Metrics glossary

Every number in CrunchJunkie comes straight from the source it belongs to — Google Ads, GA4, Meta, and the rest report their own metrics, and we surface them unchanged rather than recalculating them behind the scenes. The definitions below explain what each metric means and how it's normally derived, so you can explain any figure to a client with confidence. A quick note on attribution and rounding: platforms count conversions and revenue using their own attribution windows and models, so the same sale can be reported slightly differently by two channels. That's expected, not an error — when you blend channels in one widget, you're adding each platform's own count, not reconciling them into a single source of truth.

Impressions: how many times an ad was shown. Clicks: how many times people clicked it. CTR (click-through rate): clicks divided by impressions, as a percentage — a measure of how compelling the ad is. CPC (cost per click): spend divided by clicks — what you pay, on average, for each visit. Cost (spend): the total amount spent in the period. Conversions: the count of valued actions the platform attributes to your ads (a purchase, lead, sign-up — whatever you've set up to track). CPA (cost per acquisition): spend divided by conversions — what each conversion costs you. ROAS (return on ad spend): conversion value (revenue) divided by spend, usually shown as a ratio such as 4.0× — for every euro spent, four came back. These efficiency metrics are the ones clients care about most, which is why they make good scorecards with a comparison period attached.

Sessions: visits to the site — a single user can have several. Engaged sessions: GA4's count of sessions that lasted longer than ten seconds, had a key event, or had two or more page views — its definition of a visit that genuinely went somewhere. Engagement rate: engaged sessions divided by total sessions, as a percentage; it's GA4's modern replacement for the old "bounce rate" thinking, read the opposite way (higher is better). Key events: GA4's term for the events you've marked as important (formerly "conversions" in older GA). Revenue: the monetary value GA4 records against purchase or other revenue events. Users: the number of distinct people, as opposed to sessions. Reading volume (sessions, users) alongside quality (engagement rate) and outcome (key events, revenue) gives a fuller picture than any single metric on its own.

