editors and integrated development environments (IDEs) that helps to visually distinguish different elements of a code or markup language. It does this by coloring different elements of the code, such as keywords, variables, functions, and operators, based on a predefined set of rules.

The purpose of syntax highlighting is to make the code easier to read and understand, by drawing attention to the different elements and their structure. For example, keywords may be colored in a different hue to emphasize their importance, while comments or strings may be colored differently to distinguish them from the code itself. This helps to make the code more readable, reducing the cognitive load of the reader and making it easier to identify potential syntax errors.

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