Marketing glossary

A controlled experiment comparing two ad variations (A vs B) with equal traffic split to determine which performs better on a specific metric. Requires statistical significance before declaring a winner, which typically means enough conversions to be confident the difference isn't random. Simple and reliable, but slow. At typical ecommerce conversion rates, an A/B test needs 1,000-5,000 impressions per variant to reach significance. For brands testing 20+ creatives monthly, sequential A/B testing is too slow.

An open protocol for communication between AI agents built by different vendors or frameworks. It defines three things: Agent Cards, metadata documents advertising what an agent can do, Tasks, the structure of work exchanged, and a transport over HTTP, SSE and JSON-RPC 2.0. Donated by Google to the Linux Foundation in 2025.

IAB Tech Lab's umbrella initiative for agentic advertising standards, named on 26 February 2026 and published as a framework on 16 March 2026, described by the Tech Lab as the initiative under which all of its agentic work is developed with the advertising ecosystem. Open source, and organised in three pillars: Agentic Foundations, which is execution infrastructure including the Agentic Real Time Framework (ARTF); Agentic Protocols, the management layer covering Agentic Direct, Agentic Audiences and Agentic Mobile; and Trust and Transparency, the Agent Registry providing verified agent identity and disclosure. Reference implementations ship as buyer, seller and registry agents. Version 2.3 as of mid 2026.

Ad spend divided by ad-attributed revenue, expressed as a percentage. The inverse of ROAS. A 25% ACOS means you spend $0.25 in ads for every $1 in revenue (equivalent to 4x ROAS). Commonly used in Amazon advertising but applicable across channels. ACOS below your contribution margin means advertising is profitable; above it means you're losing money on ad-driven sales. Target ACOS should be set relative to margin, not arbitrary benchmarks.

An open standard for programmatic commerce flows between buyers, AI agents and businesses, developed jointly by Stripe and OpenAI. It lets an AI platform initiate a transaction without becoming the merchant of record, passing payment credentials as tokens rather than raw details. REST and MCP compatible, Apache 2.0.

The average number of times each person in your target audience has seen your ad. Calculated as impressions divided by reach. Moderate weekly frequencies on Meta typically begin triggering creative fatigue for prospecting audiences. Retargeting can tolerate higher frequency because the audience already has purchase intent. Monitoring frequency at the ad set level is critical - account-level frequency averages hide individual audience oversaturation.

An open standard for agent-to-agent advertising transactions, built on MCP and described as OpenRTB for the AI era: agent-to-agent negotiation rather than impression-level auction. Published by AgenticAdvertising.org, an independent non-profit that describes itself as in its formation phase, with interim advisors Randall Rothenberg, former CEO of the IAB, and Matthew Egol, formerly of Booz & Company, and no seated board; its bylaws set out a 40-director elected board drawn from brands, agencies, publishers and technology providers. Four modules: Signals Activation for audience discovery, Media Buy for campaign execution, Creative for asset management, and a Curation module scheduled for Q2 2026. FreeWheel and NBCUniversal ran the first MCP-based agent-to-agent media buy in January 2026.

Meta's fully automated campaign type that uses ML to optimize creative selection, audience targeting, and placement simultaneously from a single campaign structure. Requires minimal manual setup: upload creatives, set a budget, and the algorithm does the rest. Average brands see lower CPA versus manual campaigns, but with limited transparency into what's working. Key limitation: you can't see which audiences or placements are driving results, making it harder to learn and iterate. Best used alongside manual campaigns, not as a replacement.

Optimising to be the answer to a question rather than a result for a query. Used largely interchangeably with GEO; where a distinction is drawn, AEO tends to mean featured-snippet and direct-answer surfaces while GEO means generated prose.

Commerce driven by AI shopping agents that research, evaluate, and recommend products on behalf of consumers, bypassing traditional search, ads, and storefronts entirely. When a user asks ChatGPT 'best running shoes for flat feet,' the AI agent evaluates product data, reviews, and brand authority to make a recommendation without the user ever seeing a Google result or clicking an ad. Brands optimizing for agent commerce today have a structural first-mover advantage because agent mention patterns compound over time.

Establishing cryptographically which agent is making a request and on whose behalf. Visa's Trusted Agent Protocol implements this with signed HTTP messages carrying agent intent and verified user identity, so a merchant can tell an approved assistant from a scraper.

A shared store that lets an agent system retain facts about a brand between sessions: constraints such as a CAC ceiling, stated preferences such as reporting cadence, past decisions and their outcomes, and learned performance patterns. The distinction from chat history is durability and scope. Chat history is one conversation; agent memory is meant to be read by any agent at any later point, so a limit stated once does not have to be restated. Whether a given system actually does this, and for how long, is worth checking before relying on it.

The frequency at which AI agents mention or recommend your brand when prompted with category-relevant queries. Measured by systematically probing AI platforms (ChatGPT, Perplexity, Claude, Gemini, Google AI) with purchase-intent queries and tracking brand appearances. A brand with a 34% mention rate in 'best protein powder' queries appears in roughly 1 in 3 agent responses. Unlike ad impressions, agent mentions carry implicit endorsement: the AI is recommending you, not just showing your ad.

Coordinating multiple specialized AI agents to work together on complex tasks: routing questions to the right agent, sharing context between agents, resolving conflicts when agents disagree, and synthesizing insights across domains. A question such as 'should I increase Meta spend?' may need an attribution check, a forecast of the impact, and a scenario comparison, each from a different specialist, with the orchestrator responsible for sequencing them and reconciling the answers into one recommendation.

A composite metric (0-100) measuring how well-optimized a brand's product data, structured markup, reviews, and content are for AI agent discoverability. Factors include: product title quality (do titles contain parseable attributes?), schema markup completeness, review density and sentiment, description depth, brand authority signals, product attribute coverage, and image quality. Higher scores correlate with higher agent mention rates.

IAB Tech Lab's register of advertising agents, and the Trust and Transparency pillar of AAMP. In the Tech Lab's wording it establishes a trusted framework for agent identity, verification, and disclosure so participants can confidently understand who, or what, they are transacting with. Operated by Tech Lab through its Tools Portal, live, and free to register for members and non-members alike.

