Multi-Engine AEO Tracking Playbook

Executive Summary

This playbook provides a comprehensive framework for tracking and measuring AI answer presence across multiple AI-powered search platforms. Learn how to monitor citation frequency, track answer share, and measure AEO performance across ChatGPT, Perplexity, Google AI Overviews, Gemini, and Claude.

Introduction to Multi-Engine Tracking
Platform-Specific Tracking Methods
Query Selection Strategy
Tracking Templates & Tools
Data Collection Framework
Reporting Dashboards
Analysis & Optimization
Best Practices

1. Introduction to Multi-Engine Tracking

Multi-engine tracking is essential for comprehensive AEO measurement. Different AI platforms have different citation patterns, and tracking across all platforms provides a complete picture of your brand's AI answer presence.

Why Multi-Engine Tracking Matters

Complete Visibility: Understand your presence across all major AI platforms
Platform-Specific Optimization: Identify which platforms need more attention
Competitive Intelligence: Compare your performance against competitors
ROI Measurement: Track the impact of AEO efforts across platforms

2. Platform-Specific Tracking Methods

Each AI platform requires different tracking approaches.

2.1 ChatGPT Tracking

Manual query testing with brand name variations
Citation frequency analysis
Source link tracking
Answer quality assessment

2.2 Perplexity Tracking

Source citation tracking
Answer position monitoring
Link inclusion analysis
Co-citation patterns

2.3 Google AI Overviews Tracking

Overview inclusion monitoring
Source citation tracking
Position tracking
Click-through rate analysis

2.4 Gemini Tracking

Answer citation frequency
Source link tracking
Answer quality metrics
Brand mention analysis

2.5 Claude Tracking

Citation frequency monitoring
Source reference tracking
Answer completeness assessment
Brand authority signals

3. Query Selection Strategy

Selecting the right queries to track is critical for meaningful measurement.

3.1 Query Categories

Brand Queries: Direct brand name searches
Product Queries: Product and service searches
Problem Queries: Problem-solving searches
Comparison Queries: Competitive comparison searches
How-To Queries: Instructional searches

3.2 Query Prioritization

Prioritize high-volume queries

Focus on commercial intent queries

Include brand-relevant queries

Track competitive queries

Monitor emerging query trends

4. Tracking Templates & Tools

Use consistent templates and tools for reliable tracking.

4.1 Tracking Template Structure

Field	Description
Query	The search query tested
Platform	AI platform (ChatGPT, Perplexity, etc.)
Date	Date of tracking
Citation Status	Cited / Not Cited
Position	Position in answer (if cited)
Source URL	URL cited (if applicable)
Competitors Cited	List of competitors also cited

5. Data Collection Framework

Establish a systematic data collection process.

5.1 Collection Frequency

Weekly: High-priority queries
Monthly: Standard query set
Quarterly: Comprehensive audit

5.2 Data Storage

Centralized tracking spreadsheet
Database for historical data
Automated tracking tools where possible
Backup and version control

6. Reporting Dashboards

Create dashboards to visualize tracking data.

6.1 Key Metrics

Overall citation rate
Platform-specific citation rates
Citation trend over time
Query category performance
Competitive comparison

7. Analysis & Optimization

Use tracking data to inform optimization strategies.

7.1 Analysis Framework

Identify citation patterns
Compare platform performance
Analyze competitor presence
Identify optimization opportunities
Prioritize actions based on impact

8. Best Practices

Track consistently across all platforms

Use standardized tracking templates

Document all tracking activities

Regularly review and update query sets

Share insights with stakeholders

Iterate based on findings

Next Steps

Use this playbook to establish comprehensive multi-engine tracking. For questions or support, contact Surgeboom:

Phone: +1-857-567-2674
Contact Form: surgeboom.com/contact