Automated Reverse Silo SEO

Reverse silo SEO is a strategy that flips the traditional concept of the SEO silo on its head. In traditional siloing, content is organised into distinct categories or topics to create a structured hierarchy on a website. This approach aims to group related content together, making it easier for search engines to understand the site's structure and content themes, which can improve search rankings for those topics.

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In a reverse silo SEO strategy, the focus shifts from structuring the website's internal content to prioritising external links and content that points back to the site's main pages or cornerstone content. Here's a breakdown of how it works:

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1. External Link Building: Instead of starting with the organisation of content on the website, a reverse silo strategy begins with building external links from reputable and relevant sites. The goal is to direct external links to your site's main pages (such as product pages, service pages, or key informational content).

2. Strategic Content Creation: Content is created or optimised not just for the sake of internal hierarchy but with an eye towards attracting external links and improving the authority of main pages directly. This involves creating highly valuable, link-worthy content that external sites want to link to.

3. Internal Linking: Once external links are directing traffic and authority to the main pages, internal linking is used to distribute that authority throughout the site. Links are created from the main pages to sub-pages and articles within the site, passing along the SEO benefits. However, the emphasis is on ensuring that the main pages gain authority first from external sources.

4. Focus on User Experience and Relevance: Throughout this process, it's crucial to maintain a focus on creating a positive user experience and ensuring content relevance. The structure should still make logical sense to visitors, guiding them naturally through the site's content and toward conversion points.

The reverse silo approach can be particularly effective for websites looking to boost the authority and ranking of specific key pages quickly by leveraging external link equity. It's a more outward-looking strategy that focuses on how off-site factors can influence and enhance the site's SEO performance before reinforcing it with traditional on-site silo structuring.

Excerpt

In this walkthrough, our goal is to build a Moonlit App that would allow us to save time picking out the relevant ‘supporting’ articles to use for implementing the Reverse Silo SEO technique. We’ll achieve this through semantic clustering and some custom Python functionalities for handling data processing.

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App Breakdown

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.‍

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 of linking.

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.

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