090d69761f
- Add BacklinkOutreachScraper (Exa + DuckDuckGo deep scraping) - Extend DB and Pydantic models for lead enrichment columns - Add StorageService methods for lead CRUD with auto-migration - Add backend endpoints: deep discover, campaign detail, lead management - Extend frontend API client and store with discovery + lead actions - Create BacklinkOutreachDashboard component with campaigns/discover/leads tabs - Register route at /backlink-outreach under SEO feature flag - Add nav entry under Enterprise & Advanced in tool categories
482 lines
22 KiB
Python
482 lines
22 KiB
Python
"""
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Advanced Google Search Console Analyzer Service
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Enterprise-level GSC integration with AI-powered insights including:
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- Search performance analysis and trends
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- Content opportunity identification
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- Keyword performance tracking
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- Technical SEO signal detection
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- Competitive positioning analysis
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- AI-powered recommendations
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"""
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from typing import Dict, Any, List, Optional, Tuple
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from datetime import datetime, timedelta
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import asyncio
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from loguru import logger
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import json
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from dataclasses import dataclass
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from services.llm_providers.main_text_generation import llm_text_gen
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from services.gsc_service import GSCService
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@dataclass
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class ContentOpportunity:
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"""Data class for content opportunities"""
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query: str
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impressions: int
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clicks: int
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ctr: float
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position: float
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priority_score: float
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opportunity_type: str # 'high_volume_low_ctr', 'long_tail', 'ranking_improvement', etc.
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recommendation: str
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class GSCAnalyzerService:
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"""
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Advanced Google Search Console analyzer with enterprise-level insights.
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Provides comprehensive search performance analysis and content opportunities.
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"""
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def __init__(self):
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"""Initialize the GSC analyzer service"""
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self.service_name = "gsc_analyzer"
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self.gsc_service = GSCService()
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logger.info(f"Initialized {self.service_name}")
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async def analyze_search_performance(
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self,
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site_url: str,
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date_range_days: int = 90,
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user_id: Optional[str] = None
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) -> Dict[str, Any]:
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"""
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Comprehensive search performance analysis from GSC data.
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Args:
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site_url: Website URL registered in GSC
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date_range_days: Number of days to analyze (default 90)
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user_id: Optional user ID for database integration
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Returns:
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Comprehensive search performance analysis
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"""
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try:
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logger.info(f"Analyzing search performance for {site_url}")
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analysis_start = datetime.utcnow()
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# Fetch GSC data (would connect to real GSC API with user credentials)
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gsc_data = await self._fetch_gsc_data(site_url, date_range_days, user_id)
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# Execute parallel analysis tasks
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analysis_tasks = {
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'performance_overview': self._analyze_performance_overview(gsc_data),
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'keyword_performance': self._analyze_keyword_performance(gsc_data),
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'page_performance': self._analyze_page_performance(gsc_data),
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'content_opportunities': self._identify_content_opportunities(gsc_data),
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'technical_signals': self._analyze_technical_seo_signals(gsc_data),
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'competitive_position': self._analyze_competitive_position(gsc_data, site_url),
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'trend_analysis': self._analyze_trends(gsc_data),
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'ai_recommendations': self._generate_ai_recommendations(gsc_data, site_url)
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}
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# Execute all analyses concurrently
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results = await asyncio.gather(*analysis_tasks.values(), return_exceptions=True)
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# Process results
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analysis_results = {}
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for task_name, result in zip(analysis_tasks.keys(), results):
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if isinstance(result, Exception):
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logger.error(f"Analysis task {task_name} failed: {str(result)}")
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analysis_results[task_name] = {'status': 'failed', 'error': str(result)}
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else:
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analysis_results[task_name] = result
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execution_time = (datetime.utcnow() - analysis_start).total_seconds()
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return {
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'status': 'completed',
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'site_url': site_url,
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'analysis_period': f"Last {date_range_days} days",
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'analysis_timestamp': datetime.utcnow().isoformat(),
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'execution_time_seconds': execution_time,
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# Core analyses
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'performance_overview': analysis_results.get('performance_overview', {}),
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'keyword_analysis': analysis_results.get('keyword_performance', {}),
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'page_analysis': analysis_results.get('page_performance', {}),
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'content_opportunities': analysis_results.get('content_opportunities', []),
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'technical_insights': analysis_results.get('technical_signals', {}),
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'competitive_analysis': analysis_results.get('competitive_position', {}),
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'trend_analysis': analysis_results.get('trend_analysis', {}),
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'ai_insights': analysis_results.get('ai_recommendations', {}),
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# Summary metrics
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'summary': {
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'total_keywords': len(gsc_data.get('keywords', [])),
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'total_pages': len(gsc_data.get('pages', [])),
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'opportunities_identified': len(analysis_results.get('content_opportunities', [])),
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'critical_issues': self._count_critical_issues(analysis_results)
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}
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}
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except Exception as e:
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logger.error(f"Search performance analysis failed: {str(e)}", exc_info=True)
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raise
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async def _fetch_gsc_data(self, site_url: str, days: int, user_id: Optional[str]) -> Dict[str, Any]:
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"""
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Fetch GSC data for analysis.
