Topic Modeling vs. Topic Clustering For SEO: Why Both Are Important
Quick Summary: This article delves into the innovative content strategies of topic clustering and topic modeling, which are reshaping SEO and content creation. Topic clustering involves organizing content into a hierarchical structure centered around broad "pillar" topics, which enhances both user experience and search engine visibility. This method organizes content in a way that signals to search engines the comprehensive coverage of topics, potentially boosting domain authority and organic traffic.
Wouldn’t it be nice to glean insights from your customers with a few keystrokes? Wouldn’t it be nice to organize content for your products and services in a way that search engines see as the most reasonable? Wouldn’t it be nice if we were older…
What is rapidly becoming popular is topic clustering and topic modeling — concepts that are reshaping the way we research and create web content. Topic clustering touches upon the realm of UX, SEO and performance while topic modeling is a research and ideation framework.
So what’s the difference?
While topic modeling offers a panoramic view of content themes, keyword clustering zooms in on particular search phrases to create a web of authoritative content on your site. Both can play critical roles in your content development strategy, operating at different echelons of content analysis.
First, we will explore topic clustering and why it is crucial to your SEO efforts, especially if you are starting a new site without a ton of authoritative backlinks. Then we will discuss topic modeling and it’s importance.
What Is Topic Clustering?
Topic clustering is a strategic blueprint for content creation and structuring. Its power lies in creating an organized hierarchy of related content around a pivotal theme — called “pillar topics.”. This blueprint not only elevates the user journey but it also can create positive signals to search engines.
In a topic clustering model, your website is organized around these pillars. These are typically broad and highly sought after keywords and subjects, and act as a hub for the subject matter.
From that page, more specific related content is created and linked as spokes. From those “sub-pillar” pages, the more specific subject matter is linked, thus creating a top-down pyramid type of content structure. Another way to look at this would be a “hub and spoke” model. A visual is referenced below!
Signaling Topical Authority To Search Engines
Topic clustering, simply put it, is declaring to search engines that your domain has expansive insights on topics related to your business, and this expertise is organized for the user easily, from broad to nuanced. This declaration can help domain authority, elevate search standings, and increase organic traffic.
When you create content for your website, generally, it gets indexed by Google and ranked according to a number of factors in a number of different keywords and phrases. Showing to Google the interlinking of related keywords and phrases can help boost signals that you are a topical authority on a subject.
Creating A Better User Experience
Topic clustering is not just a tool for SEO — user needs are of utmost importance. With an organized hierarchy of information on chosen subjects that are easily findable on each page, an enhanced user experience is seen by Google. Let’s not forget that Google uses page click-thru rate as a ranking signal, though we’re not sure how much weight is given to this factor.
How To Implement Topic Clustering
Charting Your Pillar Content: Envision a broad-themed canvas with a variety of topics on a core subject. Let’s use the idea of “email marketing” as an example.
What Is Your Pillar Topic: A comprehensive guide to email marketing that covers all aspects of the subject. This would be in the format of a blog/webpage, and you could also turn it into an e-book.
What Are Your Sub-Pillar Topics and Adjoining Blog Ideas:
Email Marketing For Beginners
- The Benefits of Email Marketing
- Basic Email Marketing Terminology
- How Email Marketing Works
Advanced Email Marketing Strategy
- Segmentation and Personalization Techniques
- Email Marketing Automation
- Leveraging AI For Your Email Marketing
List Building Techniques
- Growing and Maintaining Your List
- Lead Magnet Creation
- Signup Form Optimization and Engagement Strategy
Email Copywriting & UX
- Writing Great Subject Lines
- How and Where to Implement Calls To Action
- Designing Visually Appealing Emails
Tools and Software Reviews
- Mailchimp vs. Constant Contact
- Pricing Comparisons
- Hubspot vs. Salesforce
Analytics and Success Measurements
- Open Rates and Click Thru Rates
- How To Track and Increase Your Conversion Rates
- Measuring ROI
Compliance, Legal, and Best Practices
- GDPR and What You Need To Know
- What Is The CAN-SPAM Act
- Best Practices For Ethical Email Marketing
Success Stories and Case Studies
- Real life marketing examples of successful (and failed) email marketing campaigns
While this seems like a LOT of content — and it IS a lot of content — keep in mind that a lot of the themes and sub-themes of your pillar content and sub-pillar content will be fed into your blogs.
While massive content duplication is not encouraged on Google, the algorithm understands the need for different pages to have pieces of the same or similar content to provide an optimal experience or the user, who can not find the information they are looking for on multiple web pages.
