SMITH Algorithm in SEO: The Definitive Guide by Thatware

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What is SMITH algorithm?

SMITH stands for Siamese Multi-depth Transformer-based Hierarchical Encoder for long-form document matching. SMITH algorithm is trained to understand passages within the context of the entire document. Here we use the SMITH algorithm to check passages satisfy the criteria of the algorithm in the content or not.

Today in the definitive guide we will learn more about this new algorithm and the use-cases on how it will affect the future of SEO.

The SMITH algorithm plays a crucial role in improving passage indexing because it allows Google to evaluate and rank smaller, more specific parts of a webpage. By understanding and applying the SMITH algorithm, SEOs can target passages, increasing the likelihood of getting featured in Google’s search results and featured snippets.

Deep Dive into SMITH Algorithm’s Architecture

The SMITH algorithm is based on advanced natural language processing (NLP) and uses transformer-based models. It operates by encoding a document at multiple depths, learning the relationship between individual passages and the overall document. Here’s how the architecture works in more detail:

Siamese Network Architecture: SMITH uses a Siamese network approach, which allows the model to compare two or more passages against each other. This comparison helps determine if a passage is relevant to a particular query or topic. In SEO, this means that if one passage is more relevant to the user’s query than another, it can be ranked higher.
Multi-depth Encoding: Unlike traditional models that evaluate documents at a single depth, SMITH uses multi-depth encoding, which looks at the structure of a document and considers the relationship between sentences, paragraphs, and the entire document. This helps the algorithm understand context better, improving how it matches search queries to specific passages.
Hierarchical Structure: The hierarchical encoding allows SMITH to understand documents at varying levels of granularity. Whether it’s a sentence, paragraph, or an entire document, SMITH can break down the structure and identify the most relevant passage based on the query.

How the SMITH Algorithm Impacts SEO Strategy

The implementation of the SMITH algorithm is a game-changer for content optimization. Here’s how it impacts SEO:

Increased Relevance of Specific Passages: Since the SMITH algorithm indexes individual passages, SEOs now have the opportunity to optimize sections of long-form content for specific long-tail keywords. This allows content to rank for a broader range of relevant queries rather than just one overarching keyword.
Focused Optimization: Traditionally, SEO strategies focused on optimizing entire pages around a single primary keyword. With passage indexing, SEOs can now optimize content for multiple queries within a single page, helping to target specific user intent more effectively. This requires detailed analysis of each passage’s content, keyword usage, and contextual relevance.
Use of Long-Tail Keywords: One of the most important factors for passage indexing in the SMITH algorithm is the use of long-tail keywords. Long-tail keywords often capture highly specific user intents. For example, a general article on SEO may contain different sections optimized for terms like “how AI impacts SEO,” “SEO best practices for 2024,” and “SEO tools for beginners.” By targeting long-tail keywords, SEOs can enhance the ranking potential of multiple passages across the page.
Impact on Featured Snippets: The rise of featured snippets on SERPs has made it more important than ever to focus on specific passage-level content. The SMITH algorithm directly influences this by ensuring that the most relevant passage for a given query is indexed and surfaced in the search results, increasing the chances of content being featured.

SMITH Algorithm and Passage Density

Passage density refers to the frequency of important keywords, including long-tail keywords, within a specific passage. For SEO, maintaining the right passage density is critical.

Too High Passage Density: If the passage density is too high (i.e., if the same keyword is repeated excessively within a short passage), it can be perceived as keyword stuffing, a black-hat SEO tactic that Google penalizes.
Too Low Passage Density: On the flip side, if the passage density is too low, it may not be sufficient to signal relevance to the algorithm. SMITH recommends maintaining a balanced passage density, which helps in better matching a passage with a search query.
Optimal Passage Density: The optimal passage density should be calculated based on the length of the passage and the number of important keywords used. SEOs should aim to integrate relevant keywords seamlessly into the content to meet both user expectations and algorithm requirements. The ideal density helps ensure that the passage is relevant and readable.

