Keyword-Specific AI Visibility: Why Mention Rates Vary by Search Term (82.3% Average)

AI visibilityLLMRAG

Keyword-Specific AI Visibility: Why Mention Rates Vary by Search Term (82.3% Average)

What we measured

Between 2026-09-07 and 2026-10-06, we ran 390 AI visibility probes across 30 distinct days using Perplexity as the sole AI platform. Each probe queried one of 13 predefined keywords related to the monitored B2B software vendor's product categories. The vendor was mentioned in 321 of those 390 answers, producing an overall mention rate of 82.3%. The tone across all answers was overwhelmingly neutral at 381 instances (97.7%), with only 9 positive mentions (2.3%). The total cost for all probes was $2.34, averaging $0.0060 per probe.

This study has clear limitations. It covers a single AI platform, a single brand, and one tenant environment. The keyword set of 13 terms was selected by the research team and does not represent the full range of search queries a B2B software vendor might encounter. Results from Perplexity may not generalize to other AI answer engines or search platforms. The data reflects a single 30-day window and may shift with changes in the monitored vendor's digital footprint, content output, or the AI platform's knowledge cutoff and ranking behavior.

What the data shows

The most important finding is that mention rate varies significantly by keyword, even within the same brand and platform. The monitored B2B software vendor achieved a 97% mention rate on "smart education solution" but only 67% on "LLM deployment service"—a 30-point gap across the same 30-probe set per keyword. This variation suggests that AI platforms do not treat all keyword queries as equivalent when deciding whether to surface a given brand.

The full per-keyword breakdown is as follows:

The top three keywords—smart education solution, AI visual recognition, and smart community management—all exceeded 90% mention rate. The bottom three—government digitalization platform, data visualization dashboard, and LLM deployment service—fell at or below 73%. The middle cluster, spanning AI customer service system through e-commerce mall system, held steady in the 77% to 83% range.

Tone was nearly uniform. Of the 390 answers, 381 were neutral and 9 were positive. No negative tone appeared. This means that when the monitored B2B software vendor was mentioned, it was almost always in a factual or descriptive context rather than an opinionated one.

The domain citation data reveals which properties the AI platform draws from most heavily. The vendor's own domain, zzcxin.cn and its www subdomain, accounted for 901 combined citations (777 from www.zzcxin.cn and 124 from zzcxin.cn). The next most-cited domains belonged to third-party sources: etycx.com at 301 citations, a Sina content URL at 300, and qizhidao.com properties at 366 combined. The monitored vendor's own domain was the single most-cited source in the dataset, but it shared visibility with multiple aggregator and directory sites.

Why it matters for your brand

The 30-point spread between the highest and lowest mention rates is not noise. It is signal. AI platforms pull from a finite set of indexed sources, and those sources are not distributed evenly across keyword topics. When a query matches content that already exists in abundance on the monitored vendor's domain or on well-cited third-party sites, the AI platform is more likely to surface the brand. When the query maps to a topic area where the vendor has thin or no indexed content, the brand drops out of the answer entirely.

For B2B software vendors, this means your AI visibility is not a single score you can optimize once and forget. It is a set of keyword-specific visibility profiles that shift as your content footprint changes. A vendor that publishes heavily on education technology and AI vision use cases will naturally rank higher on those keywords than on unrelated terms like LLM deployment, regardless of brand strength or overall domain authority. The monitored B2B software vendor's 97% rate on smart education solution and 67% rate on LLM deployment service illustrates this pattern directly.

The near-total neutrality of tone also carries strategic implications. AI answer engines are not generating praise for the monitored vendor—they are reproducing factual references. That makes the visibility more durable than earned media coverage, but it also means there is no sentiment advantage to be gained from AI visibility alone. The brand is being cited, not recommended. For B2B buyers who rely on AI tools during vendor evaluation, a neutral citation is better than no citation, but it does not differentiate the brand from competitors who share the same mention space.

How to act on it

1. Map your current AI visibility by keyword. Run probes across your full keyword set using the same method—consistent probe count per keyword, a defined date range, and a single AI platform for comparability. Identify which terms already produce strong mention rates and which fall below 70%.

2. Invest content and technical SEO effort into your weakest keywords first. The monitored B2B software vendor's lowest three keywords—LLM deployment service, data visualization dashboard, and government digitalization platform—each sit at 67% to 73%. Creating or improving indexed content that directly addresses these terms is the fastest way to close that gap, because the AI platform is already considering the topic but not finding the brand with sufficient confidence.

3. Strengthen domain citation dominance on your own properties. The vendor's own domain generated 901 citations across two URL variants, far outpacing any single third-party source. Consolidating content, maintaining clean internal linking, and ensuring that product and case study pages are crawlable and indexable will reinforce the signal the AI platform receives when your keywords are queried.

4. Monitor tone alongside mention rate. The 97.7% neutral tone in this dataset means the brand is being referenced factually. If your probes start showing a rise in positive tone, investigate whether new content or third-party coverage is shifting the narrative. If negative tone appears, treat it as an early warning signal before it affects broader AI visibility.

5. Re-measure quarterly. The 30-day window used here captures a snapshot, not a trend. Running the same probe set every quarter will reveal whether your mention rates are holding, improving, or degrading on each keyword, and whether the AI platform's source preferences are shifting over time.

Bottom line

The monitored B2B software vendor was mentioned in 82.3% of Perplexity answers across 13 keywords, but that average hides a 30-point range driven entirely by keyword-specific content availability. The highest-mention keywords align with areas where the vendor's own domain and cited third-party sources have the strongest footprint. The lowest-mention keywords point to content gaps. AI visibility is not a brand-level metric—it is a keyword-level metric. The practical move is to treat each keyword as a separate visibility problem, invest where the gap is widest, and re-measure to confirm the investment is moving the number.