Collection Process
GenTrackr runs automated queries across AI platforms to track your app’s visibility. Here’s exactly how it works:1
Query Generation
We generate relevant queries based on:
- Your app’s category and features
- Common user search patterns
- Competitor landscape
- Trending topics in your niche
- “best meditation app for beginners”
- “apps to reduce anxiety”
- “compare headspace vs calm”
- “meditation timer app recommendations”
2
Automated Execution
Our system submits these queries to each AI platform daily:
- ChatGPT: Web version with GPT-4
- Claude: Claude.ai interface
- Perplexity: Perplexity.ai search
- Gemini: Google Gemini interface
3
Response Capture
We record complete AI responses including:
- Full text content
- App mentions (direct and indirect)
- Source citations
- Context and recommendations
- Timestamp and platform metadata
4
Data Analysis
Each response is analyzed for:
- Mention Detection: Is your app mentioned?
- Sentiment Analysis: How is it described?
- Ranking Position: Order in recommendations
- Feature Highlights: Which features are mentioned?
- Competitive Context: Who else is mentioned?
Query Categories
We organize queries into strategic categories to provide comprehensive visibility insights:Feature-Based Queries
Queries focused on specific app capabilities.Use Case Queries
Queries about solving specific user problems.Comparison Queries
Direct competitive comparison requests.Data Frequency
Query Volume by Plan
- Starter: 500 queries/day across all platforms
- Pro: 2,000 queries/day with advanced categories
- Studio: 5,000+ queries/day with custom query sets
What Gets Tracked
Direct Metrics
Mention Count
Mention Count
Total number of times your app is mentioned by name across all queries and platforms.Tracked: Daily, weekly, monthly aggregates
Visibility Score
Visibility Score
Percentage of relevant queries where your app appears.Formula: (Queries mentioning your app / Total queries run) × 100
Citation Frequency
Citation Frequency
How often AI models cite your app store listing or reviews as sources.Includes: App Store reviews, feature descriptions, screenshots
Ranking Position
Ranking Position
Average position when your app is mentioned in lists.Example: Mentioned 2nd in “top meditation apps” = position 2
Sentiment Metrics
Feature Attribution
Feature Attribution
Which features AI associates with your app.Tracked: Most mentioned features, feature sentiment, competitive differentiation
Use Case Association
Use Case Association
What problems AI thinks your app solves.Example: “anxiety relief”, “sleep improvement”, “mindfulness training”
Sentiment Tone
Sentiment Tone
Overall tone of AI mentions (positive, neutral, negative).Analyzed: Language patterns, recommendation strength, comparison context
Competitive Metrics
- Apps frequently mentioned alongside yours
- Queries where competitors appear but you don’t
- Competitive win rate (you vs specific competitors)
- Share of voice in your category
Data Storage & History
Data Retention
- Starter: 30 days of detailed data, 90 days of aggregates
- Pro: 180 days detailed, 365 days aggregates
- Studio: Unlimited retention
- Encrypted at rest and in transit
- Backed up daily
- Accessible via dashboard and API
- Exportable to CSV/JSON
Quality Assurance
We ensure data accuracy through:Automated Validation
- Response completeness checks
- Duplicate detection
- Error handling and retries
- Platform availability monitoring
Manual Auditing
- Weekly spot checks of query results
- Monthly quality reviews
- User-reported issue investigation
- Continuous algorithm improvement
Privacy & Ethics
Our data collection complies with:- All platform terms of service
- GDPR and international privacy laws
- Ethical web scraping practices
- Industry data collection standards
Next Steps
AI Platforms
Learn about each platform we track
Tracking Visibility
How to interpret your visibility data