Traditional Search
Traditional search engines match keywords in a query to keywords on web pages and business listings. Results rank based on relevance signals like keyword density, backlinks, and domain authority. For local searches, traditional engines added location-based filtering. A search for “title company Baltimore” returns listings that contain those keywords and are geographically relevant.How Traditional Search Works
1
Query Processing
The search engine breaks the query into keywords and identifies modifiers like location terms.
2
Index Matching
Keywords are matched against an index of web pages, business listings, and other content.
3
Ranking
Results are sorted by relevance, authority, and location proximity. See How Local Search Works for ranking factors.
4
Display
The engine presents a list of links, often with a for geographic queries.
AI-Powered Search
AI-powered search tools interpret meaning rather than matching keywords. These systems understand context, synonyms, and relationships between concepts. power tools like Google’s AI Overviews, ChatGPT, Perplexity, and Microsoft Copilot. These tools synthesize information from multiple sources to provide direct answers rather than lists of links.Key Differences from Traditional Search
| Aspect | Traditional Search | AI-Powered Search |
|---|---|---|
| Query interpretation | Keyword matching | Intent understanding |
| Results format | List of links | Direct answers and summaries |
| Data sources | Indexed web pages | Multiple sources synthesized |
| User interaction | Click through to websites | Conversational follow-up |
How AI Tools Find Business Information
AI systems pull data from structured sources:- Google Business Profiles
- Business directories and aggregators
- Website content with schema markup
- Review platforms
- Social media profiles
Voice Search
Voice search uses spoken queries through devices like smartphones, smart speakers, and car systems. Voice assistants include Siri, Google Assistant, Alexa, and Cortana. Voice queries differ from typed searches in structure and length. Spoken searches tend to be conversational and question-based rather than keyword fragments.Voice Query Characteristics
Conversational Tone
“Where can I find a good mortgage lender near me?”
Question Format
“What title company is open right now?”
Longer Queries
Average 7+ words compared to 3-4 for typed searches
Local Intent
High percentage of voice searches have local intent
Voice Search Results
Voice assistants typically provide a single answer or a short list of options. There is no scrolling through multiple pages of results. The assistant reads information directly from business listings. Accuracy of name, address, phone number, and hours becomes critical since the user cannot visually verify details. See Maintaining Consistency for guidance on keeping information accurate.Voice assistants pull data primarily from Google Business Profile for Android devices and Siri, and from Bing Places for Cortana. Maintaining accurate profiles on both platforms maximizes voice search visibility.
Structured Data
refers to information formatted in a standardized way that machines can easily read and interpret. This includes schema markup on websites and consistent formatting across business listings. Search engines and AI tools use structured data to understand what a business does, where it operates, and how to contact it. Unstructured information requires interpretation, which introduces potential for errors.Types of Structured Data
Schema Markup
Schema Markup
Code added to websites that explicitly labels information. identifies business name, address, hours, and services in a format search engines understand precisely.
Business Listing Fields
Business Listing Fields
Standardized fields in platforms like Google Business Profile. Categories, service areas, attributes, and hours follow defined formats rather than free text.
Data Aggregator Formats
Data Aggregator Formats
Aggregators distribute business information to directories using standardized data formats. Consistent submission ensures accurate distribution.
Why Structured Data Matters
AI systems and voice assistants rely on structured data to provide accurate answers. When a user asks “What time does [business] close?”, the answer comes from structured hours fields, not from parsing website text.Businesses with complete structured data across platforms are more likely to appear in AI-generated answers, voice search results, and rich search features like knowledge panels.
- Higher confidence in information accuracy
- Better matching to specific queries
- Eligibility for rich results and featured snippets
- Consistent information across AI platforms
What This Means for Visibility
The evolution of search creates new requirements for local businesses:| Change | Implication |
|---|---|
| AI synthesis | Information must be consistent across all sources |
| Voice answers | Single result means only top listings get mentioned |
| Intent matching | Services and specialties must be clearly documented |
| Structured data | Machine-readable formats increase accuracy and visibility |
Next: What This Means for Local Businesses
Practical implications and action priorities for adapting to search evolution