How Google Search Uses AI: Exploring RankBrain, BERT, and MUM and Their Impact on SEO

Over the past decade, Google’s search engine has evolved from basic keyword matching algorithms to advanced artificial intelligence (AI)-driven systems that strive to understand the intent behind each search query. The goal is to deliver more relevant, accurate, and context-aware results. Systems like RankBrain, BERT, and MUM play a critical role in shaping the search results users see, and understanding these technologies is crucial for businesses looking to optimize their SEO strategies.

In this article, we’ll dive into how these AI systems influence search results, their technical aspects, and how they can inform and shape your SEO strategy.

1. RankBrain: The Introduction of Machine Learning in Search

RankBrain, introduced in 2015, was one of the first machine learning systems used by Google to understand search queries. Its main goal is to interpret queries in a way that goes beyond simple keyword matching by identifying patterns in the data and adjusting rankings accordingly.

How RankBrain Works:

RankBrain analyzes historical search data to better understand complex queries and connects them to similar past searches. By doing this, it refines its understanding of ambiguous or long-tail queries (those that don’t have an exact match in the database).

For example, if someone searches for “best long-distance car for road trips,” RankBrain tries to understand the intent behind “best,” “long-distance,” and “road trips” and connects it to results that focus on fuel-efficient cars, comfort, and durability.

SEO Implications of RankBrain:

• Content Focus: RankBrain values content that provides solutions or answers to users’ problems rather than just matching keywords. Your content should aim to satisfy user intent through comprehensive, useful information.
• User Experience (UX): Engagement metrics such as click-through rates (CTR) and dwell time (how long a user stays on a page) are believed to influence how RankBrain evaluates content. Optimize for a positive user experience by ensuring fast loading times, intuitive navigation, and valuable content.

2. BERT: Understanding the Nuance of Language

BERT (Bidirectional Encoder Representations from Transformers), introduced in 2019, represents one of the biggest breakthroughs in natural language processing (NLP). BERT allows Google to better understand the context of words in a sentence by considering the words before and after each term in the query.

How BERT Works:

Unlike earlier models, which might have considered each word independently, BERT processes words in relation to all other words in the sentence. This is crucial for understanding the nuances of queries, especially ones that include prepositions like “to” and “for,” which can completely change the meaning of a phrase.

For example, consider the query: “2019 Brazil traveler to USA need a visa.” Pre-BERT, Google may have returned results for “Brazil travelers” in general, not accounting for the direction of travel. With BERT, the system understands that the searcher is specifically looking for visa information for a Brazilian citizen traveling to the USA.

SEO Implications of BERT:

• Natural Language Queries: Since BERT is designed to handle complex, conversational queries, it’s important to write content in a natural, conversational tone rather than keyword-stuffed articles. Focus on addressing user queries as they might naturally ask them.
• Long-Tail Keywords: As BERT enhances Google’s understanding of long-tail queries, targeting specific, niche phrases that address particular user needs can become more effective.
• On-Page SEO: Optimizing for BERT is less about technical tactics and more about high-quality, human-centric content. Content that aligns with user intent and answers specific questions will perform better.

3. MUM: A Multimodal, Multi-tasking, and Multilingual AI

MUM (Multitask Unified Model), announced in 2021, takes Google’s AI capabilities to the next level. It is designed to handle complex, multi-part queries and can understand information across different formats (like text, images, and video) and languages.

How MUM Works:

MUM is multimodal, meaning it can analyze information in various formats, not just text. For example, if you ask, “What equipment do I need to hike Mount Fuji in October?” MUM can pull information from articles, videos, images, and even forum discussions across multiple languages to provide a comprehensive answer.

Moreover, MUM can understand nuances across different languages and apply the insights learned from content in one language to queries in another. It enables Google to provide more detailed answers to complicated queries, sometimes pulling information that the user didn’t explicitly ask for but that Google knows will be helpful.

SEO Implications of MUM:

• Multimedia Content: MUM’s ability to interpret images, video, and other formats means that SEO strategies should not be limited to just text-based content. Incorporating high-quality visuals, video tutorials, and infographics can make your site more visible.
• Structured Data & Schema Markup: Using structured data and schema helps search engines better understand your content. This becomes more important as MUM evolves, because the system might rely more on understanding the entities, topics, and relationships within your content.
• Global Reach: If your site operates in multiple countries or languages, MUM opens up new opportunities. Translating content or using multilingual SEO strategies can increase your site’s visibility in regions that speak different languages.

Putting it All Together: SEO Strategy in the Age of AI

Understanding how Google’s AI-driven systems work is critical to shaping a modern SEO strategy. Here’s how you can tailor your approach to RankBrain, BERT, and MUM:

1. Create Intent-Driven Content: Whether it’s RankBrain identifying user intent or BERT understanding language nuances, both systems reward content that satisfies search intent. Focus on answering specific user questions thoroughly.
   • Example: A blog post optimized for “how to maintain a boat engine” should cover common problems, step-by-step instructions, and even a video demonstration to ensure that it satisfies various user intents.
2. Improve Content Relevance: AI systems prioritize relevance, so it’s important to create in-depth, high-quality content on relevant topics.
   • Example: Instead of writing separate blog posts about boat safety and boating laws, consider a comprehensive guide that covers both, offering a more authoritative piece of content.
3. Diversify Content Formats: With MUM’s ability to analyze and rank information in different formats, it’s essential to think beyond text. Incorporate images, videos, podcasts, and infographics into your SEO strategy.
   • Example: A marine dealership could include a detailed YouTube video showcasing boat maintenance tips, which MUM can analyze alongside textual guides on the website.
4. Leverage Structured Data: To help Google’s AI better understand your content, use structured data wherever possible. This will enhance your chances of showing up in rich results and featured snippets.
   • Example: Using schema markup for product listings on an eCommerce site can help Google understand the price, availability, and reviews of your products, which is especially beneficial for long-tail search queries.
5. Focus on User Experience: AI systems like RankBrain consider how users interact with your content. A positive user experience—fast loading times, responsive design, and easy navigation—can improve rankings.

Conclusion: Embrace AI to Stay Ahead in SEO

As Google’s AI systems become more sophisticated, they push SEO to move beyond technical tricks and keyword-stuffing strategies. Success in modern SEO means crafting high-quality, intent-driven content that satisfies user needs across various formats and languages. By understanding RankBrain, BERT, and MUM, you can better align your SEO efforts with Google’s priorities, ensuring that your content not only ranks well but also delivers genuine value to users.

Incorporating a diverse range of content, focusing on user intent, and optimizing for multimedia experiences will be key to thriving in this AI-driven landscape.