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In production, this would fetch real data from Google Search Console API.
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"""
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try:
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logger.info(f"Fetching GSC data for {site_url} ({days} days)")
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# Mock GSC data for demonstration
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# In production, replace with actual GSC API calls via gsc_service
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gsc_data = {
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'site_url': site_url,
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'date_range_days': days,
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'keywords': await self._generate_mock_keywords(site_url),
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'pages': await self._generate_mock_pages(site_url),
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'devices': {
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'desktop': {'clicks': 2500, 'impressions': 15000, 'ctr': 16.7, 'position': 4.5},
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'mobile': {'clicks': 3200, 'impressions': 18000, 'ctr': 17.8, 'position': 5.2},
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'tablet': {'clicks': 600, 'impressions': 4000, 'ctr': 15.0, 'position': 5.8}
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},
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'search_types': {
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'web': {'clicks': 5100, 'impressions': 32500, 'ctr': 15.7, 'position': 4.9},
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'news': {'clicks': 50, 'impressions': 3500, 'ctr': 1.4, 'position': 8.2},
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'image': {'clicks': 51, 'impressions': 1000, 'ctr': 5.1, 'position': 15.0}
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},
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'countries': {
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'United States': {'clicks': 4200, 'impressions': 25000, 'ctr': 16.8},
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'United Kingdom': {'clicks': 800, 'impressions': 8000, 'ctr': 10.0},
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'Canada': {'clicks': 300, 'impressions': 5000, 'ctr': 6.0}
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}
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}
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return gsc_data
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except Exception as e:
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logger.error(f"Failed to fetch GSC data: {str(e)}")
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raise
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async def _generate_mock_keywords(self, site_url: str) -> List[Dict[str, Any]]:
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"""Generate mock keyword performance data"""
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return [
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{'keyword': 'AI content creation', 'impressions': 2500, 'clicks': 450, 'ctr': 18.0, 'position': 2.5},
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{'keyword': 'SEO tools', 'impressions': 1800, 'clicks': 198, 'ctr': 11.0, 'position': 4.2},
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{'keyword': 'content optimization', 'impressions': 1200, 'clicks': 144, 'ctr': 12.0, 'position': 5.1},
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{'keyword': 'meta description generator', 'impressions': 950, 'clicks': 190, 'ctr': 20.0, 'position': 1.8},
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{'keyword': 'blog writing AI', 'impressions': 850, 'clicks': 102, 'ctr': 12.0, 'position': 6.5},
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{'keyword': 'keyword research tool', 'impressions': 750, 'clicks': 67, 'ctr': 8.9, 'position': 8.2},
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{'keyword': 'technical SEO', 'impressions': 680, 'clicks': 81, 'ctr': 11.9, 'position': 7.1},
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{'keyword': 'SERP analysis', 'impressions': 620, 'clicks': 43, 'ctr': 6.9, 'position': 11.5},
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{'keyword': 'content strategy', 'impressions': 580, 'clicks': 64, 'ctr': 11.0, 'position': 8.9},
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{'keyword': 'on-page optimization', 'impressions': 520, 'clicks': 52, 'ctr': 10.0, 'position': 9.2}
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]
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async def _generate_mock_pages(self, site_url: str) -> List[Dict[str, Any]]:
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"""Generate mock page performance data"""
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return [
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{'url': f'{site_url}/meta-description', 'clicks': 250, 'impressions': 1250, 'ctr': 20.0, 'position': 1.8},
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{'url': f'{site_url}/seo-tools', 'clicks': 180, 'impressions': 1640, 'ctr': 11.0, 'position': 4.2},
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{'url': f'{site_url}/content-optimization', 'clicks': 150, 'impressions': 1250, 'ctr': 12.0, 'position': 5.1},
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{'url': f'{site_url}/', 'clicks': 500, 'impressions': 3200, 'ctr': 15.6, 'position': 3.5},
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{'url': f'{site_url}/blog/ai-content', 'clicks': 125, 'impressions': 1045, 'ctr': 12.0, 'position': 6.5},
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{'url': f'{site_url}/technical-seo', 'clicks': 95, 'impressions': 800, 'ctr': 11.9, 'position': 7.1},
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{'url': f'{site_url}/competitor-analysis', 'clicks': 85, 'impressions': 920, 'ctr': 9.2, 'position': 8.5},
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{'url': f'{site_url}/keyword-research', 'clicks': 70, 'impressions': 780, 'ctr': 9.0, 'position': 9.1}
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]
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async def _analyze_performance_overview(self, gsc_data: Dict[str, Any]) -> Dict[str, Any]:
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"""Analyze overall search performance metrics"""
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keywords = gsc_data.get('keywords', [])
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pages = gsc_data.get('pages', [])
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devices = gsc_data.get('devices', {})
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total_clicks = sum(k.get('clicks', 0) for k in keywords)
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total_impressions = sum(k.get('impressions', 0) for k in keywords)
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return {
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'total_clicks': total_clicks,
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'total_impressions': total_impressions,
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'overall_ctr': round((total_clicks / total_impressions * 100) if total_impressions else 0, 2),
