Marketing glossary.

Google Search's AI-generated summary that appears above traditional search results. Pulls from pages already ranking in positions 1-5 and favors content with Featured Snippet eligibility. Now consumes around 60% of clicks on high-intent queries.

A 2-3 sentence self-contained paragraph in the first 200 words of a page that directly answers the title's question. The most likely passage to be quoted by AI engines and Google's Featured Snippets. Must be under 70 words and name the entity explicitly.

Related: GEO · AEO · Featured Snippet

Optimizing content for direct extraction by AI answer engines and voice search. Focuses on question-form headings, atomic facts, FAQ schema, and concise definitions in the first 200 words. Sister discipline to GEO.

Related: GEO · Answer Capsule · FAQPage Schema

A single-idea sentence that makes complete sense without surrounding context. AI engines preferentially quote atomic facts because they can be lifted into an answer without follow-up explanation. Statistics, thresholds, and timelines are the highest-value atomic-fact patterns.

Content elements specifically designed to be quoted by AI engines. The four highest-ROI patterns are original data tables, named thresholds, before/after examples, and process timelines. Each article should include at least one.

Anthropic's web crawler for Claude. Identified by User-Agent ClaudeBot/1.0 and anthropic-ai. Allowing it in robots.txt is required to be cited in Claude-generated answers.

The strength of an organization's presence across structured data sources (Wikidata, Crunchbase, LinkedIn, schema.org Organization). AI engines weight this heavily before deciding to cite a brand. Brands present on 4+ authoritative platforms are roughly 2.8× more likely to appear in AI answers.

Related: sameAs · Wikidata · Knowledge Graph

JSON-LD structured data that marks a page's FAQ section as machine-readable Question/Answer pairs. Generates accordion-style rich results in Google and is one of the highest-leverage AEO patterns. Pages with FAQPage schema get cited in Perplexity around 40% more often.

Related: JSON-LD · Schema.org · Rich Results

The practice of engineering content, schema, and entity authority so a brand is cited inside generative AI answers. Targets ChatGPT, Gemini, Perplexity, Claude, and Microsoft Copilot. Built on entity disambiguation, structured data, citation-bait content, and llms.txt.

Related: AEO · Answer Capsule · Entity Authority · FAQPage Schema

Google's User-Agent for AI training (Gemini, Vertex AI). Separate from Googlebot. You can allow Googlebot for Search ranking but block Google-Extended to keep your content out of training data. though that also limits Gemini citation.

OpenAI's web crawler used to populate ChatGPT's training data and live citations. Identified by the User-Agent string GPTBot/1.0. Sites that block it via robots.txt are excluded from ChatGPT-generated answers.

Related: ClaudeBot · PerplexityBot · robots.txt

Google's database of entities (people, companies, places) and the relationships between them. AI engines like Gemini ground their answers against the Knowledge Graph before crawling fresh content. Strong sameAs links and Wikidata entries feed the Knowledge Graph.

A plain-text file at the root of a domain (/llms.txt) that describes the site's purpose, key pages, and entity definitions for AI crawlers. The AI equivalent of robots.txt. early adoption signals organizational maturity to crawlers.

Microsoft's AI assistant, integrated into Bing and Edge. Uses the Bing index for web citations. Sites must be indexed in Bing Webmaster Tools to be eligible for Copilot citations. Bing indexing is separate from Google indexing.

Google's ability to rank individual H2 sections of a page independently of the page as a whole. Means each section needs its own atomic fact, named entity, and standalone-readable structure to compete for queries.

Perplexity AI's web crawler. Two variants: PerplexityBot (training) and Perplexity-User (live citation lookup). Citation rates correlate strongly with named-author bylines, visible publish dates, and high domain authority.

The single authoritative URL for a piece of content, declared via <link rel="canonical">. Tells search engines which version to index when multiple URLs serve the same or similar content. Prevents duplicate content penalties and consolidates ranking signals.

Google's three performance metrics: LCP (Largest Contentful Paint), INP (Interaction to Next Paint), CLS (Cumulative Layout Shift). Direct ranking factor. Targets: LCP under 2.5s, INP under 200ms, CLS under 0.1.

Google's quality framework: Experience, Expertise, Authoritativeness, Trust. Used by quality raters and the algorithm to evaluate content. Real engagement examples, named author credentials, sourced statistics, and authoritative inbound links all signal E-E-A-T.

A direct-answer box at the top of Google search results, pulled from one ranked page. Eligibility requires answering the query in 40-60 words within the first H2 section. Featured-snippet pages are the primary candidates for AI Overviews.

An HTML attribute that tells search engines which language and regional variant of a page to show users in different countries. Specified as <link rel="alternate" hreflang="en-za" href="...">. Critical for sites with country-specific or city-specific content.

JavaScript Object Notation for Linked Data. the recommended format for embedding schema.org structured data in HTML. Lives in a <script type="application/ld+json"> tag and is parsed by search engines and AI crawlers but invisible to users.

A plain-text file at the domain root that tells crawlers which paths they can access. Can target specific User-Agent strings (Googlebot, GPTBot, ClaudeBot). Blocking AI crawlers here removes the site from AI-generated answers.

A shared vocabulary for structured data on the web, jointly maintained by Google, Microsoft, Yahoo, and Yandex. Implemented via JSON-LD blocks. The most cited types for marketing sites: Organization, Service, FAQPage, Article, BreadcrumbList, LocalBusiness.

The practice of structuring a site and its content to rank in traditional search engine results pages. Built on three pillars: technical (crawlability, speed, schema), on-page (titles, headings, internal links), and off-page (backlinks, citations).

An XML file that lists all public URLs on a site for search engine discovery. Should include lastmod timestamps, changefreq, and priority. Submit to Google Search Console and Bing Webmaster Tools after major content changes.

A content architecture where one comprehensive pillar page (2,500+ words) links to many supporting articles, and each supporting article links back to the pillar. Builds topical authority and signals depth to both Google and AI engines.

Customer Acquisition Cost. total marketing and sales cost divided by new customers acquired in the period. The denominator question is whether to include team salaries, software, and overhead. be consistent in how you define it.

Google's framework for collecting analytics data while respecting GDPR consent choices. Requires explicit user consent before sharing personal data with Google. Sites without Consent Mode v2 see significant tracking degradation in EU traffic.

Server-to-server tracking that sends conversion events directly from your server to ad platforms (Meta CAPI, LinkedIn CAPI, Google Enhanced Conversions). Bypasses iOS 14, Safari ITP, and ad blockers that broke client-side pixels. Required for accurate attribution in 2026.

Related: Server-Side Tracking · Consent Mode v2

Cost Per Lead. ad spend divided by leads generated. Useful at the channel level but only meaningful when tied to lead quality. CPL with terrible MQL→SQL conversion is just a vanity metric.

Cost Per Mille (cost per 1,000 impressions). The auction unit on Meta, LinkedIn, and most paid social. Useful for benchmark comparison but the bottom-funnel metric (CPA, ROAS) is what you optimize against.

Meta's 1-10 rating of how well your CAPI data matches Facebook user accounts. Higher = better attribution. Score above 6 is the working minimum; below that, the optimization signal degrades. Requires hashed email, phone, and other PII fields.

Google Ads' AI-driven campaign type that auto-allocates budget across Search, Shopping, YouTube, Display, Discover, and Gmail. Best for e-commerce with strong shopping feed and conversion data. Risk: spend can drift to low-quality placements without proper exclusions.

Return On Ad Spend. revenue divided by ad spend over a defined attribution window. The top-line efficiency metric for performance marketing. Defended ROAS requires accurate revenue attribution, which is why server-side tracking matters.

Running Google Tag Manager on a server (Google Cloud Run, Stape) instead of in the browser. The server sees first-party data and forwards events to ad platforms via CAPI. Restores ROAS visibility lost to browser privacy changes.

Using an LLM to score inbound replies or form submissions for fit and intent before routing to a human. Output is structured JSON: fit (1-10), intent (1-10), summary. Lifts MQL→SQL conversion when paired with real-time enrichment.

Automated assignment of new leads to the right sales rep based on territory, company size, ICP fit, or product interest. Sub-2-minute routing dramatically improves contact rate compared to manual queue assignment.

Visual workflow automation (formerly Integromat). Real-time triggers, data transforms, and API connectors. Best for marketing operations, lead routing, and data sync. SaaS pricing model (per-operation) makes it pricier than n8n at scale.

Open-source workflow automation platform with 400+ integrations. Self-hostable. Native AI nodes for OpenAI and Anthropic. Our primary engine for complex multi-step automations because it scales without per-execution pricing.

Quick-deploy automation across 5,000+ apps. Best for simple trigger-action workflows. Multi-step Zaps and conditional Paths handle moderate complexity. Pricing scales with task count, so Zapier is the right choice for low-volume orchestration.

B2B prospecting database covering ~280M contacts with verified emails, phone numbers, and firmographic data. Used for ICP list-building and enrichment. The depth advantage over alternatives shows up in mid-market and enterprise tiers.

AI-powered enrichment platform that combines 50+ data sources with natural-language transformations. Best for building hyper-targeted lists and personalized outbound sequences. Premium pricing reflects depth of coverage.

Gradually ramping up sending volume on a new domain to build sender reputation with email providers. Typically 4-6 weeks from 0 to full volume. Required before any cold outreach to avoid landing in spam.

Ideal Customer Profile. the company shape that's most likely to buy and most profitable to serve. Defined by industry, company size, tech stack, geography, and trigger events. Sharper ICP definition is the single biggest lift for outbound conversion.

Sending cold email from multiple email addresses across multiple domains in parallel to distribute volume and reduce per-account flagging risk. Standard practice for outreach at scale.

Signals that indicate a company is actively researching solutions in your category. Sources: G2 visits, content downloads, technographic changes, hiring patterns. Intent + ICP fit is the qualifier for cold outreach.

Three DNS records that authenticate your sending domain. SPF lists allowed senders, DKIM signs each email cryptographically, DMARC tells receivers what to do with failures. All three must be configured for B2B email to land in inbox.

Splitting traffic between two or more page variants to measure which converts better. Statistical significance requires sample size based on baseline conversion rate and minimum detectable lift. usually 1,000+ conversions per variant.

The discipline of increasing the percentage of visitors who take a desired action. Built on hypothesis-driven A/B testing, heatmaps, session recordings, and funnel analysis. Compound CRO improvements often beat new traffic acquisition for ROI.

A visualization of where users click, hover, and scroll on a page. Tools: Hotjar, Microsoft Clarity, FullStory. Best for diagnosing UX issues and identifying ignored CTAs or dead zones below the fold.

The principle that a landing page headline should mirror the ad copy or CTA that brought the user there. Mismatch causes immediate bounce. The single highest-leverage fix on most paid traffic landing pages.

Playback of an individual user's session. every click, scroll, and keystroke (with PII redacted). Reveals friction points that quantitative metrics miss: confused form fields, broken interactions, abandonment patterns.

A primary call-to-action that follows the user as they scroll, typically a fixed bottom bar on mobile or a sticky sidebar on desktop. Increases bottom-funnel conversion by keeping the action one click away regardless of scroll depth.

The time period during which a touchpoint can be credited with a conversion. Common windows: 1-day click, 7-day click, 28-day click. Different windows produce dramatically different ROAS numbers. be consistent across reports.

Data collected directly by your own systems (CRM, server-side analytics, email opens). Not subject to third-party cookie restrictions. Increasingly the only reliable signal as iOS, Safari, and Chrome restrict cross-site tracking.

Google Analytics 4. the event-based successor to Universal Analytics. Tracks user journeys across web and app, integrates with Google Ads, and supports server-side data via Measurement Protocol. Required for modern attribution in Google's ad ecosystem.

Google's tag management system. Lets non-developers deploy and update tracking pixels (GA4, Meta, LinkedIn, etc.) via a web UI without site code changes. Server-side GTM containers run the same logic on a server for first-party data.

Google Cloud's enterprise BI tool. Connects to BigQuery, GA4, and most data warehouses. Used for the operator dashboard layer that sits above raw analytics tools. Heavier setup than Mixpanel or Amplitude but more flexible at scale.

Event-based product analytics. Best for SaaS funnel analysis, retention curves, and feature adoption metrics. Pairs with GA4 for marketing-side analytics. they answer different questions.

Distributing conversion credit across multiple touchpoints in the customer journey, rather than crediting only the last click. Common models: linear, time-decay, position-based. Requires unified data from CRM, ads platforms, and analytics.

Tying marketing channel investment to opportunities generated and revenue closed downstream. Requires CRM integration, opportunity-source tagging, and a clean lead-to-opportunity funnel definition. The metric every CFO actually wants to see.