It’s interesting to note that Google’s John Mueller noted some common misconceptions about duplicate content and preached “adding value.” It should be noted that pages that are near 100% duplicate content should have a canonical tag. But for the purposes of creating valuable content that leads the user in a proper direction, you should not see any issues or penalizations from this technique.
Google’s John Mueller clarifies a misconception about duplicate content, saying it’s not a negative search ranking factor. via @MattGSouthern: https://t.co/Gyuk3yPGwH #Google #HeyGoogle @Google
— SearchEngineJournal® (@sejournal) January 31, 2021
How To Organize: Using Data Driven Content Mapping Techniques For Your Website’s Topic Clusters
While qualitative methods and brainstorming is at the heart of great content creation, embracing data-driven strategies for mapping content into topic clusters is important. Using data to create this architecture will increase performance and provide a roadmap on where to turn if your content isn’t performing the way you want.
You’ve probably heard it before….pinpoint the fundamental themes that resonate with your audience and align with your business goals. You can leverage data search patterns, audience predilections, and uncharted content territories. This is basic SEO keyword research.
Forge your pillar content to offer an exhaustive insight into the primary subject and then craft cluster content that tackles distinct queries linked to the pillar theme. Each cluster piece should link back to the pillar content, creating a web of information that search crawlers can easily understand.
This method of content mapping requires ongoing scrutiny of user interaction metrics and search engine outcomes to fine-tune your topic clusters. This guarantees that your content stays pertinent, authoritative, and user-centric. You and your team are busy — but set a time quarterly or bi-annually to review.
Want to take your keyword discovery further? Try topic modeling.
Topic Modeling vs Keyword Clustering: What’s The Difference?
In the realm of content strategy and SEO, topic modeling and keyword clustering are distinct methodologies. Topic modeling is an algorithmic approach that attempts to discover abstract themes within a document collection. In doing this, you can uncover hidden thematic frameworks, which can be valuable for categorizing new content, refining how people talk about your brand, products and services, and spotting emerging trends.
Conversely, keyword clustering is a tactical maneuver, predominantly employed in SEO and pay-per-click (PPC). It entails the aggregation of similar keywords, enabling marketers to tailor content that resonates with specific audience segments.
Traditional topic modeling platforms include BERTopic, but with the rise of AI large language models, ChatGPT and other programs can hold the key to doing this properly.
Data scientists are REALLY good at extracting topics from large sets, but if you are a digital marketer or even a small business owner, you can simplify this process for smaller data sets using ChatGPT and a few useful prompts.
There are a number of “documents” you may want to scan for topical relevance insights — the keywords your site or a competitor site ranks for would be an example. A major focus should be your own qualitative data. Typically this is gathered in the way of web forms and customer reviews.
If you have a website that requires users to fill out a contact form that requests an open ended question such as “what is your issue?” or “comments”, you have likely acquired a treasure trove of customer-centric information. It would be great to crunch all of this language data together to see the primary topics associated with your users need in their own language. An advantage is that you can process this data more efficiently and are not taking extra time on a customer experience initiative like a survey or spending tedious time on customer interviews.
BAM created its own GPT for this called “Topic Modeling Assistant”, which easily digests between 10-25 comments or reviews at a time. Though we’d love to do hundreds or even thousands at a time, this is a good enough data set to get a better understanding of the topics. While the semantic analysis is not perfect, you can scrape through the list and find plenty of positive correlation.
Advanced Keyword Clustering: Semantic Clustering With Python
Advancing into the realm of keyword clustering, particularly through semantic analysis using Python, marks a significant leap in keyword examination. You don’t need to be a developer to learn Python, and it can be extremely useful for exploration. This is typically done for large-volume opportunity keywords with tens of thousands of possibilities for modifiers.
Adopting semantic clustering with Python not only bolsters your SEO tactics, but also sheds light on the behaviors of your audience. It paves the way for crafting content that is not only highly targeted but also resonates deeply with users, thereby enhancing both the user experience and search results. I won’t be covering that here, but a great place to start is this article in Search Engine Journal.
Using Topic Modeling For Keyword Clustering
If you’re wondering if you can use topic modeling for your topic clustering, the answer is YES. There’s actually no better place to start than crawling your customer’s own words in reviews and form comments for key topics to use as pillar content. Plug all of your ideas into a search engine platform such as Ahrefs and compare keyword demand. There may be some “hidden” topics within this text — content gaps that you can fill before your competitors can find them.
