AI Ecommerce Glossary

About 70% of shoppers fill a cart and leave without buying. Abandoned cart recovery is the practice of re-engaging them before the sale is gone — traditionally via email sequences, now increasingly via real-time AI chat intervention.

The old approach: a three-email sequence starting an hour after abandonment, maybe 3% effective. The AI approach: detecting exit intent while the shopper is still on-site and opening a conversation right then — asking what held them back, answering an objection, or surfacing the size variant they couldn't find.

Timing matters more than most stores realise. An intervention 30 seconds before someone leaves converts at a significantly higher rate than the same message sent as an email 24 hours later. The shopper's intent is still warm. An hour later, they've moved on.

The version of online shopping where AI acts on a customer's behalf — researching products, comparing prices, reading reviews, and placing orders — with varying degrees of human supervision.

It sounds like science fiction. It isn't. Salesforce, Deloitte, and Stripe all published major reports on agentic commerce in 2025. Perplexity's Shopping Assistant, Google's Project Mariner, and early Shopify pilot programs are live examples. The shift is already underway.

The implication for store owners is practical: an AI agent browsing your store isn't going to respond to lifestyle photography and flash banners. It needs clean product specs, accurate inventory signals, and well-structured policy data. How your product catalogue is formatted today determines whether AI shoppers find and buy your products next year.

A step above a standard AI chatbot — an AI Concierge handles personalised, high-touch shopping guidance the way a knowledgeable salesperson on a shop floor would. It asks questions, learns preferences, and makes considered recommendations rather than just responding to what's directly asked.

The difference shows up in edge cases. Ask a basic chatbot "what's good for combination skin under $40?" and you'll get a list of products that match those keywords. A concierge asks a follow-up: "Do you have any sensitivities? Day or night use?" Then it narrows to one or two genuinely right options.

The best implementations remember context across a session and, where a customer consents, across repeat visits.

When an AI generates a confident, fluent answer that is factually wrong. In general use, this is annoying. In ecommerce, it's a commercial and legal liability.

An AI that hallucinates might tell a customer your return window is 60 days when it's 30, confirm a product is in stock when it isn't, or recommend a supplement as suitable for people with a specific condition — incorrectly. These aren't hypothetical failures. They've happened, publicly, to recognisable brands.

An AI system that goes beyond answering FAQs to actively driving sales — handling product discovery, objection handling, personalised recommendations, upselling, and assisted checkout, autonomously, at any hour.

The distinction from a standard chatbot is important. A chatbot responds. A sales agent initiates, persists, and steers toward a commercial outcome. A chatbot tells you the return policy. A sales agent notices you've been comparing two jacket sizes for six minutes and asks which one you're on the fence about — then closes the sale.

Most tools marketed as "AI Sales Agents" are chatbots with a CTA button. An actual AI Sales Agent is trained on your specific product catalogue, understands your brand voice, tracks shopper behaviour in real time, and converts that behaviour into revenue — measurably.

The average amount a customer spends per transaction. Simple to calculate: total revenue divided by total orders. Not simple to move.

AOV matters because customer acquisition is expensive. Every dollar you increase AOV is pure margin improvement without additional spend to get someone to the store. A customer who spends $85 instead of $60 is a 42% revenue lift from the same traffic.

AI moves AOV through real-time upselling and cross-selling — not with generic "customers also bought" carousels, but with contextually relevant suggestions surfaced at the moment the shopper is most receptive. The conversion gap between a generic product recommendation and a well-timed contextual one is substantial.

The real-time signals a shopper sends through their on-site behaviour — and the logic an AI uses to decide when and how to respond to them.

Examples: pausing on a product page for over 90 seconds (likely interest or confusion), switching between two similar products (indecision), viewing the returns policy before adding to cart (purchase hesitation), reaching checkout and stopping (friction). Each of these signals something specific about where the shopper is in their decision.

