Search Intent Analyzer

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:

INFORMATIONAL

enum

User wants to learn or find information (how-to, guides, definitions)

NAVIGATIONAL

enum

User wants to find a specific website or page (brand searches, login pages)

TRANSACTIONAL

enum

User wants to complete an action (buy, download, sign up)

COMMERCIAL

enum

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']}")

Output:

{
    '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.

keyword

string

required

The search query to analyze

serp_features

list[string]

List of SERP features present (from DataForSEO). Examples: featured_snippet, people_also_ask, shopping_results, local_pack, ads, video, carousel

top_results

list[dict]

Top ranking pages with titles/descriptions. Each dict should contain:

title (string): Page title
description (string): Meta description
url (string): Page URL

keyword

string

The analyzed keyword

primary_intent

string

Primary intent classification: informational, navigational, transactional, or commercial_investigation

secondary_intent

string | null

Secondary intent if within 15% confidence of primary, otherwise None

confidence

dict

Confidence scores for each intent type (percentages summing to 100)

signals_detected

dict

Detected signals for each intent type (keyword patterns and SERP features)

recommendations

list[string]

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

Example 1: Informational Query

result = analyze_intent("how to start a podcast")

print(result['primary_intent'])  # 'informational'
print(result['confidence']['informational'])  # 75.0

Recommendations:

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)

Recommendations:

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

Recommendations:

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

Recommendations:

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'])

Output:

{
    '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")

Example: “best podcast hosting” might have:

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

Keyword Analyzer - Optimize keyword usage after determining intent
Content Length Comparator - Match content length to intent type
SEO Quality Rater - Ensure content matches intent requirements

Documentation Index

​Intent Types

​Basic Usage

​Class API

​SearchIntentAnalyzer

​analyze()

​Intent Detection Logic

​1. Keyword Pattern Analysis

​2. SERP Features Analysis

​3. Content Pattern Analysis

​Real-World Examples

​Example 1: Informational Query

​Example 2: Commercial Query with SERP Data

​Example 3: Transactional Query

​Example 4: Navigational Query

​Signal Detection

​Secondary Intent

​Integration with DataForSEO

​Best Practices

​Related Modules

Intent Types

Basic Usage

Class API

SearchIntentAnalyzer

analyze()

Intent Detection Logic

1. Keyword Pattern Analysis

2. SERP Features Analysis

3. Content Pattern Analysis

Real-World Examples

Example 1: Informational Query

Example 2: Commercial Query with SERP Data

Example 3: Transactional Query

Example 4: Navigational Query

Signal Detection

Secondary Intent

Integration with DataForSEO

Best Practices

Related Modules