What queries should you run to monitor competitor visibility in AI search?
Start with a structured test suite of 50–100 prompts, organized into three buyer journey stages: awareness, consideration, and decision.
Onely recommends 50–100 queries as the minimum viable test suite for meaningful LLM Share of Voice tracking, run monthly or quarterly. Fewer queries produce results too thin to act on.
Build your suite in this order:
- List every category-level question a buyer asks before they know your brand exists
- Add direct comparison prompts that name you and your top three competitors
- Add decision-stage prompts that specify company size, use case, or budget
- Remove duplicates and conslidate prompts that would return identical responses
- Group the final list by stage before running
Before running this suite against competitors, audit your own site's AI readiness first. Sona AI Visibility surfaces how crawlable and citable your content is. Gaps there explain gaps in your results before you even look at what competitors are doing.
What metrics actually measure competitor visibility in LLM responses?
Four metrics matter: Share of Voice, mention rate, citation frequency, and position. Each tells you something different about where competitors are winning inside AI answers.
Brands appearing in the first two sentences of a multi-brand LLM response are five times more likely to enter consideration. A competitor mentioned tenth in every response is not the same threat as one mentioned first in half your prompts.
Use Sona Scoring logic to decide which competitors to track first. Score them by current market presence, ICP overlap, and the query types where they appear most.
How do you manually run competitor monitoring queries across ChatGPT, Perplexity, and Claude?
For suites under 50 queries, manual tracking across all three platforms is viable — but only if you document results in a consistent format from the first run.
Manual querying across ChatGPT, Perplexity, and Claude is the recommended starting point for teams with fewer than 50 queries before scaling to hybrid automation.
Follow this sequence for every query:
- Open each platform in a private or incognito window to eliminate personalization bias
- Run each query verbatim — no follow-up prompts in the same thread
- Record: platform, prompt, brands mentioned, order of mention, whether cited as a source, response date
- Repeat across all three platforms before moving to the next query
- Log everything in a shared spreadsheet before interpreting results
SitePoint's LLM tracker analysis confirms that manual brand status checks remain reliable for establishing baselines before investing in paid tooling.
Platform behavior differs in ways that matter. ChatGPT synthesizes responses with fewer explicit citations. Perplexity surfaces citations directly, making source tracking straightforward. Claude hedges recommendations more than the others, which affects how confidently it names specific brands. Track each platform separately. Cross-platform averages hide these patterns.
If your brand is absent from responses where competitors appear, run a crawlability check on Sona AI Visibility before drawing content conclusions. A technical indexing gap explains more absences than a content gap does.
How do you interpret citation frequency data to identify which competitors LLMs favor?
Citation frequency tells you which competitors AI engines treat as authoritative sources — not just which brands they mention, but which ones they trust enough to attribute.
Peec AI tracks up to five competitors simultaneously for mention frequency and Share of Voice broken down by prompt category, revealing which query types systematically favor specific brands.
Work through this checklist after each tracking cycle:
- Which competitors appear most in awareness-stage prompts? (Category authority signal)
- Which appear most in decision-stage prompts? (Buying-intent authority signal)
- Which are cited as sources vs. just named? (Structural content signal — their pages are indexed and trusted)
- Which platforms favor which competitors? (Platform-specific content format signal)
- Which prompts return zero mentions of your brand? (Immediate GEO content gap)
Vryse's competitive benchmarking research shows that FAQ pages, comparison pages, and schema-marked articles drive citation rates more than topic coverage alone. If a competitor dominates Perplexity citations, audit their content structure before their content calendar.
Decision-stage citation patterns connect directly to in-market buying behavior. Use Sona Intent Signals to correlate which accounts are researching the same categories where competitors dominate citations.
How do you benchmark your brand's Share of Voice against competitors over time?
Run your full query suite on a fixed monthly or quarterly schedule, calculate SOV for each brand across each platform, and track the delta — not the absolute number — to identify who is gaining ground and where.
A Share of Voice above 20% in a competitive category is the benchmark for meaningful AI recommendation presence, according to Onely's 2026 LLM tracking methodology. Below 10%, the priority is citation-building, not SOV optimization.
Manual runs work for the first two or three cycles. Once you have baseline data, layer in an automated tool. Otterly AI at $29/month handles basic LLM auditing and integrates with Semrush. Peec AI runs daily prompt checks across platforms. AIClicks research and LLMRefs both confirm that daily automated monitoring catches the shifts that matter most — a competitor earning a single high-authority citation can move SOV within days. Use automation for frequency; use human review for interpretation.
When SOV gains start translating into pipeline, connect that loop through Sona Attribution to prove which AI visibility improvements drove revenue. For the full strategic overview, see our guide on what are the best competitor analysis tools for AI search and LLMs.
Comparison table: manual vs. hybrid vs. automated LLM competitor tracking
Sources: Onely, Vryse, LLMClicks.ai
Frequently asked questions
How often should I run competitor visibility checks across ChatGPT, Perplexity, and Claude?
Run your full query suite monthly or quarterly for trend benchmarking. Use a daily automated tool like Peec AI between cycles to catch sudden shifts. LLM responses change without warning when a competitor publishes new content or earns new citations, and a monthly-only cadence misses those moves entirely.
Does Share of Voice in LLM responses correlate with actual traffic or pipeline?
Not directly, but citation frequency and first-position mentions correlate with authority signals that influence how AI engines weight your content over time. Brands mentioned in the first two sentences of multi-brand responses are five times more likely to enter consideration. That consideration gap compounds across every buyer who uses AI search before contacting a vendor.
Why track ChatGPT, Perplexity, and Claude separately instead of averaging results?
Each platform has distinct citation behavior. Perplexity surfaces explicit sources. ChatGPT synthesizes without always attributing. Claude hedges recommendations more than the others. Averaging erases these patterns — the gaps between platforms tell you which content formats to prioritize for each engine.
What is a realistic Share of Voice target for a B2B brand new to LLM tracking?
Establish your baseline across one full query suite run before setting a target. In competitive categories, 20%+ SOV marks meaningful presence. Below 10%, focus on citation-building through structured content, FAQ schema, and comparison pages before optimizing for SOV percentage.
How do I know which prompts to prioritize in my query test suite?
Map prompts to buyer journey stages. Awareness prompts reveal category authority; decision prompts reveal buying-intent authority. If competitors dominate decision-stage prompts and you appear only in awareness prompts, you have a bottom-of-funnel content gap that directly affects pipeline.
Can I track LLM competitor visibility without paid tools?
Yes, for suites under 50 queries. Open each platform in a private window, run queries verbatim, and log results in a spreadsheet. Paid tools become necessary when you need daily monitoring, 100+ queries, or cross-platform dashboards at scale.
Last updated: April 2026