The Role of Python in SMITH Algorithm Optimization

The blog you shared mentions the use of Python code to analyze passages in a document, calculating passage density, long-tail keyword presence, and similarity between passages. Let’s explore the importance of using Python for SMITH algorithm optimization:

Automating Passage Analysis: Python allows SEO experts to automate the process of analyzing long-form content. By running custom scripts to measure passage density and evaluate keyword usage, SEOs can save significant time and focus on optimizing content rather than manually assessing each passage.
Customized Metrics: Python gives SEOs the flexibility to create customized metrics and thresholds for passage evaluation. For example, SEOs can adjust word count limits, define specific ranges for long-tail keyword density, and set similarity score parameters. This customization allows for more precise control over how content is optimized for SMITH.
Reproducibility: Python scripts ensure that the process of evaluating and optimizing passages can be replicated consistently across multiple pages or websites. This is essential for scaling SEO efforts, especially for sites with large amounts of content.

Real-World Use Cases of SMITH in SEO

SMITH is particularly effective in long-form content optimization, and its use cases extend beyond traditional SEO practices. Here are some examples:

E-Commerce Websites: For e-commerce sites with product descriptions, SMITH can help optimize individual product sections within a page. Rather than relying on general keywords for the whole category, each product description can be tailored to rank for specific, highly targeted long-tail keywords.
Educational Websites: Educational content, such as online courses, guides, or articles, often contains a lot of information. SMITH can be used to optimize individual topics within a course or guide, making each section rank for different search queries. For example, a course on “Digital Marketing” might have sections on “SEO basics,” “PPC advertising,” and “social media marketing.” Each of these passages can be optimized to rank for more specific keywords.
Content Aggregators: Websites that aggregate content, like news sites or blogs, can benefit greatly from SMITH. By optimizing individual paragraphs or sections, these websites can rank for a variety of queries, from trending news topics to niche interests.

SMITH Algorithm and Future of SEO

The SMITH algorithm, along with other advancements in NLP and machine learning, is changing how SEO is done. Here’s how it could shape the future of SEO:

Better User Intent Matching: SMITH’s understanding of context at multiple depths will allow search engines to match user queries with the most relevant content. This improves the user experience by showing them content that is more aligned with their needs.
Increased Focus on Content Quality: As algorithms like SMITH get more sophisticated, the focus will shift even more towards content quality and relevance. The ability to optimize individual passages will lead to more targeted, contextually rich content that satisfies both search engines and users.
AI-Powered Content Creation: With tools like SMITH, AI-powered content generation will become more precise. SEO tools may use SMITH-like algorithms to generate optimized content at a passage level, improving content quality for both humans and search engines.

Challenges with Implementing SMITH Algorithm

While the SMITH algorithm offers numerous advantages, there are some challenges in its implementation:

Technical Expertise: Implementing and optimizing for SMITH requires a deep understanding of machine learning and natural language processing (NLP), making it more suited to advanced SEO experts or those who are familiar with programming languages like Python.
Content Structure: Not all content is structured in a way that SMITH can easily evaluate. For example, content that lacks clear passages or is poorly formatted may be harder to optimize effectively.
Adaptability of Current Content: Websites with older content may need substantial restructuring to meet SMITH’s passage-based evaluation criteria. This can be time-consuming and may require a complete overhaul of existing pages.

The SMITH algorithm marks a significant leap forward in SEO by allowing search engines to index and rank specific passages of content rather than entire pages. As the algorithm evolves, it will help SEO professionals optimize content at a much more granular level, improving the chances of ranking for highly specific queries. By understanding how the algorithm works and applying its principles, SEOs can enhance their content’s visibility, drive more targeted traffic, and increase their chances of ranking in featured snippets and other SERP features.

The Role of SMITH in Voice Search Optimization

With the increasing adoption of voice search through virtual assistants like Siri, Google Assistant, and Alexa, the role of the SMITH algorithm in optimizing content for voice search is becoming more critical. Voice queries are often more conversational, and users tend to ask longer, more detailed questions. Here’s how SMITH enhances the SEO for voice search:

Natural Language Understanding: The SMITH algorithm’s ability to understand the nuances of natural language makes it well-suited for voice search optimization. Since voice queries are often longer and more context-driven, SMITH can identify the most relevant passage in content that directly answers the user’s query.

Contextual Relevance: Voice search queries tend to focus on specific, context-based needs, like “What’s the best Italian restaurant near me?” or “How do I fix a leaky faucet?” SMITH’s passage-level indexing can help content rank for these conversational queries by matching the most contextually relevant passages from a webpage.