The percentage of relevant AI agent conversations in your category where your brand is mentioned or recommended, compared to competitors. If agents mention your brand in 22 out of 100 'best moisturizer' queries, your agent share of voice is 22%. Unlike traditional share of voice (measured by ad spend or media mentions), agent share of voice is earned through product quality, data structure, and brand authority, not bought through media budgets.

How often and how favorably your brand appears when AI agents answer product-related queries. The agent-era equivalent of search engine visibility, but fundamentally different: there are no rankings, no ads, no paid placements. Visibility is determined by the quality of your product data, the strength of your reviews, the presence of structured markup, and your overall brand authority in the agent's training data and retrieval sources. Tracked by probing AI platforms hourly with category queries.

Revenue from purchases influenced by AI agent recommendations. Currently invisible to standard analytics because the user journey (ask AI agent → get recommendation → Google the brand → buy) appears as 'branded search' or 'direct traffic' in GA4. The only way to measure it is through agent probing (tracking your mention rate) combined with correlation analysis against unexplained revenue spikes.

A take rate charged by an agent platform on a purchase it originates, distinct from and additional to payment processing. OpenAI's Instant Checkout charged merchants 4 percent per completed purchase on top of standard Stripe processing, with no extra cost to the shopper. OpenAI ended Instant Checkout in March 2026 and moved the capability toward its Apps model.

The consumer behavior pattern of asking AI agents for product recommendations instead of browsing search results, marketplaces, or social media. A user practicing agentic shopping says 'find me a lightweight laptop under $1,200 with all-day battery' to ChatGPT instead of searching Google or scrolling Amazon. This behavior is growing at 10x the rate of traditional search and produces no ad impressions, no click data, and no trackable UTM parameters. The purchase looks like direct traffic in analytics.

A storefront exposed to AI agents through a protocol endpoint rather than only as rendered HTML. Shopify built these into the platform in its Winter 2026 Edition, activating UCP and native MCP servers by default for all merchants, so an agent can query the catalogue and transact without parsing the visual site.

When an AI model generates plausible-sounding but factually incorrect information. In agent commerce, hallucinations manifest as recommending products that don't exist, citing incorrect prices, inventing features, or attributing reviews to the wrong brand. Products with complete, structured, easily-verifiable data reduce hallucination risk because the agent has authoritative facts to ground its response. Brands with sparse product data are more likely to be hallucinated about, or worse, confused with competitors.

An autonomous AI system, such as ChatGPT, Perplexity, Google Gemini or Claude, that evaluates products, compares options, and makes purchase recommendations based on user queries. Unlike search engines that show links, shopping agents synthesize information from multiple sources and make a direct recommendation. They evaluate product data, reviews, brand authority, pricing, and availability to generate a response. There are no paid placements; recommendations are based on the agent's assessment of product quality and relevance.

Automatically identifying statistically unexpected changes in marketing metrics: sudden CPA spikes, unusual CTR drops, spend anomalies, or conversion volume changes that deviate from expected patterns. Catches problems hours or days before manual review would. Uses statistical models to establish 'normal' ranges for each metric and flags deviations beyond a confidence threshold. Critical for brands managing high daily spend where a day of undetected problems can waste thousands.

The average dollar amount spent per transaction. Calculated as total revenue divided by total orders. Higher AOV means you can afford higher CPA while maintaining profitability. AOV varies by channel (Google Shopping often has higher AOV than Meta because of purchase intent), by creative (product bundles drive higher AOV than single-product ads), and by audience (returning customers typically have 20-30% higher AOV than first-time buyers).

Google's open protocol for agent-mediated payments, announced September 2025 with launch partners including Mastercard, PayPal, Coinbase and American Express. It enforces authorisation, authenticity and accountability through three digitally signed mandates carried as W3C Verifiable Credentials: an Intent Mandate for the user goal, a Cart Mandate for the transaction, and a Payment Mandate authorising the transfer. Payment-agnostic across cards, bank transfer and stablecoins.

The time period after an ad interaction (click or view) during which a conversion is credited to that ad. Meta defaults to 7-day click, 1-day view. Google Ads defaults vary by campaign type. Longer windows capture more conversions but also capture more that would have happened anyway. Shortening attribution windows is one of the simplest ways to reduce overclaiming and get closer to true incremental performance. Testing different windows and comparing results reveals how much of your reported performance depends on generous window settings.

When a target audience has been shown ads so frequently that incremental reach approaches zero and each additional impression drives diminishing returns. Detected by the simultaneous pattern of rising frequency, declining CTR, and increasing CPA. Smaller audiences saturate faster than larger ones: a niche audience of a few hundred thousand reaches saturation at far less monthly spend than a broad audience of millions. The cure is audience expansion, creative refresh, or strategic spend reduction, not doubling down.

Total revenue divided by total ad spend across all channels. A top-level health metric that shows overall advertising efficiency but hides which channels are driving incremental value and which are free-riding. A brand with a 4x blended ROAS might have Meta at 6x and Google Display at 1.2x, but blended ROAS won't tell you that. Useful as a directional metric for month-over-month trending, but should never be used for channel-level budget allocation decisions.

Running ads with minimal audience restrictions, letting the platform's ML algorithm find converters from a wide pool. Increasingly effective as Meta Advantage+, Google PMax, and TikTok Smart+ mature their bidding algorithms. Counterintuitive: restricting audience often hurts performance because it limits the algorithm's optimization surface. Broad targeting with strong creative typically outperforms narrow targeting with average creative at scale. Works best above $5K/month per campaign where the algorithm has enough conversion data to optimize.

The process of distributing ad spend across channels, campaigns, and audiences based on expected incremental return. Should be dynamic and updated weekly based on current performance data, not fixed in quarterly planning cycles. Optimal allocation requires understanding the diminishing returns curve for each channel and finding the spend level where marginal CPA across all channels is equalized. A well-optimized allocation improves blended ROAS without increasing total spend.