The value isn't in the signals themselves — it's in what the AI does with them. A shopper stuck on sizing needs a different message than one who's hesitating on shipping cost. The more granular the trigger logic, the more relevant the intervention, and the better the outcome.

The percentage of website visitors who complete a purchase. If 100 people visit your store and 2 buy, your conversion rate is 2%.

The ecommerce average sits around 2–3%. Top-quartile stores run at 4–5%. That gap looks small in percentage terms and is enormous in revenue terms — it represents thousands of additional sales from exactly the same traffic volume.

Conversion rate is the lever most stores under-invest in relative to traffic acquisition. A 25% increase in traffic grows revenue 25%. A 25% improvement in conversion rate grows revenue 25% — but the second approach is almost always cheaper than the first. Buying more ads doesn't improve conversion rate. Better product pages, faster checkout, and real-time shopping assistance do.

Selling through conversation — using chat, messaging, and AI to guide shoppers through product discovery and purchase rather than leaving them to navigate a catalogue alone.

The term was coined by Uber product lead Chris Messina in 2015, in the context of messaging apps. In ecommerce today, the most relevant form is AI-powered chat on your store's website — a system that answers questions, surfaces products, handles objections, and closes sales through natural dialogue.

The business case is solid: shoppers who engage in a chat session convert at higher rates and spend more than those who don't. The challenge is making the conversation feel like genuine help rather than like filling out a form with extra steps. That gap — between a scripted FAQ bot and a real conversational experience — is where most implementations either win or lose.

The total revenue a single customer generates across all their purchases, for as long as they remain your customer. Sometimes written as LTV (lifetime value).

CLV matters for two distinct reasons. First, it tells you how much you can afford to spend acquiring a customer — someone worth $800 over three years justifies a much higher acquisition cost than someone who buys once for $40 and never returns. Second, it's the metric that post-purchase AI personalisation moves most directly.

Most stores optimise for the first purchase. CLV-focused stores design every subsequent touchpoint — the follow-up chat, the next recommendation, the loyalty message six months later — to extend the relationship. AI makes this kind of personalised retention economically viable at scale, without requiring a customer service team that grows in proportion to the customer base.

A behavioural signal detected when a shopper is about to leave a page — typically through mouse movement toward the browser tab or address bar, a sudden scroll to the top, or idle time crossing a threshold.

Exit intent is valuable because the moment just before someone leaves is often the highest-leverage intervention point. They've already invested time in your store. Something stopped them from converting. A well-timed, relevant message at that moment converts at meaningful rates.

What stores usually get wrong: a generic 10%-off popup triggered on exit intent performs poorly because it ignores what the shopper was actually doing. "Still thinking about the Merino pullover in size M?" performs better — because it signals that the store noticed. Relevance is the variable that separates a useful intervention from an irritating one.

Behavioural and transactional data collected directly from your own customers on your own properties — pages viewed, products clicked, purchase history, time spent, search queries.

First-party data matters more than it used to because the tracking infrastructure that let brands follow shoppers around the internet for years is being dismantled. Browser privacy changes, iOS updates, and GDPR enforcement have steadily eroded third-party cookies. Brands that built their targeting strategy on borrowed data are feeling it.

What replaces it is data you own and control. AI systems use first-party data to personalise recommendations in real time, predict which customers are at risk of churning, and trigger the right message at the right moment — without needing to reach outside your own ecosystem. The richer your first-party data, the more precise the personalisation.

AI that creates new content — text, images, code, product descriptions, chat responses — rather than simply retrieving or classifying existing information.

GPT-4, Claude, Gemini, and Llama are all generative AI models. In ecommerce, generative AI powers: chat responses written fresh for each individual shopper's specific question; product description generation at scale; dynamic email copy personalised to individual customers; and image generation for product variations or marketing creative.

The distinction that matters operationally: a rule-based chatbot retrieves pre-written answers from a script. A generative AI chatbot writes a response for the specific question being asked, in the moment. The second approach handles edge cases the first can't. But it also requires solid grounding in accurate, up-to-date store data to avoid hallucinations.