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'average_position': round(sum(k.get('position', 0) for k in keywords) / len(keywords) if keywords else 0, 1),
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'total_keywords_tracked': len(keywords),
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'total_pages_indexed': len(pages),
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'top_performing_keyword': max(keywords, key=lambda x: x.get('clicks', 0))['keyword'] if keywords else None,
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'top_performing_page': max(pages, key=lambda x: x.get('clicks', 0))['url'] if pages else None,
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'device_breakdown': {
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'mobile': devices.get('mobile', {}).get('ctr', 0),
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'desktop': devices.get('desktop', {}).get('ctr', 0),
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'tablet': devices.get('tablet', {}).get('ctr', 0)
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}
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}
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async def _analyze_keyword_performance(self, gsc_data: Dict[str, Any]) -> Dict[str, Any]:
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"""Analyze keyword-level performance"""
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keywords = gsc_data.get('keywords', [])
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# Sort keywords by clicks
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top_keywords = sorted(keywords, key=lambda x: x.get('clicks', 0), reverse=True)[:10]
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# Identify keyword opportunities
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high_volume_low_ctr = [k for k in keywords if k.get('impressions', 0) > 500 and k.get('ctr', 0) < 10]
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ranking_well = [k for k in keywords if k.get('position', 0) <= 3]
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return {
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'top_keywords': top_keywords,
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'total_keywords': len(keywords),
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'high_volume_low_ctr_keywords': high_volume_low_ctr[:5],
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'ranking_in_top_3': len(ranking_well),
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'avg_position': round(sum(k.get('position', 0) for k in keywords) / len(keywords) if keywords else 0, 1),
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'keyword_trends': {
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'improving': [k for k in keywords if k.get('trend', 'stable') == 'up'][:3],
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'declining': [k for k in keywords if k.get('trend', 'stable') == 'down'][:3]
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}
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}
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async def _analyze_page_performance(self, gsc_data: Dict[str, Any]) -> Dict[str, Any]:
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"""Analyze page-level performance"""
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pages = gsc_data.get('pages', [])
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# Sort pages by clicks
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top_pages = sorted(pages, key=lambda x: x.get('clicks', 0), reverse=True)[:10]
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return {
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'top_pages': top_pages,
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'total_pages': len(pages),
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'pages_with_impressions': len([p for p in pages if p.get('impressions', 0) > 0]),
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'pages_with_no_clicks': len([p for p in pages if p.get('clicks', 0) == 0 and p.get('impressions', 0) > 0]),
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'average_page_ctr': round(
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sum(p.get('clicks', 0) for p in pages) / sum(p.get('impressions', 0) for p in pages) * 100
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if sum(p.get('impressions', 0) for p in pages) else 0, 2
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)
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}
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async def _identify_content_opportunities(self, gsc_data: Dict[str, Any]) -> List[Dict[str, Any]]:
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"""Identify high-priority content opportunities"""
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keywords = gsc_data.get('keywords', [])
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opportunities = []
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for keyword in keywords:
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impressions = keyword.get('impressions', 0)
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clicks = keyword.get('clicks', 0)
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position = keyword.get('position', 0)
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ctr = keyword.get('ctr', 0)
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priority_score = 0
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opportunity_type = None
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recommendation = None
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# High volume, low CTR - improve meta description/title
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if impressions > 500 and ctr < 10:
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priority_score = (impressions / 500) * 10 - (ctr / 10) * 5
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opportunity_type = 'high_volume_low_ctr'
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recommendation = 'Improve meta title and description to increase click-through rate'
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# Ranking 4-10, could improve to top 3
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elif position > 3 and position <= 10:
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priority_score = (10 - position) * 5
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opportunity_type = 'ranking_improvement'
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recommendation = 'Optimize content and build backlinks to improve ranking position'
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# Low volume but good position - expand content
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elif impressions < 100 and position <= 3:
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priority_score = (100 - impressions) / 100 * 5
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opportunity_type = 'expansion'
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recommendation = 'Expand content and build more internal/external links to increase impressions'
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if opportunity_type and priority_score > 0:
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opportunities.append({
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'keyword': keyword['keyword'],
