The Search Intent Analyzer determines the search intent of a query by analyzing SERP features and content patterns. It classifies queries into four intent types: Informational, Navigational, Transactional, or Commercial Investigation.
Intent Types
The analyzer uses the SearchIntent enum with four intent classifications:
User wants to learn or find information (how-to, guides, definitions)
User wants to find a specific website or page (brand searches, login pages)
User wants to complete an action (buy, download, sign up)
User is researching before purchase (reviews, comparisons, alternatives)
Basic Usage
Use the convenience function for quick analysis:
from data_sources.modules.search_intent_analyzer import analyze_intent
result = analyze_intent("how to start a podcast")
print(f"Primary Intent: {result['primary_intent']}")
print(f"Confidence: {result['confidence']}")
print(f"Recommendations: {result['recommendations']}")
{
'keyword': 'how to start a podcast',
'primary_intent': 'informational',
'secondary_intent': None,
'confidence': {
'informational': 75.0,
'navigational': 5.0,
'transactional': 10.0,
'commercial_investigation': 10.0
},
'signals_detected': {
'informational': [
"Keyword contains 'how'",
"Keyword contains 'guide'"
]
},
'recommendations': [
'Create comprehensive, educational content',
'Include step-by-step instructions or explanations',
'Answer common questions (People Also Ask)',
'Use FAQ sections and definition boxes',
'Target featured snippet optimization',
'Include videos, images, and visual aids'
]
}
Class API
SearchIntentAnalyzer
The main analyzer class:
from data_sources.modules.search_intent_analyzer import SearchIntentAnalyzer
analyzer = SearchIntentAnalyzer()
result = analyzer.analyze(
keyword="best podcast hosting platforms",
serp_features=["carousel", "people_also_ask", "video"],
top_results=[
{
'title': 'Top 10 Podcast Hosting Platforms in 2024',
'description': 'Compare the best podcast hosting services...',
'url': 'https://example.com/podcast-hosting'
}
]
)
analyze()
Analyze search intent of a keyword.
The search query to analyze
List of SERP features present (from DataForSEO). Examples: featured_snippet, people_also_ask, shopping_results, local_pack, ads, video, carousel
Top ranking pages with titles/descriptions. Each dict should contain:
title (string): Page titledescription (string): Meta descriptionurl (string): Page URL
Primary intent classification: informational, navigational, transactional, or commercial_investigation
Secondary intent if within 15% confidence of primary, otherwise None
Confidence scores for each intent type (percentages summing to 100)
Detected signals for each intent type (keyword patterns and SERP features)
Content strategy recommendations based on detected intent
Intent Detection Logic
The analyzer uses three scoring methods:
1. Keyword Pattern Analysis
Detects intent from keyword patterns:
# Informational signals
['what', 'why', 'how', 'when', 'where', 'who', 'guide', 'tutorial',
'learn', 'tips', 'best practices', 'explained', 'definition']
# Navigational signals
['login', 'sign in', 'website', 'official', 'home page', 'account']
# Transactional signals
['buy', 'purchase', 'order', 'download', 'pricing', 'cost',
'sign up', 'subscribe', 'coupon', 'deal', 'discount']
# Commercial signals
['best', 'top', 'review', 'vs', 'versus', 'compare', 'comparison',
'alternative', 'better than', 'or', 'option']
2. SERP Features Analysis
Maps SERP features to intent types:
# Informational
'featured_snippet', 'knowledge_graph', 'people_also_ask', 'video'
# Transactional
'shopping_results', 'local_pack', 'ads'
# Commercial
'carousel'
3. Content Pattern Analysis
Analyzes titles and descriptions of top-ranking pages for intent indicators.
Real-World Examples
result = analyze_intent("how to start a podcast")
print(result['primary_intent']) # 'informational'
print(result['confidence']['informational']) # 75.0
- Create comprehensive, educational content
- Include step-by-step instructions
- Target featured snippet optimization
- Add FAQ sections
Example 2: Commercial Query with SERP Data
result = analyze_intent(
"best podcast hosting platforms",
serp_features=['carousel', 'people_also_ask', 'video'],
top_results=[
{
'title': 'Top 10 Podcast Hosting Platforms in 2024',
'description': 'Compare the best podcast hosting services for your show',
'url': 'https://example.com/best-podcast-hosting'
},
{
'title': 'Best Podcast Hosting Services: Expert Review',
'description': 'Our in-depth review of podcast hosting platforms',
'url': 'https://example.com/podcast-hosting-review'
}
]
)
print(result['primary_intent']) # 'commercial_investigation'
print(result['confidence']['commercial_investigation']) # 65.0
print(result['secondary_intent']) # 'informational' (within 15% confidence)
- Create comparison and review content
- Include pros/cons and alternatives
- Add detailed feature breakdowns
- Show ‘best for’ categories
Example 3: Transactional Query
result = analyze_intent(
"buy podcast microphone",
serp_features=['shopping_results', 'ads', 'local_pack']
)
print(result['primary_intent']) # 'transactional'
print(result['confidence']['transactional']) # 80.0
- Focus on product/service pages
- Include clear pricing and purchase options
- Add trust signals (reviews, testimonials)
- Optimize for conversion, not just traffic
- Include strong, action-oriented CTAs
Example 4: Navigational Query
result = analyze_intent("spotify for podcasters login")
print(result['primary_intent']) # 'navigational'
print(result['confidence']['navigational']) # 85.0
- Optimize for brand-related searches
- Ensure homepage/key pages rank well
- Include site navigation and clear CTAs
- Strengthen brand presence
Signal Detection
The signals_detected field shows which patterns triggered each intent:
result = analyze_intent(
"how to buy the best podcast microphone",
serp_features=['shopping_results', 'people_also_ask']
)
print(result['signals_detected'])
{
'informational': [
"Keyword contains 'how'",
"SERP has people_also_ask"
],
'transactional': [
"Keyword contains 'buy'",
"SERP has shopping_results"
],
'commercial': [
"Keyword contains 'best'"
]
}
Secondary Intent
When secondary intent is detected (within 15% confidence of primary), consider blending content approaches:
if result['secondary_intent']:
print(f"Primary: {result['primary_intent']}")
print(f"Secondary: {result['secondary_intent']}")
print("Consider blending both content approaches")
- Primary: Commercial (65%) - comparison content
- Secondary: Informational (55%) - educational content
- Strategy: Create comparison article with educational elements
Integration with DataForSEO
Combine with DataForSEO for real SERP analysis:
from data_sources.modules.dataforseo import DataForSEO
from data_sources.modules.search_intent_analyzer import analyze_intent
# Get SERP data
dfs = DataForSEO()
serp_data = dfs.get_serp_data("your keyword")
# Analyze intent
result = analyze_intent(
keyword="your keyword",
serp_features=serp_data['features'],
top_results=serp_data['organic_results'][:10]
)
Best Practices
- Always provide SERP features when available for more accurate classification
- Include top 10 results for content pattern analysis
- Check for secondary intent - many queries have mixed intent
- Follow recommendations - they’re tailored to the detected intent type
- Monitor confidence scores - low confidence may indicate ambiguous intent