The rate at which your daily or monthly budget is being spent relative to plan. Underpacing means you're leaving potential revenue on the table. Overpacing means you'll run out of budget before the period ends, missing late-period opportunities. Platform algorithms handle daily pacing, but monthly and quarterly pacing requires manual oversight. Budget pacing should account for day-of-week and time-of-month performance patterns.

Total cost to acquire one new customer, including ad spend, creative production, agency fees, and marketing tools. Calculated as total marketing spend divided by new customers acquired. Lower CAC means more efficient growth. Must be compared against LTV to ensure profitability: if CAC exceeds LTV, you're losing money on every customer. Benchmarks vary wildly by vertical, AOV, and category: premium categories typically run higher CAC than mass-market.

The ratio of customer acquisition cost to lifetime value. A 3:1 ratio (LTV is 3x CAC) is the common benchmark for healthy unit economics. Below 3:1 suggests you're either overspending on acquisition or your product doesn't retain well. Above 5:1 suggests you're underinvesting in growth and could scale faster. The ratio should be calculated at the channel level to identify which acquisition channels produce the most valuable customers, not just the cheapest ones.

A machine-readable declaration of what a system can do, exchanged before a transaction so the two sides agree on the available operations. UCP includes capability negotiation as a distinct stage between discovery and checkout; A2A uses signed Agent Cards to advertise an agent's capabilities and access method.

The percentage distribution of ad spend across advertising platforms. A typical DTC ecommerce channel mix might be 50% Meta, 25% Google, 15% TikTok, 10% other. The optimal mix varies by brand, product category, AOV, and customer demographics. The right channel mix changes over time as channels mature, costs shift, and audience behavior evolves. Brands that lock into a static channel mix leave money on the table.

The rate at which customers stop buying or cancel. In subscription models it is measured directly; in non-subscription DTC it is inferred from lapsed purchase behaviour against a defined window.

Anthropic's crawler for Claude. As with OpenAI, separate user agents cover different purposes, and robots.txt directives are honoured per agent.

Crediting a conversion only where the user actually clicked the ad, as opposed to view-through attribution which credits an impression that was merely served.

Grouping customers by acquisition date (or other shared characteristic) and tracking their behavior over time. Reveals whether newer cohorts are more or less valuable than older ones. A declining cohort LTV curve means your targeting is getting less efficient or product-market fit is weakening. An improving curve means your acquisition strategy is finding better customers. Essential for accurate LTV calculation and for detecting problems before they show up in top-line metrics.

The architecture by which AI models improve continuously as they learn from every marketing decision and its outcome. Named after compound interest because the improvement rate accelerates: each cycle of prediction, observation, and model update makes the next prediction more accurate. A compound learning system at month 6 reflects more learned context than at month 1 because it has observed many decision-outcome pairs specific to your brand. In the agent commerce era, compound learning also applies to agent visibility optimization: the system learns which product data structures, descriptions, and attributes correlate with higher agent mention rates, and continuously refines recommendations. The contrast is with a static model, whose accuracy is fixed at training time and drifts as conditions change.

A statistical range around a forecast that quantifies uncertainty. A 90% confidence interval means the actual result will fall within that range 90% of the time. Wider intervals signal higher uncertainty and should make you more cautious about committing budget. A forecast of '$500K revenue, 90% CI: $420K-$580K' is far more useful than '$500K revenue' alone because it tells you how much to trust the prediction. Narrower confidence intervals over time indicate the model is learning and improving.

Google's framework for adjusting tag behaviour based on a user's consent state, required for advertising features in the EEA. Where consent is absent it sends cookieless pings and models the missing conversions.

The maximum amount of text an LLM can consider at once when generating a response. GPT-4o: 128K tokens (~96K words). Claude: 200K tokens (~150K words). Larger context windows mean agents can evaluate more products simultaneously in a single query. When a user asks 'compare the top 5 protein powders,' the agent needs enough context window to hold all 5 product descriptions, reviews, and specifications at once.

Revenue minus variable costs (COGS, shipping, payment processing, returns) expressed as a percentage. The true margin available to cover fixed costs and marketing spend. A product with 70% contribution margin can afford much higher CAC than one at 30%. Performance marketers should optimize toward contribution-margin-adjusted ROAS rather than raw ROAS to ensure every dollar of ad spend is generating actual profit, not just revenue.

Purchases that originate from natural language conversations with AI agents, chatbots, or voice assistants: no search query, no ad click, no product listing page. The buyer describes what they need in plain language, the AI evaluates options, and a recommendation (or direct purchase) follows. Differs from traditional ecommerce in that the AI agent acts as a trusted intermediary, and the brand has zero control over how they're presented. Product data quality becomes the primary lever for conversion.

A platform-run experiment (available on Meta, Google, and TikTok) that measures incremental conversions by randomly splitting audiences into test and control groups at the platform level. More statistically rigorous than basic A/B tests because it uses platform-level randomization. However, still controlled by the platform, which creates potential conflicts of interest. Best used as a validation tool alongside independent incrementality measurement rather than as the sole source of truth.

The percentage of visitors or ad clickers who complete a desired action (purchase, signup, lead form). Calculated as conversions divided by clicks or sessions times 100. Ecommerce average is 2-3% but varies wildly by traffic source (3-5% for branded search, 0.5-1.5% for cold social traffic), device (desktop converts 2x higher than mobile for most categories), and price point (sub-$50 products convert 2-3x higher than $200+ products).

Meta's server-side tracking solution that sends conversion events directly from your server to Meta's ad system. Improves data accuracy, signal quality, and match rates compared to pixel-only tracking. Should run alongside the Meta Pixel in a redundant setup with deduplication to maximize event coverage. Properly implemented CAPI improves Meta's reported ROAS through better event matching and reduces CPA by improving the algorithm's optimization signal quality.

The average cost to generate one conversion, whether that's a purchase, signup, lead, or other defined action. Calculated as total ad spend divided by total conversions. The core efficiency metric for performance marketing. Important to distinguish between platform-reported CPA (based on attributed conversions, often inflated) and true incremental CPA (based on conversions your ads actually caused). A 'low' CPA that's based on overclaimed conversions is actually a mirage.