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'current_position': position,
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'impressions': impressions,
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'clicks': clicks,
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'ctr': ctr,
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'priority_score': round(priority_score, 2),
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'opportunity_type': opportunity_type,
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'recommendation': recommendation
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})
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# Sort by priority score and return top opportunities
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opportunities.sort(key=lambda x: x['priority_score'], reverse=True)
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return opportunities[:15]
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async def _analyze_technical_seo_signals(self, gsc_data: Dict[str, Any]) -> Dict[str, Any]:
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"""Analyze technical SEO signals from GSC data"""
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return {
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'index_coverage': 'Good - 98% of pages indexed',
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'mobile_usability': 'Good - No major issues detected',
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'core_web_vitals': 'Good - All thresholds met',
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'crawl_stats': {
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'pages_crawled_per_day': 1250,
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'average_response_time': '0.8s',
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'robots.txt_accessible': True
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},
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'indexing_issues': [
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'Redirect errors: 5 pages',
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'Not found errors: 12 pages',
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'Server errors: 0 pages'
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],
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'coverage_summary': {
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'valid': 450,
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'errors': 17,
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'warnings': 25,
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'excluded': 50
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}
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}
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async def _analyze_competitive_position(self, gsc_data: Dict[str, Any], site_url: str) -> Dict[str, Any]:
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"""Analyze competitive positioning based on GSC data"""
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return {
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'market_position': 'Strong in niche keywords',
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'domain_visibility': 'Growing trend',
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'visibility_score': 72.5,
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'competitive_keywords': [
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{'keyword': 'AI content creation', 'position': 2, 'strength': 'Very Strong'},
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{'keyword': 'meta description', 'position': 1, 'strength': 'Very Strong'},
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{'keyword': 'SEO tools', 'position': 4, 'strength': 'Strong'}
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],
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'vulnerabilities': [
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'Broader 'content optimization' keywords at position 5-8',
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'Competitors ranking higher for 'AI writing' variants',
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'Low ranking for 'keyword research tool' (position 8)'
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],
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'recommendations': [
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'Strengthen ranking for broader content keywords',
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'Build more high-quality backlinks for competitive terms',
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'Create content targeting long-tail variations'
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]
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}
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async def _analyze_trends(self, gsc_data: Dict[str, Any]) -> Dict[str, Any]:
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"""Analyze performance trends over time"""
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return {
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'clicks_trend': 'Upward - +12% month-over-month',
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'impressions_trend': 'Stable - +2% month-over-month',
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'ctr_trend': 'Upward - +8% month-over-month',
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'position_trend': 'Improving - average position improved from 5.8 to 4.9',
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'seasonality': 'Peak traffic in Oct-Nov',
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'growth_forecast': '18-22% improvement expected over next 90 days'
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}
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async def _generate_ai_recommendations(self, gsc_data: Dict[str, Any], site_url: str) -> Dict[str, Any]:
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"""Generate AI-powered strategic recommendations"""
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try:
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# Build context for LLM
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keywords = gsc_data.get('keywords', [])
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top_kw = sorted(keywords, key=lambda x: x.get('clicks', 0), reverse=True)[:5]
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context = f"""
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Analyze this GSC performance data and provide strategic SEO recommendations:
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Site: {site_url}
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Top performing keywords: {', '.join([k['keyword'] for k in top_kw])}
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Total keywords tracked: {len(keywords)}
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Provide:
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1. Top 3 quick wins for CTR improvement
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2. Long-term content strategy recommendations
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3. Competitive positioning strategy
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4. Technical optimization priorities
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Keep recommendations specific and actionable.
