How to Conduct Competitive Analysis Using Generative AI Search Data

Why Generative AI Search Data Is a Competitive Intelligence Goldmine

Traditional competitive analysis in search meant checking keyword rankings, backlink profiles, and domain authority scores. That data is still useful. But it tells you about the past — which pages earned authority over the last few years. Generative AI search data tells you something different and arguably more actionable: which brands are being recommended to buyers right now, in the moments of highest purchase intent.

When a potential customer asks Perplexity "what's the best [category] tool for a mid-size company," the brands that appear in that answer are the ones being actively recommended. That's real-time competitive intelligence that no traditional SEO tool can surface.

What Is Generative AI Search Competitive Data?

Generative AI search competitive data refers to information about how your competitors are represented — or not represented — in AI-generated responses. This includes which competitors are cited for queries in your category, which sources drive their AI citations, how often they appear compared to you (share of voice), and how AI engines characterize their brand relative to yours.

Collecting and analyzing this data systematically is the foundation of a modern AI competitive analysis.

How to Conduct a Generative AI Competitive Analysis: Step by Step

Step 1: Identify Your Key Competitors

Start with the competitors you already track in traditional SEO. Then expand that list by prompting AI engines with your target queries and noting which brands appear. You may find new competitors you weren't previously tracking — brands that have invested specifically in AI visibility and are winning share in that channel before you've noticed them in traditional search.

Step 2: Build a Shared Query Library

Use the same query library you built for your own brand monitoring — but now run those queries with competitive awareness. This query library is essentially your brand's prompt universe, applied competitively. For each query, document every brand that appears in the answer, not just whether you appeared. This gives you a complete picture of the competitive landscape in AI search for your category.

Step 3: Analyze Which Sources Drive Competitor Citations

When an AI engine cites a competitor, it's usually citing a specific source: a blog post, a press article, a G2 review, a Reddit thread. That source is the reason the competitor appeared. By identifying and analyzing those sources, you learn what content investments your competitors have made that are paying off in AI visibility — and what you could produce to compete.

Step 4: Measure Share of Voice in AI Search

Share of Voice (SOV) in AI search is the percentage of relevant AI-generated answers that mention your brand versus your competitors. If your category has five major players and you appear in 20% of relevant answers while the category leader appears in 55%, you have a quantified gap — and a target. Track SOV monthly to measure whether your content and PR investments are moving the needle. See the GEO metrics and dashboards we use to track share of voice over time.

Step 5: Identify Queries Where Competitors Are Weak

Not all queries are equally contested. Run your full query library and look for relevant, high-volume queries where no strong competitor has established AI visibility. These are your opportunity queries — the ones where a focused content investment could quickly establish your brand as the go-to cited source before competitors notice the gap.

What Tools Support AI Competitive Analysis?

Several platforms have built competitive tracking directly into their AI search monitoring products. Profound offers competitive share-of-voice reporting across multiple AI engines. Peec AI allows side-by-side comparison of brand appearance rates. Semrush's AI Overview tracking includes competitor data for Google specifically. For a manual approach, a structured spreadsheet tracking competitor appearances by query and by AI engine gives you the same insights — just more slowly.

A Real-World Competitive Insight We Found for a Client

During a generative AI competitive analysis for a B2B software client, we discovered that a smaller, less-established competitor was appearing in 3x more Perplexity answers than our client — despite having lower domain authority and fewer backlinks. When we dug into the sources driving that visibility, we found the competitor had published a series of highly structured, data-rich comparison guides that Perplexity was pulling from repeatedly.

Our client replicated that format for their own comparison content and within 60 days had closed a significant portion of the AI visibility gap. The insight came entirely from AI search competitive data — it was invisible in traditional SEO tools.

Turning Competitive Data Into a Content Strategy

The most valuable output of a generative AI competitive analysis isn't a report. It's a prioritized content roadmap that answers: what topics should we publish on to take share from competitors in AI search, what format and structure should that content follow to maximize AI citation rates, and which competitors' citation sources should we actively try to displace? Answer those three questions with real data, and you have a content strategy that's grounded in how AI search actually works today.

Ready to run this analysis for your brand? Our GEO Audit includes competitive benchmarking across your full prompt universe, with actionable recommendations for closing the gap.