The AI's ability to understand what a shopper actually wants — the intent behind their message — rather than just the literal words they typed.

This matters because people don't talk to search boxes the way they talk to each other. "Something for date night under $100" is a clear shopping intent with an occasion, a budget, and a purchase goal. Keyword search can't handle it. A well-trained intent recognition system can: it identifies the context, the category, the constraint, and the intent — then surfaces relevant options.

Intent recognition also distinguishes between messages that look similar on the surface but mean different things. "How long does shipping take?" from a first-time visitor is a pre-purchase question. The same message from someone who ordered three days ago is a support request. The right response is completely different — and a system without good intent recognition will get this wrong frequently.

The type of AI model behind most modern chatbots and AI assistants — trained on vast amounts of text to understand language, generate human-like responses, and reason through complex questions.

GPT-4 (OpenAI), Claude (Anthropic), Gemini (Google), and Llama (Meta) are the major LLMs. Most AI products in ecommerce are built on top of one of these foundations — the vendor's product sits above, adding domain-specific logic, product data grounding, and guardrails.

The branch of AI that allows computers to understand, interpret, and respond to human language — not just keyword matching, but actual comprehension of meaning, context, and nuance.

NLP is why a modern AI can handle "do you have those boots in a darker colour and half a size bigger?" and give a useful answer. Without it, that question fails because no keyword matches "darker colour" to a product attribute or "half a size bigger" to an inventory filter.

NLP has existed for decades. What changed recently is accuracy and scale. Modern NLP handles context across multiple messages in a conversation, sarcasm, ambiguous phrasing, and the difference between "I love this product" said sincerely and said ironically. That quality gap is what separates a genuinely useful shopping assistant from one that frustrates every third customer.

Selling across multiple channels — website, mobile, social media, marketplaces, physical store — in a way that feels consistent and connected to the customer, regardless of where they engage.

The operative word is connected. Multichannel means selling in multiple places. Omnichannel means those channels share data, so the customer experience doesn't fragment across them. A customer who adds to cart on mobile and completes checkout on desktop doesn't start over. A customer who asks a question in Instagram DMs gets the same service quality as one chatting on your website.

AI is increasingly the connective tissue in omnichannel setups — maintaining customer context across touchpoints, routing inquiries appropriately, and personalising messages based on the full cross-channel picture rather than a partial one.

AI-generated product suggestions tailored to each shopper based on their browsing behaviour, purchase history, stated preferences, and real-time signals.

The benchmark everyone cites: 35% of Amazon's revenue comes from their recommendation engine. That figure is quoted so often it becomes background noise — but it represents a real phenomenon. Surfacing the right product to the right person at the right moment converts substantially better than showing everyone the same carousel.

The quality gap between implementations is large. "Customers who bought X also bought Y" is blunt and frequently irrelevant — you've already seen it be wrong. Recommendations that account for what someone has already purchased, what price range they've been browsing, and what they're looking at right now are meaningfully more useful. And they convert at meaningfully higher rates.

The process by which shoppers find products they want to buy — and how AI changes that process from a keyword search into a guided conversation.

Traditional product discovery relies on the shopper knowing what they're looking for and being able to describe it in terms that match your product taxonomy. This works for someone searching "black running shoes size 10." It fails for gift-buyers ("something for my dad who likes cooking"), shoppers with an aesthetic preference they can't describe in keywords ("minimal, earthy tones, nothing too trendy"), and anyone who doesn't know the technical name for what they need.

AI-powered product discovery handles natural language queries, guided "tell me about yourself" flows, and follow-up clarifying questions — routes that keyword search simply can't serve. For stores with large or complex catalogues, the impact on conversion is especially significant.

An AI system that initiates actions — starting conversations, surfacing recommendations, sending messages — based on predicted intent, rather than waiting passively for a user to do something first.

