The Metrics That Are Replacing Keyword Rankings

For a long time, keyword rankings were the north star metric of digital marketing. If you ranked first for the right terms, traffic followed. The relationship was reasonably clean.

That relationship is breaking down. Not disappearing — traditional search still matters — but a growing share of discovery now happens in AI assistants, and keyword rankings tell you nothing about what’s happening there. You can rank first for your category’s core terms and be completely absent from AI-generated recommendations.

The discipline that’s emerging to fill this gap uses different metrics. They’re less familiar but map directly to what actually matters in AI-mediated discovery.

Mention frequency. How often does your brand appear across a defined set of prompts run against the AI platforms your buyers use? This is the basic presence metric. If you’re running 20 prompts across ChatGPT, Claude, and Perplexity, and your brand appears in 4 of 60 total responses, your mention frequency is about 7%. Most teams are shocked by how low this number is when they first measure it.

Mention sentiment. Not just whether you’re mentioned, but how. An AI model that describes you as “has mixed reviews, with users frequently citing support delays” is doing you real damage even while technically including you. Sentiment scoring captures the tone and framing of mentions, which determines whether being in the response helps or hurts.

Share of voice in AI responses. Across prompts where your category is being discussed, what percentage of brand mentions go to you versus competitors? This is the AI equivalent of search market share, and it’s often more skewed than traditional search — because AI models tend to name one or two brands confidently rather than returning ten results for the user to evaluate.

Context quality. Is your mention substantive or passing? There’s a difference between “you might also consider Brand X” and “Brand X is specifically well-suited for this use case because of Y.” The first is faint presence. The second is genuine endorsement. Tracking context quality separates meaningful mentions from token inclusions.

What’s useful about these metrics, beyond the immediate data, is that they point toward specific actions. Low mention frequency in problem-oriented queries tells you where your content has gaps. Negative sentiment around a specific theme tells you where you need to either address the underlying issue or reframe the narrative in public content. Low share of voice in comparison queries tells you which competitor relationships you need to directly address.

The downside is that these metrics are harder to collect than pulling a keyword ranking report. You have to actually query the AI platforms, evaluate the responses, and track changes over time. That’s not something most existing analytics tools do.

The brands building AI visibility practices now are establishing measurement baselines before their competitors even know what metrics to track. That early position compounds: you learn what moves the needle, you iterate faster, and you show up in the responses that are increasingly driving consideration.

Beket.ai tracks these metrics for you — mention frequency, sentiment, and share of voice across ChatGPT, Gemini, and Perplexity — and surfaces where inaccuracies or gaps are costing you. See your numbers at beket.ai.