Appendix D — Marketing AI Glossary (40+ Plain-Language Definitions)
Plain-language definitions for the AI marketing terms used throughout this book series. Use this as a reference when sitting in vendor meetings, scoping projects, or coaching new team members. About 30 terms cover 95% of conversations in 2026.
Key Terms
| Term | Definition |
|---|---|
| A/B Testing | Comparing two versions of a campaign or asset to determine which performs better on a defined metric. |
| Agent (AI Agent) | An AI system that executes multi-step tasks with autonomy, using tools and making choices within set boundaries. |
| API | Application Programming Interface — how software systems talk to each other. |
| Attribution | The method of assigning credit to marketing touches for a customer conversion. |
| Chatbot | A conversational AI interface for customer-facing interactions. |
| Context Window | The amount of text an AI model can consider at once. |
| CRM | Customer Relationship Management system — your source of truth for customer records. |
| CTR | Click-Through Rate — clicks divided by impressions. |
| Data Privacy | The practices and obligations around collecting, storing, and using personal data. |
| Embeddings | Numeric representations of text used for similarity search. |
| EU AI Act | European Union regulation classifying AI systems by risk and assigning obligations. |
| Fine-Tuning | Further training a base AI model on your own data. |
| First-Party Data | Data your business collects directly from customer interactions. |
| Generative AI | AI that creates new content (text, image, audio, video) from a prompt. |
| GEO | Generative Engine Optimization — optimizing for AI-generated search answers. |
| Guardrails | Rules that prevent AI from going off-script. |
| Hallucination | When an AI model produces confident-sounding but incorrect information. |
| Hyper-personalization | Tailoring content, offers, and timing to individual customers using behavioral data. |
| Inference | Running the model to get an output (vs. training). |
| LLM | Large Language Model — the engine behind ChatGPT, Claude, Gemini. |
| LTV | Lifetime Value — predicted revenue from a customer over their tenure. |
| MCP | Model Context Protocol — standard for connecting AI to tools and data. |
| MMM | Marketing Mix Modeling — statistical analysis of channel contribution. |
| Multimodal | An AI system that works across text, image, audio, video. |
| NLP | Natural Language Processing — AI for understanding and generating language. |
| Personalization | Adapting content or experience to a segment or individual. |
| Predictive Analytics | Using historical data to forecast future outcomes. |
| Prompt | The instruction given to an AI model. |
| Prompt Engineering | The practice of crafting prompts that produce reliably useful outputs. |
| RAG | Retrieval-Augmented Generation — AI pulls from your documents to ground answers. |
| RGCO | Role, Goal, Context, Output — the four-part prompt structure. |
| ROAS | Return On Ad Spend — revenue divided by ad cost. |
| ROI | Return on Investment — business outcome relative to cost. |
| SEM | Search Engine Marketing — paid advertising on search engines. |
| SEO | Search Engine Optimization — practices to improve unpaid search visibility. |
| Sentiment Analysis | Using AI to classify the emotional tone of text. |
| System Prompt | The instruction setting an AI model’s role, constraints, and behavior. |
| Token | The unit of text AI models process — roughly 0.75 words in English. |
| UX | User Experience — how people interact with your product. |
| Vector Database | Storage optimized for embeddings (Pinecone, Weaviate, pgvector). |
| Zero-Party Data | Data customers voluntarily share (preferences, intent). |
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