Most chatbots are reactive: they sit on the page until someone clicks the chat bubble. Proactive AI watches what shoppers are doing and decides when to step in, based on a combination of behavioural signals and predicted intent models.

The value of proactive versus reactive engagement is well-documented — proactive engagement rates run 3–5x higher than passive ones. But there's a quality threshold. A popup that fires five seconds after anyone lands on a page isn't proactive intelligence — it's just an annoying popup with extra steps. The trigger logic is what separates useful proactivity from irritating interruption.

A chat interaction initiated by the AI — based on real-time shopper behaviour — rather than opened by the customer clicking a chat button.

Proactive chat is one of the highest-ROI applications of AI in ecommerce. A visitor who's spent two minutes on a product page and scrolled back to the top has an unanswered question. A proactive message — "Trying to decide between sizes? I can help." — catches them at exactly the right moment.

Timing and relevance determine whether this feels helpful or invasive. Proactive chat that fires on page load with "Hi there! How can I help today?" performs poorly — it's a generic interruption. Proactive chat triggered by specific signals, with a message relevant to what the shopper is actually looking at, performs substantially better. The content of the trigger matters as much as the fact of it.

A technique that connects a language model to a specific knowledge base — your product catalogue, return policy, shipping information, FAQs — so the AI gives accurate, grounded answers instead of generating responses from general training data alone.

Without RAG, an AI answers questions based on what it learned during training. That's fine for general knowledge questions. It's a problem when someone asks about your specific return window, or whether a particular product comes in a specific variant, or what your current shipping times are. Without RAG, the model either says it doesn't know or — worse — fabricates a plausible-sounding answer.

With RAG, the model retrieves the relevant, current information from your own data before generating a response. The answer is grounded in reality. This is the core technical difference between an AI that's safe to deploy on your store and one that will create customer service problems within a week.

The percentage of incoming customer support requests resolved automatically by AI — without a human agent getting involved.

Most support queues are dominated by a small set of repeating questions: order status, return policy, shipping estimates, size charts. These questions have correct answers. An AI that's properly grounded in your store's data handles them accurately, at any hour, in seconds. The ticket deflection rate tells you what fraction of that volume you've automated.

For context: Tidio reports a 67% deflection rate for their Lyro AI. Gorgias customers report 60%+. These aren't edge cases for perfectly configured large operations — they're achievable figures for most mid-size stores. The economic implication is direct: a team handling 500 support tickets a month at 60% deflection answers 200, not 500. Same staffing level, far less volume, faster responses for complex cases.

Upselling is persuading a customer to buy a higher-value version of what they're already considering. Cross-selling is recommending complementary products alongside their intended purchase. Both drive average order value.

In AI ecommerce, neither looks like "would you like fries with that?" AI upselling is specific: noticing that a shopper has been comparing two camera models and spending disproportionate time reading video spec sections, then surfacing the next tier with a focused explanation of what the upgrade actually gets them — not a list of features, but the relevant one.

Cross-selling done well feels like service. Someone buying a camera should probably know they'll need a memory card and a case — not because the store wants to sell more things, but because arriving without those accessories is a bad experience. The difference between an AI that cross-sells effectively and one that annoys customers is whether the suggestions feel relevant or random.

Information a shopper explicitly and voluntarily shares with a brand — quiz answers, stated preferences, wishlist items, gift intent — as distinct from behavioural data you infer from tracking.

The value of zero-party data is two-fold: it's accurate (the customer told you directly, not inferred from proxy behaviour), and it's privacy-compliant by definition (they chose to share it). As third-party tracking continues to erode, zero-party data becomes the premium input for personalisation.

AI sales agents are a natural collection mechanism. A conversational flow that asks "Who are you shopping for? What's the occasion? What's your budget?" collects high-quality preference data in a way that feels like helpful service rather than a data grab. That data then feeds better recommendations, more relevant email campaigns, and a personalisation engine that improves with every conversation. Done well, both sides benefit: the store gets richer data, the shopper gets a better experience.