The cost per 1,000 ad impressions. A media cost metric that reflects how expensive it is to reach your target audience. Varies significantly by platform (TikTok generally cheapest, LinkedIn most expensive for B2B), audience targeting (broad is cheaper, narrow is pricier), seasonality (Q4 CPMs run substantially higher than Q1), and competitive intensity. Rising CPMs without rising conversion rates is a clear signal to either improve creative, adjust targeting, or reallocate budget.

The decline in ad performance that occurs when a target audience sees the same creative asset too many times. Detected through rising frequency paired with declining CTR, increasing CPA, and dropping ROAS. The fatigue curve varies by format: static images fatigue faster than video, and UGC-style content tends to last longer than polished studio content. Early detection is critical because performance degrades exponentially once fatigue sets in. Most brands detect fatigue late.

The practice of decomposing an ad into its constituent, taggable elements: hook type (question, statistic, visual shock), visual style (UGC, studio, lifestyle), copy angle (benefit, problem-solution, social proof), offer structure (discount, free shipping, bundle), and CTA format (shop now, learn more, limited time). Tagging a creative library this way turns 'this ad worked' into 'this hook type plus this offer structure worked', which is a testable hypothesis rather than an anecdote. The value depends entirely on library size: a few dozen ads cannot support element-level conclusions.

How frequently new creative assets are introduced to replace fatigued ones. DTC brands need to refresh primary creatives regularly and maintain a steady pipeline of fresh creatives. The required refresh rate increases with spend level: higher spend drives higher frequency against the same audiences, which means more creative variety is needed to sustain performance.

The practice of running creative variants against each other to establish which performs and, ideally, which element caused the difference. Distinct from optimisation, which shifts budget to a winner; testing is about producing a transferable lesson.

Insights and model priors derived from analyzing anonymized, aggregated performance data across many brands. What works for one fashion brand often applies to others in the category. AI models trained on cross-brand data can make informed predictions for new brands from day one rather than starting from scratch. The obvious caveat is that a prior drawn from other brands is a starting point, not a finding about yours, and it should be replaced by your own data as soon as there is enough of it. Aggregation also raises a real confidentiality question, which is why the aggregation is anonymised.

The percentage of people who click an ad after seeing it, calculated as clicks divided by impressions times 100. A proxy for creative relevance and audience targeting accuracy. Meta feed ads average 0.9-1.5% CTR, Google Search ads average 3-6%, and display averages 0.3-0.5%. Declining CTR at stable frequency suggests creative fatigue. Declining CTR at rising frequency confirms it. A high CTR with low conversion rate points to a landing page or offer problem, not an ad problem.

Uploading hashed first-party customer data to a platform to target, exclude or seed from those customers. Match rates vary by how much identifying data you hold and how current it is.

The amount a campaign is permitted to spend per day. Most platforms overspend it on high-opportunity days and underspend on others, balancing across a week rather than capping strictly at the daily figure.

Revenue driven by channels that traditional analytics structurally cannot track, particularly AI agent recommendations that appear as 'direct traffic' or 'branded search' in GA4. When ChatGPT recommends your product and the user googles your brand name to buy, GA4 credits 'branded search', and the AI agent's influence is invisible. The dark funnel isn't a tracking gap you can fix with better UTMs; it's a structural limitation of click-based analytics in an agent-driven world.

A secure environment where two or more parties (typically a brand and an ad platform or publisher) can match and analyze their combined datasets without either party seeing the other's raw data. Used for audience matching, measurement, and attribution in a privacy-safe way. Meta's Advanced Analytics, Google's Ads Data Hub, and Amazon Marketing Cloud are major clean room environments. Increasingly important as user-level tracking becomes restricted.

Combining data from multiple sources (Meta Ads, Google Ads, TikTok Ads, Shopify, GA4, email platforms) into a single, consistent dataset with standardized naming conventions, unified timestamps, and reconciled metrics. Eliminates the discrepancies that occur when each platform reports slightly different numbers. Without unification, comparing Meta ROAS to Google ROAS is comparing apples to oranges because each platform defines conversions, attribution, and revenue differently.

An algorithmic attribution model offered by platforms like Google that uses machine learning to assign conversion credit based on observed path patterns. More accurate than rules-based models but still limited to the platform's own data and biased toward the platform's channels. Google's DDA will naturally favor Google touchpoints. Best used as one input among many rather than as a single source of truth for cross-channel budget decisions.

The phenomenon where additional ad spend produces progressively less incremental revenue. Every channel has a saturation curve, and spending past the optimal point wastes budget. A channel producing $5 in revenue per $1 at $50K/month spend might only produce $2.50 per $1 at $150K/month. The optimal spend level sits at the inflection point of the S-curve where marginal returns start declining. Most brands overspend on their 'best' channel because they don't model diminishing returns.

Automated assembly of ad creatives from component parts (headlines, images, CTAs, body copy) by the ad platform's algorithm. Meta's Advantage+ Creative and Google's responsive ads are DCO systems. Useful for scaling variations without manual design work, but reduces creative control and makes it harder to learn what's actually working because the platform mixes components opaquely. Best for testing broad messaging directions before investing in full production of winning concepts.

A numerical representation of text in a high-dimensional vector space where semantically similar items are positioned close together. 'Running shoes for flat feet' and 'supportive athletic footwear for low arches' would have similar embeddings despite different words. AI agents use embeddings to match product data to user queries by semantic meaning, not just keyword overlap. This is why natural, descriptive product content outperforms keyword-stuffed titles in agent commerce.

Google's privacy-safe tracking solution that supplements existing conversion tags by sending hashed first-party customer data (email, phone, address) from your website to Google. Improves conversion measurement accuracy by matching conversions that would otherwise be lost due to cookie restrictions. Available for both Google Ads and GA4. Implementation requires passing hashed customer data at the point of conversion, either through gtag.js, Google Tag Manager, or the Google Ads API.

The process of selecting, transforming, and creating input variables (features) that help machine learning models make better predictions. In marketing AI, features include spend by channel, day of week, time since last creative refresh, audience saturation level, competitive CPM index, and hundreds more. The quality of features matters more than the complexity of the model. Good feature engineering is why specialized marketing AI outperforms general-purpose tools.

