Glossary

AI agents

AI agents are software programs that pursue a goal by taking a sequence of actions - searching, retrieving information, comparing options, and in some cases completing transactions - without requiring a human to approve each step.

AI concierge (dealership)

An AI concierge for a dealership is an on-site digital assistant that answers questions about vehicles and dealership services, helps shoppers narrow inventory choices, and connects them to the next step - whether that is booking a test drive, scheduling service, or reaching a sales advisor.

AI customer support

AI customer support uses artificial intelligence to answer common customer questions automatically - such as shipping status, return policies, and product details - by drawing on a business's own connected knowledge sources.

AI shopping assistant

An AI shopping assistant is a conversational tool embedded on a retail website that helps shoppers find products, compare options, and get questions answered in natural language - much like a knowledgeable in-store associate available at any hour.

AI site search

AI site search uses machine learning to interpret queries by meaning and rank results, rather than relying only on exact keyword matches. It understands intent, synonyms, and context so shoppers or visitors find relevant results even when their wording differs from the product catalog.

Answer engine optimization (AEO)

Answer engine optimization (AEO) is the practice of structuring web content so AI-powered answer engines - such as ChatGPT, Google AI Overviews, and Perplexity - can extract, cite, and accurately represent it in response to user questions.

Average order value (AOV)

Average order value (AOV) is the average amount a customer spends each time they place an order, calculated by dividing total revenue by the number of orders in a given period. It is one of the three core levers on ecommerce revenue, alongside conversion rate and traffic volume.

Business development center (BDC)

A business development center (BDC) is a dedicated dealership team or department responsible for handling inbound inquiries, following up with prospects, and scheduling appointments to convert interest into showroom visits.

Conversational commerce

Conversational commerce is a shopping experience where customers interact with a retailer through natural dialogue - asking questions, getting recommendations, and completing purchases without navigating menus or filters alone.

Conversion rate

Conversion rate is the percentage of website visitors who complete a desired action - most often a purchase - and serves as the headline metric for measuring ecommerce and dealership site performance.

Dealership inventory search

Dealership inventory search is how shoppers find vehicles on a dealer website - traditionally through make/model/year/trim dropdowns, and increasingly through natural-language queries like "used SUV with third row under 30k miles."

Faceted search

Faceted search lets shoppers narrow a product catalog by attributes such as size, color, price, and brand, using filters derived directly from product metadata. Each attribute becomes a selectable dimension that progressively reduces the visible result set.

First-party customer data

First-party customer data is information a business collects directly from its own customers - with their knowledge and consent - through owned channels such as its website, app, or in-store interactions.

Generative AI

Generative AI is a class of artificial intelligence that produces new content - text, images, audio, or code - in response to a prompt, rather than simply classifying existing data or returning a fixed lookup result.

Generative engine optimization (GEO)

Generative engine optimization (GEO) is the practice of structuring and presenting content so that AI-powered answer engines - such as ChatGPT, Perplexity, and Google AI Overviews - surface and cite it when responding to user queries. It is closely related to answer engine optimization (AEO) and extends traditional SEO practices into the era of AI-generated responses.

Hybrid search

Hybrid search combines keyword (lexical) matching with semantic (vector) matching so results capture both exact terms and intended meaning. It blends two retrieval methods into a single ranked result set.

Keyword search

Keyword search matches the literal words a shopper types against the words in your product catalog - titles, descriptions, tags, and other indexed fields. If the words match, the product surfaces; if they don't, it doesn't.

Knowledge base

A knowledge base is a structured collection of a business's content - help articles, policies, FAQs, and product information - that support and search systems draw answers from. It serves as the authoritative source a business maintains so customers and staff can find accurate answers quickly.

Large language model (LLM)

A large language model (LLM) is an AI system trained on massive amounts of text data that can understand, reason about, and generate human-like language. LLMs power a wide range of applications, from search assistants to content tools, by predicting and producing contextually relevant text.

Natural language search

Natural language search lets people find information by typing or speaking in everyday phrases and questions, rather than carefully chosen keywords, and still receive relevant results.

Personalization

Personalization tailors the shopping experience - results, recommendations, and content - to an individual shopper's context, preferences, and behavior rather than serving every visitor the same generic experience.

Product discovery

Product discovery is the process by which shoppers find relevant products - through search, browsing, recommendations, and guided experiences - particularly when they don't arrive with a specific item already in mind.

Product Q&A

Product Q&A answers shopper questions about a specific product - sizing, materials, compatibility, care instructions - directly on the product detail page, without the shopper having to contact support or leave the page.

Product recommendations

Product recommendations suggest items a shopper is likely to want, based on what they are viewing, their browsing behavior, or patterns from shoppers with similar interests.

Query understanding

Query understanding is how a search system interprets a shopper's input - identifying intent, product attributes, and constraints - before deciding which results to retrieve.

Retrieval-augmented generation (RAG)

Retrieval-augmented generation (RAG) retrieves relevant facts from a trusted knowledge source and feeds them to a language model at answer time, so responses are grounded in real, current data rather than the model's training alone. It combines the fluency of a language model with the accuracy of a live knowledge base.