Automated Reverse Silo SEO

In the world of search engine optimization (SEO), staying ahead of the curve and adapting new techniques as the content depends change is crucial for success. One innovative approach that has gained traction in recent years is reverse silo SEO. This technique, when combined with the power of automation, can improve use experience while helping search engines crawl your pages more efficiently.

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In this post we'll be diving into what Reverse Silo is, how it works, challenges and benefits. Lastly, we'll provide a walkthrough of how we built our Automated Reverse Silo app using Moonlit.

What is Reverse Silo SEO?

Reverse silo SEO is a content structuring strategy that differs from traditional silo SEO. In a traditional silo structure, content is organized in a hierarchical manner, with the most important pages at the top and supporting pages branching out beneath them. In contrast, reverse silo SEO flips this structure on its head.

With reverse silo SEO, supporting articles are created first, each focusing on a specific sub-topic related to the main theme. These supporting articles then link back to a central target page, which serves as the hub for the entire content cluster. The target page, in turn, links to the homepage, creating a cohesive and interconnected structure.

The benefits of using a reverse silo approach are numerous. It allows for a more natural and organic content creation process, as writers can focus on creating in-depth, high-quality supporting articles without being constrained by a rigid hierarchy. Additionally, the interlinking structure helps to distribute link equity throughout the site, improving overall SEO performance.

However, implementing a reverse silo structure manually can be challenging, especially for larger websites with extensive content. This is where automation comes into play.

Automating Reverse Silo SEO

The need for automation in reverse silo SEO becomes apparent when dealing with a vast amount of content. Manually organizing and interlinking articles can be time-consuming and prone to errors. By leveraging artificial intelligence (AI) and machine learning (ML) techniques, the process can be streamlined and optimized.

The automation process involves several key components, including topic modeling, content organization, and internal linking. Let's explore each of these in more detail.

Key Components of Automated Reverse Silo SEO

Content Organization

Once topics have been identified, the next step is to organize the content based on relevance. This involves ensuring that supporting articles are properly linked to their respective target pages and that the overall structure follows the reverse silo approach.

However, challenges may arise when integrating automated solutions with content management systems (CMS). For example, there can be issues with the order of topics displayed in the CMS not matching the desired order, possibly due to the underlying array not being sorted correctly. Potential solutions involve changing the array index, looping through topics differently, or checking documentation for ordering arrays in the specific templating language being used.

Internal Linking

Internal linking is a critical component of reverse silo SEO. By strategically linking supporting articles to the target page and the target page to the homepage, link equity is distributed effectively, and the overall SEO performance is enhanced.

Automating the internal linking process can save significant time and effort. However, it's important to ensure that the linking structure is logical and user-friendly. Strategies for automated internal linking may involve using natural language processing (NLP) techniques to identify relevant anchor text and destination pages.

Implementation Considerations

When implementing automated reverse silo SEO, there are several considerations to keep in mind.

CMS Integration

Integrating automated solutions with existing content management systems can be challenging. Each CMS has its own architecture and limitations, and ensuring seamless integration is crucial for success. It's important to carefully evaluate the compatibility of the automated solution with the CMS and address any potential issues before implementation.

User Experience

While automated reverse silo SEO can greatly improve SEO performance, it's essential to prioritize user experience as well. The automated structure should be intuitive and easy to navigate for users. Ensuring that the interlinking structure is logical and that users can easily find the information they're looking for is crucial for a positive user experience.

Benefits of Automated Reverse Silo SEO

Implementing automated reverse silo SEO offers several significant benefits:

Improved SEO Performance

By optimizing the content structure and interlinking, automated reverse silo SEO can lead to improved search engine rankings and increased organic traffic. The strategic distribution of link equity and the creation of a cohesive content cluster can greatly enhance the website's visibility and authority in search results.

Scalability

Automated reverse silo SEO enables content strategists to scale their efforts effectively. As the website grows and new content is added, the automated system can seamlessly integrate it into the existing structure, ensuring that the reverse silo approach remains intact. This scalability is particularly valuable for large websites with a constantly expanding content library. While you have the option to run apps at scale with Moonlit's Bulk Run feature, we do recommend that you do this manually to double check each the suggested internal links for each page.

Resource Optimization

Automating the reverse silo SEO process can significantly save time and resources. Instead of manually organizing and interlinking content, the automated system handles these tasks efficiently. This allows content strategists and SEO professionals to focus on other critical aspects of their work, such as content creation and analysis.

Conclusion

As with any automated solution, it's important to carefully consider the implementation process and address potential challenges along the way. By understanding the key components of automated reverse silo SEO, such as topic modeling, content organization, and internal linking, content strategists can make informed decisions and optimize their implementation.

Embracing automated reverse silo SEO can be a game-changer for businesses looking to stay ahead in the competitive digital landscape. By combining the proven principles of reverse silo SEO with the efficiency and scalability of automation, content strategists can unlock new levels of success and drive meaningful results for their websites.

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How we built the Automated Reverse-Silo App

Inputs

• Target “money” Page (text input): For passing the full url to the page we want to rank.
• Sitemap URL: to fetch all pages and find relevant ones.
• Blog Segment Filter: For filtering the sitemap URLs by a specific url segment.‍

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Logic

• Step 1: First we'll using the Extract Sitemap URLs function to fetch all the urls from the sitemap, filtered by the given segment filter (which can be empty if we want all the urls in the site regardless of their path)
• Step 2: Once we have our URLs, we’ll add in a Custom Python Function for three purposes; A. we’ll check if there were any urls extracted from the sitemap and raise an error if not. B. if the ‘Target Page’ was not included in the pulled urls we’ll add it in. And C, we will compute the ‘K’ value for our k-means clustering function as the number of urls divided by 5. The reasoning behind this is that we want the top 5 most relevant articles, so if we have 100 urls and we want 5 most relevant, then we want on average, 20 different clusters. The ‘average’ here is important, we’re mostly likely going to get clusters of different sizes, and it’s not the most reliable technique. However, it’s better than arbitrarily using a ‘K’ value without any regard to the number of urls.
• Step 3: This is where we will perform semantic clustering. In this function we’ve set the ‘table source’ to read from the custom python function node using dot notation. Since our Python function returned an object with two keys: ‘data’ (all the urls, along with titles, and descriptions), and ‘k’ (dictating the number of clusters).
• Step 4: Now in the last step, we’ll use another ‘Custom Python Function’ to process and display our data in the desired format.

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Outputs

- Table Output: We finally map the processed results (from our Custom Python Function in step 4) to a table output for display to the end user.

- Text Output: just for extra details.

Final Thoughts

‍Thanks to Steve Toth For inspiring the idea behind this app. While there’s still a good chunk of manual parts that need to be executed, the most time consuming task of finding the right supporting articles has been done for us through this app. Now, to put this into action after using it, you’ll need to inject the links as denoted by the output table, and also make sure that you have backlinks to your supporting articles to boost the ranking of your “money page” even further.

A few weeks ago I made a ‘Smart Internal Linking’ tool that uses AI to naturally inject internal links into a given webpage, so feel free to check that out to help you with the process oflinking.

Lastly, as with most of my Moonlit Apps, this one is allowed to be cloned. So you can add to your project and edit it to better fit your use-case. You can edit things like the number of pages to use, the prompts, or extend its functionality and automation capabilities.