These come from AI Visibility scans, not ad platforms, and they count EARNED mentions only — never paid placements such as ChatGPT Ads, which are a separate channel and separate spend. Visibility %: how often your brand appears across all tracked prompts and models — your overall earned presence in AI answers. Share of Voice: your slice of total brand mentions versus the competitors you track — who owns the conversation in your category. Sentiment: how positively or negatively a mention describes you, scored across the answers where you appear. Average Position: where your brand tends to land within an answer when it does appear (earlier and more prominent is better). Citation Diversity: across how many of your tracked prompts your own domain is actually cited — breadth, not a raw count a single prompt can dominate — shown as "cited in N of M prompts", so being cited widely reads stronger than being cited once. Every source and citation figure — Retrievals, Citations, Citation Rate, Source Appearances, Citation Diversity, top cited domains, cited URLs, gap domains, content gaps' cited domains, fan-out citation shares, shopping sources, perception's attribute sources, source health and the citation-gap actions — leaves out DeepSeek V4.1 Flash answers, the model DeepSeek has run since 10 September 2026. DeepSeek's current model ignores the web search tool our scan sends, so the URLs in its answers are written from model memory, not found by a search. They are labelled "from model memory, not a search" on the Responses view and in the API, never counted as citations, and left out of the numerator and the denominator alike. Wherever one of these figures is shown — on a screen, in a report widget or chart, in an AI summary's data, or in an API response — a note or a field says how many answers it left out. DeepSeek's brand mentions still count toward Visibility, Share of Voice, Sentiment and Average Position — naming a brand needs no search. Retrievals, Retrieval Rate, Citations and Citation Rate on our own scans count only the answers that show what the engine retrieved and what it cited. Retrievals is the number of answers in which the engine retrieved a page of your domain while searching, and Retrieval Rate divides it by the answers that show what they retrieved. Citations is the number of answers that cited your domain, out of the answers that show what they cited. Citation Rate takes the answers that show both lists and retrieved your domain, and gives the share of them that also cited it. When no such answer retrieved your domain, Citation Rate reads "—", in any month. A KPI card states how many answers its figure is based on (n). An answer without a list for one side is left out of that side's figures, numerator and denominator: answers scanned before 2 July 2026, when the split was first stored; engines that report one side only (Perplexity, Microsoft Copilot and Google's AI Overviews and AI Mode report only what they cited); and Gemini answers from 2 to 9 July 2026, whose sources were stored as Google redirect links that name no domain. A period in which no answer shows a list has no figure: it reads "—" on KPI cards and in tables (Month and Week tables included), in the app, in shared reports and in PDFs; a chart leaves a gap; the AI summary's data says "no data"; the API and the MCP connector return null. It never reads 0. A period in which only some answers show it is computed over those answers alone, and its n says how many. Until 10 October 2026 such a period read 0 on report KPI cards, in Month and Week tables, on charts, in the AI summary's data and in the MCP connector, and the Gemini answers from 2 to 9 July counted as answers that neither retrieved nor cited your domain. A saved AI summary that quoted such a 0 is flagged as "figures changed" once, and the next scheduled send writes it again. Gemini answers scanned before 9 July 2026 stored their sources as Google redirect links (vertexaisearch.cloud.google.com/grounding-api-redirect/…) that name no domain. Such a link is never counted as a domain, a page or a citation, in any figure. An answer whose source list holds only such links is left out of every source and citation figure listed above, numerator and denominator, the way an answer from model memory is, and the MCP connector's get_ai_answer lists none of its sources and, like list_ai_answers, says how many links it left out; its brand mentions still count. An answer with at least one real source keeps its real sources. The Responses view and a follow-up conversation leave these links out of an answer's sources as well and say in one line how many they left out (until 10 October 2026 they listed them). A cited list that holds only such links is read as not known, never as "cited nothing", and the pages the answer retrieved never stand in for it: get_ai_answer then returns citedUrls: null next to the answer's real retrievedUrls. Until 10 October 2026 Source Appearance Rate counted such an answer as one that did not show your domain, and Citation Diversity counted its prompt as one that did not cite you, and the top cited domains, the Source domains, Trending URLs and Cited URLs tables, the MCP connector's citation tools, gap domains, content gaps and fan-out citation shares listed the redirect host as a source. Gemini, read through Google's API, names the website each source came from, never the page: since 9 July 2026 a Gemini answer stores the address of the site (before that, the redirect links above). Every figure counted per page leaves Gemini's answers out, from the count and from its base: the URLs view and its split into your homepage, your other pages and third-party sites, the Cited URLs and Trending URLs tables, get_cited_url and the top pages of get_citations in the MCP connector and the REST API, a page's Cited by AI in the Rank Tracker, and the pages read and cited on the AI bot traffic view. The URLs view, the two tables and the API responses say how many answers they left out. Every figure counted per domain keeps them. Until 10 October 2026 each site Gemini named counted as a citation of that site's homepage, which made homepage counts too high on projects that track Gemini. Gemini chats imported from Peec AI name real pages and count everywhere; a project that tracks Gemini alone has domain figures only. A page table whose answers all come from Gemini says why it has no rows instead of "No data for this period", and get_cited_url and get_report_data return emptyReason "sites_only". In the MCP connector, list_ai_answers and get_ai_answer mark a Gemini answer with sourcesNameSitesOnly: its URLs are websites, never the page it cited. A content brief never analyses a homepage Gemini named as the page the AI cited: Gemini's answers still count for whether the brand is absent and which competitors were named, and when Gemini was a prompt's only source the brief says it has no cited page to learn from. A page key ignores the query string and the fragment, and since 10 October 2026 also a link to a quoted passage (#:~:text=…) that an engine percent-encoded, so one page is one row however many of its passages were cited; the Rank Tracker matches its pages the same way. Under a Locale filter, every source and citation figure and the Source domains, Trending URLs and Cited URLs tables keep the answers scanned before locales were stored, read as the project's locale, as Visibility and the Topic, Tag and Prompt tables already did. Until 10 October 2026 they left those answers out, and the Cited URLs, Owned off-site citations and Follow-up survival tables showed nothing under any Locale filter. Mention gap: not a score but a filter — prompts where a tracked competitor is mentioned and you are not. It's the most actionable lens in the product, because each gap is a specific prompt you could realistically start winning. All of these can be filtered by model, by prompt intent (Discovery/Brand/Competitor), and by tag.

The GEO Audit produces a single 0–100 readiness score that summarises how well a site is positioned to be crawled, parsed and cited by AI engines. It's a weighted blend of five categories: AI crawler access (30), content accessibility / SSR (30), structured data (20), technical SEO hygiene (15) and llms.txt (5). Read it as a positioning measure, not a results measure: a high score means "this site has removed the technical barriers to being cited," whereas whether the brand is actually cited is tracked separately by the Visibility % metric on the AI Visibility tab. Keeping the two apart is deliberate — fixing readiness is necessary but not sufficient, and pairing the two tells the honest story.