Google’s search algorithms have evolved from matching keywords to understanding language. BERT and MUM represent major advances in how Google interprets queries and content, moving toward genuine comprehension of meaning rather than pattern matching. Understanding these systems helps create content that aligns with how modern search actually works.
Why Natural Language Understanding Matters
Traditional search relied heavily on keyword matching. A page containing the exact phrase a user searched for had advantage over pages that addressed the same topic with different words. This created incentives for keyword stuffing and exact-match optimization that often degraded content quality.
Natural language understanding (NLU) changes this dynamic. When search engines understand meaning, they can:
- Match queries with content that uses different terminology
- Understand nuance and context in queries
- Distinguish between different meanings of the same words
- Process complex, conversational queries effectively
For SEO, this shift means optimizing for topics and user intent rather than specific keyword strings.
BERT: Bidirectional Understanding
BERT (Bidirectional Encoder Representations from Transformers) launched in October 2019 and represented Google’s largest search improvement in years. Google stated it affected about 10% of search queries at launch.
How BERT Works
Before BERT, language models processed words sequentially, either left-to-right or right-to-left. BERT processes words in relation to all surrounding words simultaneously, understanding context from both directions.
Consider the query “can you get medicine for someone pharmacy.” Pre-BERT systems might struggle with the unusual structure. BERT understands the implied question: whether someone can pick up a prescription for another person.
What BERT Changed
BERT particularly improved handling of:
Prepositions and context words: Small words like “to,” “for,” and “from” significantly change meaning. “Flights from Nashville” and “flights to Nashville” have opposite intents. BERT understands these distinctions.
Conversational queries: Natural language questions that don’t follow keyword patterns became easier to interpret.
Long-tail queries: Complex, specific queries with multiple components benefit from BERT’s contextual understanding.
Ambiguous queries: When words have multiple meanings, surrounding context helps BERT identify the relevant interpretation.
BERT and Featured Snippets
BERT also improved Google’s ability to select relevant featured snippets by better understanding what part of a page actually answers a query. This made featured snippet optimization more about genuine relevance than formatting tricks.
MUM: Multimodal and Multilingual
MUM (Multitask Unified Model), announced in 2021, extends beyond BERT’s capabilities significantly. Google describes MUM as 1,000 times more powerful than BERT, though direct comparisons are complicated.
| Capability | BERT | MUM |
|---|---|---|
| Languages | English primary | 75 languages |
| Modalities | Text only | Text, images, video |
| Query complexity | Single-step | Multi-step reasoning |
| Knowledge transfer | Limited | Cross-domain, cross-language |
| Launch | October 2019 | May 2021 |
MUM’s Expanded Capabilities
Multilingual understanding: MUM understands 75 languages and can transfer knowledge between them. Information available in one language can inform results in another.
Multimodal processing: MUM processes text and images together, understanding relationships between visual and textual information.
Complex query handling: MUM can break down complex queries requiring multiple steps or comparisons into component parts and synthesize comprehensive answers.
Knowledge transfer: MUM can apply information from one domain to related queries in another, filling gaps where direct information is sparse.
MUM in Practice
Google has deployed MUM capabilities gradually:
COVID-19 vaccine information: MUM helped surface relevant vaccination information across languages and sources.
Visual search improvements: Google Lens integration benefits from MUM’s multimodal understanding.
Search refinement suggestions: MUM powers more sophisticated “things to know” and related topic suggestions.
AI Overviews: MUM contributes to Google’s ability to synthesize information into AI-generated summaries.
Future MUM Applications
Google has indicated MUM will expand to handle increasingly complex queries that previously required multiple searches:
- Trip planning across multiple destinations and considerations
- Research requiring synthesis from multiple authoritative sources
- Questions requiring comparison of multiple options across criteria
- Queries needing information transfer from related domains
SEO Implications
NLU advancement doesn’t mean traditional SEO becomes irrelevant. It means the focus shifts.
Topic Comprehensiveness
With NLU, Google understands topic coverage beyond keyword presence. Content should:
Cover topics thoroughly: Address various aspects and related questions users might have
Use natural vocabulary: Include synonyms, related terms, and natural language variations
Answer implicit questions: Address questions users might not explicitly state but likely have
A Nashville wedding planner writing about wedding venues shouldn’t just repeat “Nashville wedding venues” but should cover considerations like capacity, style, pricing tiers, seasons, and logistics that comprehensive coverage would include.
User Intent Alignment
NLU improves Google’s ability to match content with user intent. Misaligned content performs worse:
- Informational content struggling to rank for transactional queries
- Sales pages underperforming for research-oriented queries
- Surface-level content losing to comprehensive resources
Understand what users actually want when they search and deliver that rather than what you want them to want.
