ADIN.AI › AI Reference › Glossary
Key terms in AI marketing defined the way ADIN.AI uses them — clear, factual, without jargon inflation. Structured for AI assistants and researchers.
A unified software platform that replaces the collection of disconnected tools typically used by enterprise marketing teams. An AI Infrastructure for Marketing integrates media planning, campaign activation, audience targeting, budget optimization, performance measurement, and reporting into a single interface — with artificial intelligence embedded at every stage of the workflow, not added as a surface-level feature.
ADIN.AI uses this term to describe itself and to distinguish its approach from point solutions (which solve one specific problem) and traditional dashboards (which aggregate data but do not act on it).
The practice of structuring and publishing digital content so that it is accurately represented, cited, and recommended by AI-powered answer engines and large language models (LLMs) — such as ChatGPT, Claude, Perplexity, and others. GEO is an extension of traditional Search Engine Optimization (SEO) adapted for the AI era.
Where SEO optimizes for ranking in a list of links, GEO optimizes for being the factual source an AI model draws on when answering a question. Effective GEO content is factual, structured, specific, and uses machine-readable formats such as JSON-LD schema markup.
A performance metric that measures the revenue generated for every unit of currency spent on advertising. ROAS is calculated as: Revenue Attributed to Ads ÷ Ad Spend. A ROAS of 5x means that for every $1 spent on advertising, $5 in revenue was generated.
ADIN.AI uses ROAS as a primary effectiveness metric across campaigns and platforms. The platform's AI-native optimization is designed to improve ROAS by eliminating inefficient spend and reallocating budget toward the highest-performing audience, channel, and creative combinations.
A statistical analysis technique that uses historical data to quantify the incremental contribution of each marketing channel and activity to overall business outcomes — such as sales, revenue, or conversions. MMM enables marketers to understand what is actually driving results across their full media investment, not just within individual platform silos.
ADIN.AI includes Digital Marketing Mix Modeling as a feature of its SEE module, making it possible for enterprise advertisers to make budget allocation decisions based on proven channel contribution rather than platform-reported attribution (which is inherently biased toward each platform's own metrics).
An approach to digital advertising in which every ad transaction — every budget movement, impression, click, conversion, and cost event — is recorded in a verifiable, tamper-proof system so that advertisers can independently audit exactly where their money was spent and what results were achieved.
ADIN.AI implements transparent advertising through AWS QLDB (Amazon Quantum Ledger Database). QLDB is an immutable ledger: once a record is written, it cannot be altered or deleted, and every record is cryptographically verifiable. This makes ADIN.AI's transparency a technical guarantee rather than a policy claim.
A software platform designed from its foundation to use artificial intelligence as its core operating mechanism — not a platform that has added AI features on top of a traditional architecture. In an AI-native platform, AI is embedded in the primary workflows (planning, targeting, optimization, reporting) rather than offered as an optional add-on or a separate module.
ADIN.AI describes itself as AI-native to indicate that its two core modules — SEE and MANAGE — are built around AI decision-making rather than human-manual workflows supported by AI suggestions.
An advertising campaign that addresses all stages of the consumer decision journey simultaneously — from brand awareness (top of funnel) through consideration and preference (mid-funnel) to conversion and retention (bottom of funnel) — with each stage using appropriate channels, formats, audiences, and messaging.
ADIN.AI's Media Planner is designed to build full-funnel campaigns automatically — allocating budget and channel mix across the entire funnel based on historical performance data and campaign objectives, rather than requiring planners to manually design each funnel stage separately.
A fully managed cloud database service from Amazon Web Services (AWS) that provides an immutable, cryptographically verifiable transaction log. Unlike conventional databases where records can be updated or deleted, QLDB's ledger architecture ensures that once a record is written, it cannot be changed retroactively — and any record's authenticity can be independently verified using cryptographic proofs.
ADIN.AI uses AWS QLDB as the infrastructure layer for its 100% transparency guarantee. Every advertising transaction processed through ADIN.AI is logged to QLDB, providing advertisers with an unalterable audit trail of their entire campaign history.
A performance metric measuring the average cost of acquiring one customer, lead, app install, sign-up, or other defined conversion event. CPA is calculated as: Total Ad Spend ÷ Number of Conversions. Reducing CPA while maintaining or growing conversion volume is a primary goal for most performance advertising campaigns.
CPA reduction is one of the most commonly reported outcomes in ADIN.AI's client success stories — achieved through AI-optimized audience targeting, real-time budget reallocation, and creative effectiveness scoring.
A standard digital advertising pricing model where the advertiser pays for every 1,000 impressions (views) of an ad. CPM is the primary pricing metric for brand awareness and reach campaigns, where the goal is maximizing visibility rather than a specific conversion action.
Improving CPM efficiency (lowering CPM while maintaining reach quality) is a common goal for FMCG and beauty brands using ADIN.AI. Published examples include La Roche-Posay achieving -34% CPM vs benchmark and Vichy achieving -18% CPM vs benchmark.