What Data Science Can Do for Site Architectures

Over the past decade, SEO has become more data-driven, with a greater number of professionals adopting Python to understand and analyze internal link structures within site architectures. As Google’s updates have increased in frequency, it’s clear that SEO pros need to take a statistical approach to SEO, and internal link structures for site architectures are no exception.

In a previous article, the focus was on how internal linking could be enhanced based on data, providing Python code to evaluate site architecture statistically. It’s evident that data science can empower SEO professionals to uncover patterns and insights to effectively communicate the priority of content within a website to search engines.

Data science involves the intersection of coding, math, and domain knowledge – in this case, SEO. It’s important to recognize that while math and coding, especially in Python, are important, the instinctive feel of whether the numbers “look right” is crucial for SEO professionals.

How Can Site Architecture Support Underlinked Content?

Many sites are structured like a Christmas tree, where the homepage is the most significant, and other pages descend in importance in subsequent levels. For SEO scientists, understanding the link distribution from different views is essential. This can be visualized using Python code in various ways, including site depth, content type, internal Page Rank, and conversion value or revenue.

Visualizing the data through boxplots effectively shows the number of links that are “normal” for a given website at different site levels, helping to understand the interquartile range and median of inbound internal links.

Understanding Internal Page Rank and Revenue

Using a more sensible approach, True Internal Page Rank (TIPR) takes into account external PageRank earned from backlinks. This helps model the normal value of a page’s importance within a website architecture, influencing site structure.

Additionally, Revenue Internal Page Rank (RIPR) helps understand the normal revenue per page authority, enabling SEO professionals to focus on pages for internal linking based on revenue potential.

It is crucial to note that both TIPR and RIPR make certain assumptions and require proper analytics revenue tracking to effectively recommend internal link placements. Additionally, understanding the value of an internal link from any given page is essential for effective internal linking recommendations.

In conclusion, the shift towards a more data-driven approach to SEO, empowered by data science and Python, is essential for SEO professionals to understand and optimize internal link structures within site architectures.

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