Featured Snippets and Voice Search: Many voice search results pull directly from featured snippets or position zero in SERPs. Since SMITH enhances passage relevance, it increases the chances of content being pulled for these featured snippets, providing better visibility for voice-based search queries.

Improving Content Depth and Structure with SMITH

One of the key ways that SMITH impacts SEO is through its ability to assess content depth and structure. Before SMITH, content optimization was mostly about ensuring keyword density, relevance, and overall readability. However, now SEO experts must focus on the structure of the content itself to get the most out of the SMITH algorithm. Here’s how you can improve content depth and structure:

Organizing Content into Clear Sections: Since SMITH evaluates passages individually, structuring content into distinct sections with clear headers can make a huge difference. For example, a blog post on “SEO Best Practices” should have headers like “Keyword Research,” “On-Page Optimization,” and “Link Building.” This structure helps SMITH better understand the relevance of each section to specific queries.

Comprehensive Content: The more comprehensive your content is, the better SMITH will be able to evaluate individual passages. Comprehensive articles with deep, detailed information naturally provide more opportunities for content to be indexed and ranked for multiple, highly specific queries. This can lead to higher chances of ranking in position zero for multiple long-tail keywords.

Bullet Points and Lists: Bullet points and numbered lists help break down complex information into digestible parts, which is essential for passage indexing. By using these elements, SEOs can increase the likelihood that Google will index specific points and use them in relevant search queries, improving content ranking.

Linking and SMITH: Importance of Internal and External Links

In addition to optimizing individual passages, it’s important to consider the role of internal and external links in enhancing SMITH’s effectiveness. Here’s how links play into the broader SEO strategy for SMITH:

Internal Links: Internal linking remains a critical factor for SEO, as it helps search engines crawl your website more effectively. By linking relevant passages to other sections of your site, you enhance the semantic relationships between various pieces of content, which SMITH uses to determine relevance. For instance, if you have a long-form article on “SEO Strategies”, linking to related articles on “Advanced Keyword Research” or “Content Marketing Tips” can improve the interconnectivity of your site, helping SMITH understand the relationship between these pieces of content.

External Links: While SMITH focuses on internal content, external backlinks still play a major role in SEO. Quality external links that reference specific passages in your content can boost its credibility and authority. When reputable sources link to a specific passage of your content, it signals to Google that the passage is trustworthy and relevant, further improving its chances of ranking for related queries.

Working mechanism

We first take a long content with lots of passages. Probably a landing page of a website
Then we check passage density in the content.
Then we check the word count of each passage.
Check any long tail keywords if it’s available in the passage or not
The check similarity distance between two passages.
Check, If the similarity distance is high, have a long tail keyword available & satisfy the density criteria then the passage fulfills the SMITH algorithm in search engine optimization.

Ideal score’s which you need to keep in mind

The minimum word count of the passage should be between 30-60
Long-tail keyword present should be greater than 1

Details which we have taken for the optimization / Suggestion

1. We have taken the below content for the SMITH algorithm

We have taken all the contents from this landing page from our website https://thatware.co/about-us/

2. Execution of Codes in Python

We have then used our own customized code (coded in Python) to measure the passage density, long-tail keywords, and for checking the passage similarity.

The Output’s Obtained

Word count & Passage density given below along with the passage:

P.S: The code triggered 7 passages and whilst counting the word’s the stop words have been ignored!

[There are many technical seo companies around the world but THATWARE is recognized as a leader in it.]

Word count: 9
Passage Density 4.411764705882353

[Traditional marketing never provided a better opportunity in understanding the customer’s need. Moreover, with old school marketing, proper analysis and handling of data were a lot difficult. As a result, the ROI and efficiency were much lower as compared to the AI-based marketing model. 4 key benefits are highlighted here as to why seo services should use the ai model over the traditional approach.]

Word count: 39
Passage Density 19.11764705882353

[AI gives a positive influence on SEO. With AI, corporations can boost the precision, efficiency, and performance of search engine optimization techniques, comprising the content generated for SEO. While some of the SEOs may fear that AI will replace their role, AI fulfills a supporting role as an equipment.AI in SEO assists to enhance your recent SEO strategy by finding out chances, like related keywords. Its algorithms, as well as rate, help businesses expedite the method and improve the precision of keyword research, competitor analysis, search intent analysis, and much more.]

