Conversational search is an emerging trend in search engine technology that allows users to search by speaking or typing questions in natural language, rather than using traditional keyword-based queries. Major search engines like Google are investing heavily in conversational AI like LaMDA (Language Model for Dialogue Applications) to understand and respond to natural language queries.
This evolution in search presents new opportunities and challenges for SEO. Optimising for conversational search requires a shift in approach, but also opens up new possibilities for connecting with users and ranking highly for questions people naturally ask.
In this step-by-step guide, we’ll explore conversational search, how LaMDA works, and actionable tips for optimising your content for conversational queries and searches.
Conversational queries are search queries expressed in natural human language, using complete sentences, questions and conversational phrases. Some examples:
– “What is the best restaurant in London for a romantic dinner?”
– “I need to renew my passport – how long does that take?”
– “Show me some hiking trails near Seattle”
This differs from traditional “keyword” queries which are often just 2-3 words like “London restaurants” or “renew passport”.
With conversational search, the user intent is clearer within the full query. Users are asking questions or talking directly to the search engine, rather than just entering related keywords.
LaMDA (Language Model for Dialogue Applications) is Google’s conversational AI system that can engage in natural conversations and understand open-ended questions.
LaMDA is powered by deep-learning neural networks trained on massive volumes of conversational data. It develops an understanding of language from these patterns, allowing it to discern the meaning and intent behind conversational queries.
Some of the main ways LaMDA understands natural language
Contextual understanding – Analyses the complete query and conversation to determine context and intent. Understands follow-up questions based on previous queries.
Entity Recognition – Identifies entities (people, places, things) within queries to understand the subject.
Sentiment Analysis – Detects emotion and sentiment to understand user needs. i.e. frustration, urgency.
Grammatical Analysis – Parses sentence structure and grammar to extract query focus and meaning.
Semantic Similarity – Understands words with similar meanings to identify intent. Groups synonyms.
Conversational Patterns – Recognises conversational styles, questions, and phrases from training data.
LaMDA combines these techniques to decipher queries and respond in a conversational, contextual way.
Traditional SEO has focused on keywords, backlinks and other signals to rank pages. However, conversational search requires a more user-focused approach optimised for natural language queries. Here are some key reasons it matters for SEO.
Increasing use of voice search – Up to 50% of searches will be voice queries by 2023. SEO needs to optimise for spoken questions.
Queries are more conversational – Even typed search is becoming more conversational. SEO should match user language.
Understands user intent – Conversational search means queries have clearer intent. SEO needs to optimise pages for full user questions.
Context is important – Conversational systems rely on context between queries. SEO must consider search journeys rather than single keywords.
Focuses on user needs – Conversational AI aims to directly help users. SEO needs to take this into account.
In summary, SEO is moving beyond keywords to focus on how real humans search – through questions, conversations and natural language. Optimising for this presents new SEO opportunities.
Optimising content for conversational search requires some new strategies compared to traditional keyword-focused SEO. Here is a step-by-step process.
1. Identify Common User Questions
Analyse search data and trends to discover questions people are asking about your topics and products. Some techniques:
– Look for rising question keywords – “how”, “what”, “why” etc.
– Use Google Autocomplete and Related Questions.
– Check Google Trends for conversational search queries.
– Ask customers directly what questions they have.
Group questions into themes and user intents. Prioritise questions to optimise for.
2. Create Optimised Answer Pages
Create dedicated pages that provide full detailed answers to each question. Make sure they:
– Directly match the question terminology.
– Are structured clearly around the question.
– Give a complete and authoritative answer.
– Use conversational language and tone.
– Include related questions people also ask.
Well-optimised, detailed answer pages are essential for ranking natural language queries.
3. Integrate Keywords Naturally
While focusing on conversational language, also incorporate relevant keywords related to the question where possible for SEO. But integrate them naturally – don’t force keywords.
4. Enhance for Voice Search
Many conversational queries will be voice searches. Optimise pages for voice results.
– Use clear, concise sentences and formatting.
– Make sure key questions and phrases are in headings and text.
– Include audio/video content where relevant.
5. Help Continue the Conversation
Include links and suggestions for related questions to continue the search conversation.
– Link to your other relevant answer pages.
– Suggest further questions the user may have.
– Use internal links to naturally guide searchers.
This helps search engines understand the overall conversation and improve rankings.
6. Promote Your Content
Employ traditional SEO promotion strategies.
– Link building from other reputable sites.
– Social sharing to increase engagement.
– Guest posting on authoritative sites.
– PR outreach.
This increases authority and visibility for both search engines and users.
7. Track Rankings
Closely monitor how your pages rank for conversational search queries and continue optimising.
Some key metrics to track progress.
– Ranking position improvements for target questions.
– Click-through rate from search engines.
– Search engine traffic from conversational queries.
– Voice search impressions and CTR.
Here are some examples of sites optimising and ranking highly for natural language conversational queries.
Expedia has detailed pages for queries like:
“What is the best time to book a flight?”
The content directly answers the question in detail for different trip types. This allows them to rank #1 for that conversational query ahead of larger travel sites.
Wikihow focuses on How To style conversational content like:
“How to pack for a trip to Europe”
Their step-by-step format naturally fits conversational search queries starting with “how to”.
The Q&A site Quora already has a conversational structure with experts answering people’s questions.
This allows them to rank highly for queries like:
“What’s the difference between type 1 and type 2 diabetes?”
Their quality detailed answers outperform generic sites.
Optimising for conversational search requires a shift in strategy for SEO, but brings big opportunities. Some key tips:
– Identify the questions your audience is asking and create useful answers.
– Format content around natural conversational queries.
– Write genuinely helpful answers in a conversational tone.
– Promote content based on voice search behaviour.
– Monitor your rankings for question queries and continue optimising.
– Build an ongoing search conversation with internal links and related questions.
With the right strategy tailored to conversational search, you can connect with customers in this emerging space and build SEO success.