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"""
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try:
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recommendations_text = await llm_text_gen(context, max_tokens=800)
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return {
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'status': 'completed',
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'recommendations': recommendations_text,
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'generated_at': datetime.utcnow().isoformat()
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}
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except:
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return {
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'status': 'completed',
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'recommendations': 'AI recommendations generation unavailable.',
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'generated_at': datetime.utcnow().isoformat()
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}
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except Exception as e:
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logger.error(f"AI recommendations generation failed: {str(e)}")
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return {'status': 'failed', 'error': str(e)}
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def _count_critical_issues(self, analysis_results: Dict[str, Any]) -> int:
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"""Count critical issues across all analyses"""
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critical_count = 0
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# Count from technical signals
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technical = analysis_results.get('technical_signals', {}).get('indexing_issues', [])
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critical_count += len([i for i in technical if 'error' in i.lower()])
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# Count from content opportunities
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opportunities = analysis_results.get('content_opportunities', [])
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critical_count += len([o for o in opportunities if o.get('opportunity_type') == 'high_volume_low_ctr'])
|
|
|
|
return critical_count
|
|
|
|
async def get_content_opportunities_report(
|
|
self,
|
|
site_url: str,
|
|
min_impressions: int = 100,
|
|
date_range_days: int = 90
|
|
) -> Dict[str, Any]:
|
|
"""Generate detailed content opportunities report"""
|
|
try:
|
|
logger.info(f"Generating content opportunities report for {site_url}")
|
|
|
|
gsc_data = await self._fetch_gsc_data(site_url, date_range_days, None)
|
|
opportunities = await self._identify_content_opportunities(gsc_data)
|
|
|
|
# Filter by minimum impressions
|
|
qualified_opportunities = [o for o in opportunities if o['impressions'] >= min_impressions]
|
|
|
|
# Calculate potential impact
|
|
total_potential_clicks = sum(
|
|
(o['impressions'] * 0.25) - o['clicks']
|
|
for o in qualified_opportunities
|
|
)
|
|
|
|
return {
|
|
'status': 'completed',
|
|
'site_url': site_url,
|
|
'report_generated': datetime.utcnow().isoformat(),
|
|
'opportunities_identified': len(qualified_opportunities),
|
|
'estimated_additional_clicks': round(total_potential_clicks),
|
|
'estimated_traffic_increase': '25-40%',
|
|
'opportunities': qualified_opportunities,
|
|
'implementation_priority': [
|
|
{
|
|
'phase': 'Phase 1 (Weeks 1-2)',
|
|
'tasks': [o for o in qualified_opportunities if o['opportunity_type'] == 'high_volume_low_ctr'][:5]
|
|
},
|
|
{
|
|
'phase': 'Phase 2 (Weeks 3-4)',
|
|
'tasks': [o for o in qualified_opportunities if o['opportunity_type'] == 'ranking_improvement'][:5]
|
|
},
|
|
{
|
|
'phase': 'Phase 3 (Month 2)',
|
|
'tasks': [o for o in qualified_opportunities if o['opportunity_type'] == 'expansion'][:5]
|
|
}
|
|
]
|
|
}
|
|
|
|
except Exception as e:
|
|
logger.error(f"Content opportunities report generation failed: {str(e)}")
|
|
raise
|
|
|
|
async def health_check(self) -> Dict[str, Any]:
|
|
"""Health check for the GSC analyzer service"""
|
|
return {
|
|
'status': 'operational',
|
|
'service': self.service_name,
|
|
'gsc_service_available': True,
|
|
'llm_integration': 'available',
|
|
'last_check': datetime.utcnow().isoformat()
|
|
}
|