Introducing Enhanced Web Insights: 3 New Reports & How to Use Them

Summary

Sensor Tower has significantly enhanced Web Insights, increasing website coverage sixfold to 4 million sites and 12 million paths across 56+ countries. Three new reports target critical ad ops pain points. The Traffic Flow report maps inbound/outbound traffic at the site level, allowing analysts to identify top referrers, benchmark competitor referral ecosystems, and uncover partnership opportunities.

This goes beyond basic channel attribution to show exact referral paths. The LLM Market Share report provides exclusive data on AI platform usage, including chat counts, user numbers, and engagement depth per platform like ChatGPT, Claude, and Gemini. This is crucial as AI-referred traffic grows and SEO shifts to Generative Engine Optimization (GEO).

The Gen AI Brand Mentions report tracks brand visibility in LLM conversations, enabling competitive share-of-voice analysis across markets. Additionally, Spike Analysis automatically detects and explains traffic anomalies, reducing manual investigation time. Key data points: 4M sites, 12M paths, 56 countries, and specific example of Domino's US traffic flow.

For UA teams, these tools enable more precise audience targeting and partner identification. Monetization teams can optimize placement strategies based on user journey context. The AI-focused reports address the emerging need to optimize for AI-driven discovery, a shift that forward-thinking brands are already prioritizing.

Sensor Tower's updates position it ahead of competitors like SimilarWeb and Comscore in AI analytics depth.

Analyst Note

The release signals that LLM-referred traffic has crossed the threshold from anecdotal to measurable, now warranting dedicated analytics infrastructure. Sensor Tower's introduction of a standalone LLM Market Share report—tracking chats, users, and messages across ChatGPT, Claude, Gemini, and DeepSeek—reflects the broader industry pivot from SEO to GEO. For UA managers, this matters because traditional attribution models built primarily on search engine referrals are increasingly incomplete as a share of discovery traffic.

The concurrent rollout of Traffic Flow adds referral mapping across inbound and outbound paths, enabling competitive benchmarking of site-to-site relationships rather than only direct acquisition channels. The key implication is the convergence of two previously siloed measurement domains: standard web referral analytics and AI platform usage data. Brand mention tracking within LLM conversations represents a new share-of-voice metric, distinct from social listening or search ranking intelligence.

Timing aligns with heightened advertiser concern over attribution fidelity as AI assistants increasingly intermediate the user journey.