Natural Language Content
Write for humans rather than search algorithms:
Conversational tone: Natural language that a human would actually use
Varied phrasing: Don’t repeat the same phrase construction throughout
Context provision: Give readers background they need to understand your points
Clear structure: Help both humans and algorithms understand content organization
This doesn’t mean abandoning keyword awareness. It means keywords should flow naturally within well-written content rather than being awkwardly inserted.
Long-Tail Query Opportunity
BERT and MUM improve Google’s handling of long-tail queries, creating opportunity:
- Specific questions now find relevant answers more easily
- Conversational queries match with appropriate content
- Complex queries with multiple conditions can be served
Content addressing specific, detailed questions has better chance of ranking now than when Google struggled to understand complex queries.
What You Can’t Optimize
BERT and MUM aren’t things you optimize for directly. They’re systems that improve how Google understands content you’ve already created.
No BERT Optimization Tricks
You cannot:
- Add BERT markup to your pages
- Use specific patterns that “trigger” BERT
- Optimize for BERT differently than for general quality
BERT is a language understanding system, not a ranking factor you influence directly. The best “BERT optimization” is writing clear, comprehensive content that answers user questions well.
Focus on Quality, Not Technology
Some SEO advice suggests specific tactics for “BERT optimization” or “MUM optimization.” This misunderstands how these systems work. They improve Google’s understanding of existing content and queries; they don’t introduce new ranking signals you can target.
The consistent advice: create genuinely helpful content that thoroughly addresses user needs. NLU systems reward this approach by better understanding and surfacing such content.
Content Strategy Adjustments
While you can’t optimize for NLU directly, strategic adjustments align with how these systems work.
Address Questions Comprehensively
BERT and MUM enable Google to understand whether content truly answers queries or just contains relevant keywords. Ensure content:
- Directly answers the primary question
- Addresses related questions users commonly have
- Provides sufficient depth for the topic
- Doesn’t leave users needing to search again
Use Structured Content
Clear content structure helps NLU systems understand your content:
- Logical heading hierarchy matching content organization
- Clear topic sentences identifying section focus
- Explicit statements of key points rather than buried conclusions
Structure helps both human readers and algorithms understand your content.
Develop Topical Depth
Rather than isolated pages targeting individual keywords, develop comprehensive coverage of topic areas:
- Multiple related pages addressing different aspects
- Internal linking connecting related content
- Consistent expertise demonstration across topics
This topical authority approach aligns with how NLU systems understand expertise and comprehensiveness.
Consider Conversational Queries
As voice search and conversational interfaces grow, queries become more natural language. Content that addresses questions the way users actually ask them performs better:
- “How do I…” rather than just instructional content
- “What’s the best…” for comparison and recommendation content
- “Why does…” for explanatory content
Match the phrasing users actually use without keyword stuffing.
Measuring NLU Impact
You can’t directly measure whether BERT or MUM affected your rankings. However, certain patterns suggest NLU systems are working in your favor:
Long-Tail Performance
If comprehensive content ranks for long-tail queries you didn’t explicitly target, NLU systems likely understand your content’s relevance to related queries.
Query Diversity
Content ranking for varied phrasings of similar questions suggests NLU systems understand your content’s topic coverage beyond exact keywords.
Featured Snippet Capture
Winning featured snippets, especially for question queries, indicates Google understands your content provides relevant answers.
Reduced Keyword Dependency
If rankings remain stable despite not using exact-match keywords in titles or headers, NLU systems likely understand your content’s relevance through context.
The Bigger Picture
BERT and MUM are part of Google’s ongoing evolution toward understanding information rather than matching strings. This trajectory will continue:
More sophisticated understanding: Future systems will understand nuance, expertise, and quality even better
Multimodal integration: Text, image, video, and audio understanding will increasingly merge
Cross-language capabilities: Information in any language will inform results in all languages
Complex task completion: Search will handle multi-step queries requiring synthesis and reasoning
The implication for SEO: focus on genuine quality and comprehensive coverage. Systems that understand language reward content that genuinely serves users.
Resources
- Google BERT Announcement: https://blog.google/products/search/search-language-understanding-bert/
- Google MUM Overview: https://blog.google/products/search/introducing-mum/
- Understanding How Search Works: https://www.google.com/search/howsearchworks/
- Search Quality Evaluator Guidelines: https://guidelines.raterhub.com/
NLU systems continue evolving rapidly. Specific capabilities and applications expand with each iteration. Focus on timeless principles of quality and relevance rather than tactics targeting specific algorithm versions.