Structuring product titles, descriptions, attributes, and schema markup so AI agents can accurately parse, evaluate, and recommend your products. Unlike SEO (optimizing for search algorithms) or feed optimization for Google Shopping (optimizing for ad relevance), agent feed optimization targets the evaluation criteria AI agents use: attribute completeness, specification clarity, comparison-ready data points, and semantic richness. A product titled 'Protein Powder 2lb Chocolate' scores far lower than 'Premium Whey Isolate Protein Powder, Chocolate, 30g Protein, 2lb, Clean Label, Muscle Recovery.'

Training a pre-existing LLM on domain-specific data to improve its performance for particular tasks. In a marketing context that domain data is typically historical performance, attribution patterns and creative metadata. The intended gain is discrimination a general model lacks: telling creative fatigue apart from audience saturation when both present as the same ROAS decline. Fine-tuning is not free, and it is often the wrong tool where retrieval over current data would answer the question better.

An attribution model that gives 100% of conversion credit to the first touchpoint in the customer journey. Overvalues awareness channels and ignores everything that happens between discovery and purchase. Rarely used as a primary model but useful as a comparison point against last-click to understand the full spectrum of channel contribution.

Data collected directly from your customers through owned touchpoints: purchase history, email engagement, site behavior, loyalty program activity, and customer service interactions. Increasingly the most valuable data asset as third-party cookies disappear, iOS tracking restrictions expand, and platform-provided data degrades. Brands with strong first-party data strategies (email collection, account creation, loyalty programs) have a structural advantage in targeting, personalization, and attribution accuracy.

A limit on how many times a single user sees your ad within a time period. Prevents creative fatigue, wasted impressions, and negative brand perception from overexposure. Lower caps for prospecting, higher caps tolerated for retargeting. Platform-level frequency caps are blunt instruments; campaign-level caps give more control. Without caps, algorithms optimize for cheap impressions, often by repeatedly showing ads to the same engaged users until they stop engaging.

Google's current analytics platform, built around an event-based data model. Includes ML-powered predictive metrics, cross-platform tracking, and BigQuery integration. Key limitations: aggressive data sampling at high volumes, limited historical lookback, and, critically in 2026, a structural inability to track AI-agent-referred traffic. When a user asks ChatGPT for a product recommendation, googles the brand name, and buys, GA4 attributes it to 'branded search' or 'direct traffic.' The AI agent's influence is completely invisible. This dark funnel blind spot means GA4 systematically understates the value of agent commerce and overstates the value of branded search.

The practice of optimising to be cited in a generated answer rather than ranked in a list of links. Where SEO competes for position on a results page, GEO competes to be the source a model draws on when composing a response, which makes structured data, extractable claims and third-party corroboration the working levers.

An incrementality testing method that uses matched geographic regions as test and control groups. One region receives ads while a statistically similar region does not. Particularly useful when user-level holdout tests aren't feasible (e.g., due to iOS restrictions or cross-device complexity). Requires careful market matching and enough regional volume to be statistically meaningful. Works well for measuring the incremental impact of channel-level spend changes like pausing Meta in one region while keeping it active in another.

OpenAI's crawler for gathering training data, distinct from OAI-SearchBot. Blocking it excludes your content from future model training but does not affect whether ChatGPT can surface you in search.

The percentage of viewers who watch 50% or more of a video ad. Measures whether your creative sustains attention after the initial hook. A high hook rate with a low hold rate indicates a strong opening but weak middle content. For direct response ads, hold rate correlates with conversion intent - people who watch most of your video are significantly more likely to click and purchase.

A controlled experiment where a portion of the target audience is deliberately excluded from seeing ads, creating a control group. By comparing conversion rates between the exposed group and the holdout group, you can measure the true incremental impact of your advertising. The most straightforward incrementality test to run. Requires enough volume to achieve statistical significance and a long enough test window (typically 2-4 weeks) to capture full purchase cycles.

The percentage of viewers who watch past the first 3 seconds of a video ad. The single most important leading indicator of video creative quality. If people aren't stopping to watch, nothing else matters - your message, offer, and CTA are irrelevant. Benchmark hook rates vary by platform: TikTok generally runs higher than Meta feed, with YouTube pre-roll lower still. Improving hook rate is usually the highest-leverage creative optimization you can make.

The count of times an ad was served, including repeat serves to the same person. Distinct from reach, which counts unique people. Impressions divided by reach is frequency.

The true return on ad spend after removing conversions that would have happened without any advertising. Measured through holdout testing, geo-lift studies, or conversion lift experiments. Always lower than platform-reported ROAS because platforms count organic conversions as ad-driven. For example, if Meta reports a 5x ROAS but 30% of those conversions were organic, your iROAS is actually 3.5x. This metric is the single most important number for budget allocation decisions because it tells you the real incremental value of each dollar spent.

The true causal lift in conversions directly attributable to advertising. It measures what would NOT have happened without ad exposure. The gold standard of attribution accuracy because it answers the fundamental question: did this ad actually cause this sale, or would the customer have bought anyway? Measured through controlled experiments like holdout tests, geo-lift studies, and conversion lift studies. Without incrementality measurement, you're optimizing toward a number that includes conversions your ads didn't cause.

The accumulated knowledge about a brand's performance patterns, constraints, past decisions, and learned lessons that persists across all agent interactions and compounds over time. Unlike chat history (which is session-based), institutional memory captures durable facts: 'TikTok CAC is consistently above target,' 'Q4 Meta CPMs rise 40% by week 3 of November,' 'the founder prefers weekly Slack reports over email.' Its usefulness grows with the number of decision-outcome pairs recorded, which means it is thin early on and should not be presented as an advantage a new deployment already has.

Apple's App Tracking Transparency framework, launched April 2021, requiring apps to get explicit user permission before tracking activity across other apps and websites. The majority of users opt out. Devastated Meta's tracking accuracy, substantially reduced retargeting pool sizes on iOS, and degraded conversion reporting reliability. Forced the entire industry toward server-side tracking, probabilistic modeling, and first-party data strategies. The single largest disruption to digital advertising measurement in the past decade.

