Challenge: Measuring Visibility in an AI-First Search Era
As search evolved beyond traditional blue links, Dun & Bradstreet faced a new visibility challenge.
While the website had a strong SEO foundation, there was limited clarity on how the brand appeared across:
- Google AI Overviews
- LLM-powered discovery platforms such as ChatGPT, Gemini, and Perplexity
Key questions remained unanswered:
- Which keywords triggered AI Overviews?
- Was Dun & Bradstreet being cited in AI-generated answers?
- How much traffic was actually coming from LLM platforms?
- How could AI visibility be validated with confidence?
Traditional SEO tools were not designed to answer these AI-specific questions.
Objective: Expand tracking beyond rankings to measure AI discoverability.
Key Objectives
- Increase keyword presence and share within Google AI Overviews
- Measure and grow traffic originating from LLM platforms
- Improve brand mention frequency inside AI-generated responses
- Gain reliable, verifiable insights into AI search performance
Why Infigrowth Was Chosen?
Infigrowth was implemented to provide dedicated visibility into AI-driven search, something traditional SEO tools could not offer.
With native support for:
- AI Overview tracking
- LLM traffic and visibility measurement
- SERP snapshots and comparisons
- AI keyword opportunity analysis
Infigrowth became the central platform for monitoring, validating, and scaling AI search performance.
Strategy & Implementation Using Infigrowth
1. AI Overview Keyword Opportunity Identification
Using Infigrowth’s AI Overview Tracking module, the team analyzed large keyword sets in the B2B data and analytics space to identify:
2. Entity & Semantic Optimization Guided by Competitor Insights
Insights from Infigrowth highlighted gaps in semantic and entity alignment.
Key landing pages were optimized to:
- Strengthen entity relationships around business intelligence and decisioning data
- Improve contextual relevance for AI interpretation
- Increase factual authority and summarization readiness for AI systems
3. LLM Traffic & Visibility Tracking
Using Infigrowth’s LLM Traffic reports, the team gained visibility into:
- Traffic originating from ChatGPT, Gemini, and Perplexity and more
- Pages and topics surfaced by LLMs
- Location-based LLM traffic
These insights informed the creation of conversational content, structured FAQs, and context-rich definitions aligned with LLM consumption patterns.
4. SERP Snapshot & Comparison for Validation
To ensure data accuracy and reporting confidence, SERP Snapshots and SERP Comparison were used to:
- Validate keyword rankings against live SERPs
- Compare AI Overview presence across different dates
- Monitor SERP volatility and competitive movement
This removed ambiguity from reporting and ensured decisions were backed by real SERP evidence.