Word count: 59
Passage Density 28.921568627450984

[There are many technical seo companies around the world but THATWARE is recognized as a leader in it. From advanced off-page services to professional seo services we are the market leader. ]

Word count: 18
Passage Density 8.823529411764707

[Our main value-added proposition is ai seo optimization & business seo with cutting edge technologies such as data science, machine learning, semantic engineering, advanced search, and much more.]

Word count: 23
Passage Density 11.27450980392157

6. [While some of the SEOs may fear that AI will replace their role, AI fulfills a supporting role as an equipment.AI in SEO assists to enhance your recent SEO strategy by finding out chances, like related keywords. Its algorithms, as well as rate, help businesses expedite the method and improve the precision of keyword research, competitor analysis, search intent analysis, and much more. ]

Word count: 39
Passage Density 19.11764705882353

[Its algorithms, as well as rate, help businesses expedite the method and improve the precision of keyword research, competitor analysis, search intent analysis, and much more. ]

Word count: 17
Passage Density 8.333333333333332

The number of long-tail keywords available in the passages is as under:

For Passage 1:

Number of long-tail keyword present: 6

For Passage 2:

Number of long-tail keyword present: 14

For Passage 3:

Number of long-tail keyword present: 6

For Passage 4:

Number of long-tail keyword present: 7

For Passage 5:

Number of long-tail keyword present: 5

For Passage 6:

Number of long-tail keyword present: 3

For Passage 7:

Number of long-tail keyword present: 2

The Similarity score’s for the passages have been given below:

P.S: Every passage have been compared with their subsequent passages for a similarity comparison

For Passage 1:

With passage 2, Score is 0.243
With passage 3, Score is 0.214
With passage 4, Score is 0.8
With passage 5, Score is 0.172
With passage 6, Score is 0.218
With passage 7, Score is 0.162

For Passage 2:

With passage 3, Score is 0.356
With passage 4, Score is 0.283
With passage 5, Score is 0.209
With passage 6, Score is 0.341
With passage 7, Score is 0.315

For Passage 3:

With passage 4, Score is 0.21
With passage 5, Score is 0.152
With passage 6, Score is 0.915
With passage 7, Score is 0.622

For Passage 4:

With passage 5, Score is 0.197
With passage 6, Score is 0.206
With passage 7, Score is 0.159

For Passage 5:

With passage 6, Score is 0.15
With passage 7, Score is 0.219

For Passage 6:

With passage 7, Score is 0.686

Conclusion and Final Result

After checking each passage with our code we can say that only one paragraph follows closely on the SMITH algorithm. Here it is:

[While some of the SEOs may fear that AI will replace their role, AI fulfills in a supporting role as an equipment.AI in SEO assists to enhance your recent SEO strategy by finding out chances, like related keywords. Its algorithms, as well as rate, help businesses expedite the method and improve the precision of keyword research, competitor analysis, search intent analysis, and much more.]

Passage similarity Score 0.915
Number of long-tail keyword present: 3
Word count: 39 (without stop words)
Passage Density 19.11764705882353

Only above passage fulfills all condition of SMITH algorithm here.

Benefit’s in SEO:

Now since we know, how to evaluate the SMITH Algorithm of a landing page. This gives us a clue as to which passage Google might consider for an enhanced Passage Indexing.
With the above point being said, SEOs can now optimize the “passage” obtained in the output by enriching it more as per the context which will give the SEO experts an upper edge to optimize for feature snippets and passage indexing.
Please note, the use-case is ultra-useful for websites that have long contents and numerous passages. This makes effortless gains for SEO pros to optimize a given passage to yield higher potential for SERP features.

Tuhin Banik

Thatware | Founder & CEO

Tuhin is recognized across the globe for his vision to revolutionize digital transformation industry with the help of cutting-edge technology. He won bronze for India at the Stevie Awards USA as well as winning the India Business Awards, India Technology Award, Top 100 influential tech leaders from Analytics Insights, Clutch Global Front runner in digital marketing, founder of the fastest growing company in Asia by The CEO Magazine and is a TEDx speaker and BrightonSEO speaker.