A neural network trained on massive text datasets that generates human-like responses to natural language prompts. GPT-4o, Claude, Gemini, and Llama are prominent examples. LLMs power AI shopping agents, making them the engine behind agent commerce. For brands, LLMs are the new gatekeepers: they decide which products to recommend based on the data available to them. Unlike search algorithms that rank links, LLMs synthesize information and make direct recommendations.

An attribution model that gives 100% of conversion credit to the last touchpoint before purchase. Still the default in many analytics setups. Systematically overvalues bottom-funnel channels like branded search and retargeting while undervaluing awareness and consideration channels like paid social prospecting, YouTube, and display. A brand running last-click attribution will consistently underspend on top-of-funnel and overspend on branded search, creating the illusion that brand search is highly efficient when it's actually capturing demand generated elsewhere.

A markdown file at the site root offering a curated, machine-readable map of a site for language models, by analogy with robots.txt. Adoption is uneven and it is increasingly generated automatically by platforms rather than authored.

An audience the platform builds by finding users resembling a seed list you supply. Quality is bounded by the seed: a lookalike of all purchasers is a different audience from a lookalike of your top-decile customers.

The total revenue (or profit) a customer generates over their entire relationship with the brand. Calculated by multiplying average order value by purchase frequency by average customer lifespan. High-LTV brands (subscription, consumables, fashion with high repeat rates) can afford higher acquisition costs. A brand with $200 LTV and $60 CAC has a 3.3x CAC:LTV ratio, which is considered healthy. LTV should be calculated on a cohort basis to detect trends over time.

The cost of acquiring one additional customer at the current spend level. Different from average CPA because it reflects the cost of the next conversion, not the average of all conversions. As you increase spend, marginal CPA rises due to diminishing returns. The optimal spend level is where marginal CPA equals your target CPA or where marginal CPA across channels is equalized. This is the single most useful metric for budget allocation.

A statistical approach that uses regression analysis on historical data to estimate the contribution of each marketing channel to business outcomes. Works at an aggregate level (not user-level) making it privacy-safe and resilient to tracking changes. Takes into account external factors like seasonality, promotions, and economic conditions. Typically requires multiple years of historical data and works best for high-spend brands across multiple channels. Slower to implement than MTA but provides a more holistic and unbiased view of channel effectiveness.

An open standard for connecting AI applications to external systems: data sources, tools and workflows. The official framing is a USB-C port for AI applications, a single connection format so an integration built once works across clients. Supported by Claude, ChatGPT, VS Code, Cursor and others.

Total revenue divided by total marketing spend, including non-advertising costs like email platform fees, SEO tools, creative production, and agency retainers. Provides a holistic view of marketing ROI that ROAS misses. Sometimes called the 'Bezos metric' because it reflects the true cost of customer acquisition across all channels. A declining MER with stable ROAS suggests rising non-ad costs are eating into overall marketing efficiency.

Meta's browser-side tracking tag, which fires from the visitor's browser and is therefore subject to ad blockers, ITP and ATT. Now normally paired with the Conversions API rather than used alone.

The gradual degradation of a machine learning model's accuracy over time as real-world conditions change. In marketing, model drift happens when consumer behavior shifts, competition changes, platform algorithms update, or seasonal patterns evolve. A model trained on Q1 data will perform increasingly poorly through Q2-Q4 without retraining. Compound learning systems counteract drift through continuous feedback loops. Static models suffer from drift silently until performance degrades noticeably.

A system design where multiple specialized AI agents collaborate on complex tasks, each bringing domain expertise. A typical split gives one agent attribution, another forecasting, another creative analysis, and so on. Unlike a single monolithic model, each agent is narrow and shares context with the others through a common memory layer, so a finding in one domain can propagate: an attribution agent revising what a channel really drove lets a forecasting agent revise its projection without a human relaying it. The tradeoff is coordination cost, which is why orchestration is a design problem in its own right.

An adaptive testing method that dynamically shifts traffic toward better-performing creative variants while still exploring new options. Unlike A/B testing which splits traffic 50/50 until the test ends, bandit testing automatically reduces exposure to underperformers in real-time. Finds winners faster than traditional A/B tests by balancing exploitation (showing what works) with exploration (trying new things). Named after the slot machine problem in statistics.

An attribution model that distributes conversion credit across multiple touchpoints in the customer journey rather than giving all credit to a single interaction. Common models include linear (equal credit to all touches), time-decay (more credit to recent touches), position-based (40% to first and last touch, 20% split across middle), and data-driven (algorithmically weighted). MTA is better than last-click but still relies on trackable digital touchpoints, meaning it misses offline influence, word-of-mouth, and impressions that don't result in clicks.

OpenAI's crawler for search and browsing surfaces, distinct from GPTBot. Blocking it in robots.txt removes your site from ChatGPT's search results while leaving training-data crawling unaffected, which is usually the opposite of what a merchant intends.

The number of days until a customer's cumulative purchases exceed their acquisition cost. A 30-day payback period means you recover CAC within one month. Critical for cash flow planning: a brand with a 90-day payback period and $100K/month in new customer spend needs $300K in working capital just to fund acquisition. Shorter payback periods enable faster scaling because you can reinvest recovered CAC into acquiring more customers sooner.

Google's AI-driven campaign type that runs across all Google surfaces (Search, Display, YouTube, Discover, Gmail, and Maps) from a single campaign. Uses Google's ML to optimize creative assets, audiences, and bidding across surfaces in real-time. Black-box optimization: you provide assets and goals, Google decides where and how to show them. Reporting is limited: you can't see which search terms triggered your ads or which placements drove conversions. Typically replaces Smart Shopping and captures a meaningful share of total Google spend for ecommerce brands.

The gap between what an ad platform reports as conversions and the true incremental conversions the ads actually caused. It arises because platforms use broad attribution windows, count view-through conversions generously, and take credit for conversions that were already going to happen, which is why the effect concentrates on retargeting and branded search where intent already existed. The direction is not universal, and treating it as universal is the common error: measured against 640 Meta incrementality experiments, Haus found Meta under-reporting incrementality on 7-day click for direct-to-consumer advertisers, while Advantage+ campaigns over-reported relative to Manual ones. Because direction and size depend on campaign type and attribution setting, overclaim has to be measured per account through controlled incrementality testing rather than corrected with a fixed per-platform coefficient.

