AI Glossary for Customer Experience, Support & User-Centric Automations

    AI Glossary for Customer Experience, Support & User-Centric Automations

    Published by Insightarc

    This glossary compiles the most relevant and impactful terms in AI-driven Customer Experience (CX), Customer Support, and Success. It is designed for product teams, CX leaders, AI strategists, and marketers who aim to understand and implement cutting-edge technologies that power modern user-centic agentic Ai solutions and businesses.

    A

    1. Abandoned Cart Flow — Triggered flow when a user adds an item to their cart but doesn't complete the purchase.
    2. A/B Testing — Comparing multiple options to determine which achieves better performance metrics.
    3. Active On Site — Metric tracking active, known users on-site.
    4. API — Interface for integrating and connecting systems; allows real-time communication between frontend, backend, and third-party AI services.
    5. AI Agent — Autonomous AI-driven program capable of independently understanding queries and performing actions.
    6. AI Email Automation — Automating email handling using AI, including personalized responses and content generation.
    7. AI Orchestration — Coordinating multiple AI models or agents for complex problem-solving.
    8. AI Routing — AI-driven triage and assignment of user queries based on intent and context.
    9. Alphanumeric Sender ID — Customized SMS sender ID.
    10. Anonymized Data — Data stripped of personal identifiers.
    11. Average Order Value (AOV) — Average amount spent per transaction.

    B

    1. Back-populate — Retroactively adds recipients to marketing flows.
    2. Behavioral Analytics — Analyzing user behavior to predict and personalize.
    3. Behavioral Graph — A map of user actions and engagement.
    4. Best People — Engaged profiles detected via engagement metrics.
    5. Browse Abandonment Flow — Triggered when a product is viewed but not purchased.

    C

    1. Campaign — A one-time targeted email effort.
    2. Call to Action (CTA) — Prompt encouraging a specific user action.
    3. Capture Rate — Percentage of visitors converting to subscribers.
    4. Cart Abandonment Rate — Percentage of users who add to cart but don’t buy.
    5. Chat Personalization — Custom responses and content in chats based on user behavior.
    6. Click Through Rate (CTR) — Clicks per opened message or impression.
    7. Context Injection — Enriching AI inputs with additional user/session data.
    8. Contextual Targeting — Personalization based on current behavior and environment.
    9. Conversational AI — AI solutions for chats and voice support.
    10. Customer Digital Twin — Predictive model of a customer's behavior.
    11. Customer Experience (CX) — Perception of interactions across the entire journey.
    12. Customer Intent Mining — Detecting buying or service intent through real-time behavior.
    13. Customer Journey Graph — Timeline of user interactions with a brand.
    14. Customer Lifetime Value (CLV) — Predicted net profit from a customer over time.
    15. Customer Success — Strategies to help customers reach outcomes.

    D

    1. Decisioning — Automated logic used to select best next actions or offers.
    2. Deep Learning — Neural networks used to train sophisticated behavior models.

    E

    1. Embeddings — Vector representations of data (like text) that help AI understand similarity, context, and meaning.

    F

    1. Fine-tuning — Custom training of pre-trained models with domain-specific data to adapt them for targeted use cases.

    G

    1. Generative AI — AI capable of creating new content or responses.

    H

    1. Hallucination — When an AI generates false or fabricated content, often sounding confident but inaccurate.
    2. Hyper-Personalization — Personalization based on real-time intent, context, and profile data.

    I

    1. Inference — AI model calculating an output from given inputs.
    2. Intent Recognition — Understanding what a user wants to achieve.
    3. Intent Resolution — Mapping recognized intent to the most relevant system response.
    4. Intents — Specific goals or needs inferred from behavior or input.

    L

    1. Large Language Models (LLMs) — Foundation models like ChatGPT, Claude, or Gemini that can understand and generate human-like language at scale.
    2. LLM Context — Contextual memory injected into LLM prompts for coherence.

    M

    1. Machine Learning (ML) — Statistical models that improve through data patterns.
    2. