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Plain-language definitions of the terms that matter in AI visibility, advertising automation, e-commerce, and AI enablement. Each entry includes practical context, real data, and links to related concepts.
Generative Engine Optimization (GEO) is the practice of optimizing your brand's content and digital presence so AI assistants like ChatGPT, Perplexity, Google Gemini, and Claude can discover, understand, and cite your brand in their responses.
AI brand visibility measures how often and how favorably your brand appears in responses from AI assistants like ChatGPT, Google Gemini, Perplexity, and Claude when users ask questions related to your products or industry.
Answer Engine Optimization (AEO) is the practice of structuring your content to directly answer user questions, making it more likely to be featured in Google's answer boxes, voice search results, and AI-generated summaries.
AI Overviews are AI-generated summaries that appear at the top of Google search results, synthesizing information from multiple web sources to answer user queries directly on the search page.
llms.txt is a plain text file placed at the root of a website that provides structured information about a business to AI language models, similar to how robots.txt communicates with search engine crawlers.
Schema markup for AI refers to the implementation of structured data (typically JSON-LD) on web pages to help both search engines and AI assistants understand, categorize, and cite your content accurately.
AI citation optimization is the practice of structuring your content so AI assistants are more likely to reference and link to your website when answering user questions.
Perplexity optimization is the practice of structuring content to improve visibility in Perplexity AI's search results, which cites sources inline and provides referenced answers to user queries.
E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness) is Google's content quality framework that also influences how AI models evaluate and cite sources in their responses.
Brand entity optimization is the practice of building and reinforcing your brand's identity as a recognized entity across the web, so AI models and search engines consistently associate your brand with specific topics, products, and expertise.
AI search refers to search engines and platforms that use AI models to generate synthesized answers to user queries instead of (or alongside) traditional link-based results, including Google AI Overviews, ChatGPT, Perplexity, and Claude.
Google AI Mode is Google Search's dedicated conversational AI interface, launched in late 2025, that lets users ask follow-up questions and get in-depth AI-generated answers with cited sources, separate from the standard AI Overviews that appear in regular search results.
Zero-click searches are search queries where the user gets their answer directly on the search results page (through featured snippets, knowledge panels, or AI Overviews) without clicking through to any website.
A knowledge graph is a structured database of entities (people, places, brands, concepts) and the relationships between them, used by Google and AI models to understand real-world facts and deliver accurate, context-aware answers.
Topical authority is the degree to which a website or brand is recognized as a credible, in-depth source on a specific subject area, influencing both traditional search rankings and AI citation likelihood.
AI share of voice measures how frequently and prominently your brand appears when AI platforms (ChatGPT, Perplexity, Gemini, Copilot) respond to queries in your category — the AI equivalent of traditional search engine market share.
AI-ready product data is structured, machine-readable product information optimized for consumption by AI agents and language models — including complete schema markup, standardized attributes, and consistent data across all retail and brand touchpoints.
AI-powered advertising uses machine learning algorithms to automate and optimize ad campaign management, including audience targeting, bid optimization, creative testing, and budget allocation across platforms like Meta, Google, TikTok, and Amazon.
Programmatic advertising is the automated buying and selling of digital ad placements using software and algorithms, replacing manual negotiations with real-time bidding across display, video, and native ad formats.
ROAS (Return on Ad Spend) measures the revenue generated for every dollar spent on advertising, calculated as (revenue from ads / ad spend). A ROAS of 4x means you earned $4 for every $1 spent.
Amazon DSP (Demand-Side Platform) is Amazon's programmatic advertising platform that lets brands reach audiences both on and off Amazon properties using Amazon's first-party shopping data for targeting.
Meta Advantage+ is Meta's AI-powered advertising suite that automates campaign creation, audience targeting, creative selection, and placement optimization across Facebook and Instagram.
Google Performance Max is an AI-driven campaign type that runs ads across all Google properties (Search, Display, YouTube, Gmail, Maps, Discover) from a single campaign using machine learning to optimize performance.
Retail media networks are advertising platforms operated by retailers (Amazon, Walmart, Target, Instacart) that let brands target shoppers using the retailer's first-party purchase data.
ChatGPT Ads are sponsored placements that appear within OpenAI's ChatGPT responses, currently in limited beta testing with enterprise brands at a reported $200,000 minimum spend commitment.
Creative testing is the systematic process of running multiple ad variations across platforms like Meta, Google, and TikTok to identify which visual and copy combinations drive the best performance.
Attribution modeling is the method used to assign credit for a conversion to the marketing touchpoints that influenced it, from first ad impression through final purchase click.
Lookalike audiences are ad targeting segments created by platforms like Meta, Google, and Amazon that find new users who share behavioral and demographic patterns with your existing customers.
Amazon Sponsored Products are pay-per-click ads that appear in Amazon search results and product detail pages, promoting individual product listings to shoppers actively searching for related items.
Amazon Performance+ is Amazon's AI-powered automated bidding and audience targeting system for DSP campaigns, using machine learning to optimize campaign performance with minimal manual targeting — Amazon's answer to Meta's Advantage+ and Google's Performance Max.
Non-endemic advertising is when brands that do not sell products on a retail media platform (like Amazon, Walmart, or Instacart) use that platform's ad inventory and audience data to reach consumers. An insurance company running Amazon DSP ads is a non-endemic advertiser.
Retail media mix is the strategic allocation of advertising budget across retail media networks (Amazon, Walmart, Target, Instacart) and traditional digital channels (Google, Meta, TikTok) to maximize total return. It answers the question: how should you split your ad dollars across platforms?
AI-powered bid optimization uses machine learning algorithms to automatically set and adjust advertising bids in real-time across platforms like Amazon, Google, and Meta, aiming to maximize conversions, ROAS, or other KPIs within a given budget.
Amazon ads automation is the use of rules, scripts, APIs, and AI tools to manage Amazon advertising campaigns programmatically, covering bid adjustments, keyword harvesting, budget allocation, dayparting, and performance reporting without manual intervention.
Multi-touch attribution (MTA) is a measurement methodology that assigns credit for a conversion across multiple marketing touchpoints in a customer's journey, rather than giving 100% credit to the first or last interaction. It answers: which combination of ads, emails, and content drove the sale?
Digital shelf optimization is the process of improving how your products appear across online retail platforms (Amazon, Walmart, Target) to increase visibility, conversion rates, and sales.
Amazon ACoS (Advertising Cost of Sale) is the ratio of ad spend to ad revenue on Amazon, calculated as (ad spend / ad revenue) x 100. A lower ACoS means higher advertising efficiency.
Product feed optimization is the process of improving the quality and completeness of your product data feeds sent to advertising platforms and marketplaces to increase ad performance and organic visibility.
Amazon A+ Content (formerly Enhanced Brand Content) lets brand-registered sellers add rich media, comparison charts, and branded storytelling to their product detail pages to increase conversion rates.
Conversion rate optimization (CRO) is the systematic process of increasing the percentage of website visitors who take a desired action, whether that's making a purchase, filling out a form, or booking a call.
TACoS (Total Advertising Cost of Sale) measures advertising spend as a percentage of total revenue (not just ad-attributed revenue), giving a clearer picture of advertising efficiency relative to overall business performance on Amazon.
The Amazon Buy Box is the prominent "Add to Cart" and "Buy Now" section on a product detail page. When multiple sellers offer the same product, Amazon's algorithm selects one seller to "win" the Buy Box, capturing 80-90% of that listing's sales.
Customer acquisition cost (CAC) is the total cost of acquiring a new customer, calculated by dividing total sales and marketing spend by the number of new customers acquired in a given period.
Agentic commerce is the emerging model of e-commerce where autonomous AI agents research, evaluate, and purchase products on behalf of consumers, shifting the buying decision from human browsing to AI-driven recommendation and transaction.
The invisible shelf is the product evaluation layer that exists entirely within AI systems — where AI shopping agents compare, rank, and select products using structured data, brand signals, and algorithmic criteria that are invisible to traditional analytics tools.
Contribution margin is the profitability metric that shows how much revenue remains after deducting variable costs at each level — CM1 (gross margin), CM2 (after advertising), CM3 (after all variable costs) — used as the primary decision framework for Amazon and retail media investment.
Agent-to-agent commerce (A2A) is the emerging model where autonomous AI agents transact directly with other AI agents — negotiating, purchasing, and fulfilling orders without human involvement at each step.
AI enablement is the process of helping organizations adopt AI tools and workflows across their operations, from initial readiness assessments and tool selection through implementation, training, and ongoing optimization.
AI agents in commerce are autonomous AI systems that can research products, compare options, and make purchasing decisions on behalf of consumers, representing a shift from human-driven to agent-driven buying behavior.
AI content detection refers to tools and techniques used to determine whether text was written by an AI model or a human, relevant for brands creating content at scale who need to maintain authenticity.
AI workflow automation uses artificial intelligence to automate multi-step business processes, from data extraction and decision-making to content generation and customer communication, reducing manual work across marketing and operations.
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