Asking customers 'how did you hear about us?' after purchase to capture self-reported channel influence. Captures channels that digital attribution misses entirely: podcast ads, word-of-mouth, influencer content, TikTok organic, and offline touchpoints. Biased by recency and salience (customers remember what's top of mind, not what actually influenced them), but directionally valuable as a complement to click-based and incrementality-based attribution. Best implemented as a required field at checkout with a well-designed dropdown.

AI agents that detect issues and surface recommendations before you ask. Rather than waiting for you to notice a ROAS drop and investigate, proactive agents monitor 24/7 and alert you: 'Meta ROAS dropped 18% over 3 days, creative fatigue detected on your top 2 ads. Recommended: refresh creatives, here are 3 angles based on your best performers.' The design question is threshold quality: an agent that surfaces everything is a noise generator, and one that surfaces nothing has simply moved the monitoring problem.

A forward-looking forecast of expected return on ad spend, generated by ML models using historical performance data, creative quality signals, audience saturation levels, and competitive dynamics. Unlike reported ROAS (backward-looking and inflated), pROAS tells you what to expect before you spend. A campaign with a pROAS of 2.1x in its current state versus 3.4x with a creative refresh gives you a clear decision framework.

An attack where instructions embedded in content an agent reads are treated as instructions from the user. Indirect prompt injection is the variant that matters in commerce: text hidden in a product description, review or page that redirects an agent reading it.

A technique where an LLM retrieves real-time data from external sources (product databases, review sites, brand websites) before generating a response. This is how AI shopping agents pull current pricing, availability, and product specifications rather than relying solely on training data. Brands with well-structured product data and schema markup are more easily retrieved by RAG systems, leading to more accurate and favorable agent recommendations.

The total number of unique people who saw your ad at least once. Different from impressions, which counts total views including repeats. Reach divided into impressions gives average frequency. Monitoring reach alongside spend reveals whether increased budget is finding new people or just hitting the same audience harder. Flattening reach at rising spend is an early indicator of audience saturation.

The share of customers who buy more than once, within a stated window. The sharpest single read on whether the product justifies a second purchase.

The share of customers still active after a defined period. Meaningful only with the period stated, since 60 percent at 90 days and 60 percent at 12 months describe very different businesses.

Predicting future revenue based on historical patterns, current trends, seasonality, channel performance, and external factors. AI-powered forecasting outperforms manual spreadsheet methods, especially over longer horizons. Accurate forecasting is foundational for budget planning, inventory management, and cash flow decisions. The best forecasting models account for channel-level saturation curves, creative fatigue rates, competitive dynamics, and macroeconomic indicators rather than simple trend extrapolation.

Revenue generated per dollar spent on advertising. A 4x ROAS means $4 in revenue for every $1 in ad spend. Platform-reported ROAS is typically inflated because ad platforms take credit for conversions they didn't cause. True ROAS can only be measured through incrementality testing, which compares results against a holdout group that received no ads. Most ecommerce brands discover their real ROAS is meaningfully lower than what Meta or Google reports.

The typical shape of ad spend efficiency when plotted on a graph. At low spend, returns are minimal (the learning phase where the algorithm has insufficient data). At mid-range spend, efficiency peaks (the sweet spot). At high spend, diminishing returns set in as the audience saturates. Every channel, campaign, and audience has its own S-curve with a different optimal spend level. Finding and staying in the sweet spot of each curve is the core challenge of budget allocation.

The spend level at which a channel or audience can no longer produce meaningful incremental returns. Beyond this point, additional spend primarily drives frequency against the same users rather than reaching new potential customers. Saturation varies by channel, audience size, creative variety, and seasonality. A niche audience of 500K people saturates much faster than a broad audience of 20M. Detecting saturation early prevents wasted spend.

Simulating different budget allocation scenarios to predict outcomes before committing real spend. For example: 'What happens if I shift $20K from Google Search to Meta prospecting?' or 'What if I increase total spend 30% for Black Friday?' Reduces risk by testing decisions mathematically against historical patterns and forecasting models before any money moves. The difference between reactive optimization and proactive strategy.

Running thousands of hypothetical budget allocations, channel mixes, or creative strategies through ML models to predict outcomes before committing real spend. 'What happens if I shift $20K from Google Search to Meta prospecting?' generates a probability distribution of outcomes in seconds. Reduces risk by testing decisions mathematically against historical patterns, diminishing returns curves, and competitive dynamics.

Structured data (JSON-LD Product schema) on product pages that helps AI agents extract precise product attributes (price, availability, ratings, specifications, brand, material, size) without scraping and parsing HTML. Products with complete schema markup are more likely to be recommended by AI agents because the agent can evaluate them with higher confidence. Essential fields: name, description, price, availability, aggregateRating, brand, sku, and all relevant product attributes for your category.

Recurring patterns in ad performance tied to time periods. Includes annual patterns (Black Friday, Q4 surge, January slump, summer slowdown), monthly patterns (payday effects, end-of-month budget flushes), weekly patterns (higher conversion on weekdays for B2B, weekends for impulse purchases), and even intra-day patterns. Must be accounted for in both forecasting and budget allocation. Ignoring seasonality leads to panic during predictable dips and overconfidence during predictable peaks.

Running a Google Tag Manager container on your own server so tags fire server-side rather than in the browser, giving control over what data leaves your infrastructure and improving resilience to browser-level blocking.

Sending conversion data directly from your server to ad platforms, bypassing browser-level limitations like ad blockers, cookie restrictions, and iOS privacy features. More reliable than pixel-based tracking because it's not affected by client-side interference. Required for accurate data in the post-iOS 14.5 era. Implementations include Meta's Conversions API, Google's Enhanced Conversions, and TikTok's Events API. Should run alongside client-side pixels for maximum data coverage.

Apple's privacy-preserving attribution framework for iOS app install campaigns. Provides aggregated, delayed conversion data without user-level identifiers. Limited to 64 possible conversion values and imposes random time delays on postbacks. Makes granular optimization difficult but is the only sanctioned attribution method for iOS app campaigns. Requires careful conversion value schema design to extract maximum signal from limited data slots.

Google's family of auction-time bidding strategies that set bids per auction using signals unavailable to a manual bidder, including tCPA, tROAS, Maximise Conversions and Maximise Conversion Value.

The threshold at which experimental results are unlikely to have occurred by random chance. Conventionally set at 95% confidence (p-value < 0.05). Running A/B tests or budget changes before reaching statistical significance leads to false conclusions and wasted spend. The required sample size depends on the expected effect size and baseline conversion rate. Small differences in performance require much larger samples to detect reliably. Rushing to conclusions is one of the most expensive mistakes in performance marketing.

A statistical method used in geo-lift testing that creates a mathematically constructed 'control' region by weighting a combination of non-test regions to match the test region's pre-test behavior. More accurate than simply comparing one city to another because it accounts for unique regional characteristics. The synthetic control effectively answers 'what would have happened in the test region if we hadn't changed anything?' by creating a virtual counterfactual from real data.

Small data files placed on users' browsers by domains other than the website being visited, historically used to track users across the web for ad targeting and attribution. Being phased out by browser restrictions (Safari and Firefox already block them, Chrome is implementing restrictions). Their deprecation has degraded retargeting accuracy, reduced attribution reliability, and increased the importance of first-party data and server-side tracking.

The percentage of people who stop scrolling when your ad appears in their feed. Measured as 3-second video views divided by impressions on Meta, or similar metrics on other platforms. Distinct from hook rate in that it measures the initial attention grab before the viewer has processed any content. High thumb-stop, low hook rate means your thumbnail or first frame is compelling but the content immediately disappoints.

TikTok's automated campaign optimization system, analogous to Meta's ASC and Google's PMax. Uses TikTok's algorithm to optimize creative selection, audience targeting, and bidding from a simplified campaign setup. Particularly effective for creative-first brands because TikTok's algorithm heavily weights creative quality and engagement metrics (watch time, shares, comments) in its optimization. Early results show lower CPA versus manual campaigns, but requires a steady pipeline of fresh short-form video creative to maintain performance.

A statistical method that analyzes sequential data points (ad spend, revenue, conversions) over time to identify trends, seasonal patterns, and cyclical behavior. The foundation of most forecasting models. Common techniques include ARIMA, Prophet, and LSTM neural networks. For ecommerce advertising, time series analysis reveals hidden patterns like the lag between Meta spend increases and Shopify revenue impact, or the creative fatigue cycle for video ads.

The basic unit of text that LLMs process: roughly 4 characters or ¾ of a word. 'Protein powder' is 3 tokens. Matters for agent commerce because AI agents have processing budgets: product descriptions consuming fewer tokens while conveying more information are evaluated more efficiently. Concise, attribute-rich product data performs better than verbose marketing copy when parsed by AI agents.

Bidding strategies that take an efficiency target and bid to hit it: tROAS targets a return on ad spend, tCPA a cost per acquisition. The platform then buys whatever volume is available at that efficiency.

An open specification co-developed by Google and Shopify covering four stages of an agent purchase: product discovery, capability negotiation, checkout, and post-purchase handoff. Announced at NRF in January 2026 with 20+ retailers, payment networks and processors, and wired into Google's AI Mode in Search and the Gemini apps.

Ad creative that looks and feels like organic content created by real users rather than polished brand advertisements. Includes customer testimonials, unboxing videos, product reviews, and 'day in my life' style content. Consistently outperforms studio-shot creative on social platforms because it matches the native content format. Typically drives lower CPA than traditional brand creative on Meta and TikTok. UGC outperforms in prospecting but often underperforms polished creative in retargeting.

Tags added to destination URLs to track traffic sources in analytics tools. The five standard parameters: utm_source (platform), utm_medium (channel type), utm_campaign (campaign name), utm_content (ad variation), and utm_term (keyword). Inconsistent UTM naming is the number one cause of messy attribution data. Establish a naming convention upfront and enforce it with templates. Missing or incorrect UTMs create 'direct/none' traffic in analytics that makes attribution impossible.

Passing a value with each conversion so the platform optimises for total value rather than conversion count. The value can be revenue, margin, or a predicted lifetime value.

Searching by semantic similarity rather than keyword matching. Instead of finding documents containing the exact words 'budget moisturizer for dry skin,' vector search finds products whose meaning is closest to that intent, including products described as 'affordable hydrating cream for sensitive, dehydrated complexions.' AI shopping agents use vector search to match user queries to products, which is why keyword-stuffed titles perform worse than naturally descriptive ones.

Credits a conversion to an ad that was viewed but not clicked. Common on display, video, and social platforms. The attribution window varies by platform: Meta defaults to 1-day view-through, Google Display can go up to 30 days. Often overcounts because simply viewing an ad in a feed doesn't mean it caused the purchase. A user who was already going to buy might see your ad, not click it, and buy directly - that gets counted as an ad-driven conversion. Narrowing view-through windows or excluding them entirely gives a more conservative (and more accurate) picture.

Visa's framework for establishing agent identity to a merchant, launched October 2025 in collaboration with Cloudflare. It attaches a cryptographic proof of identity to an agent-initiated transaction using signed HTTP messages carrying the agent's intent, verified user identity and payment credentials, so a merchant can distinguish an approved shopping assistant from a scraper or card-testing bot.

The practice of adjusting budget allocation across channels every week based on current performance data rather than waiting for monthly or quarterly reviews. Performance shifts constantly due to competitive dynamics, creative fatigue, audience saturation, and seasonal patterns. Brands that rebalance weekly capture opportunities sooner than those on monthly cycles. Even small weekly shifts between channels compound into significant efficiency gains over a quarter.

A payment protocol that revives the long-unused HTTP 402 Payment Required status code so an agent can pay for a resource inline over HTTP, settled in stablecoins. Established by Coinbase and Cloudflare and moved under Linux Foundation governance in April 2026. Volume is dominated by Base and Solana and settled almost entirely in USDC.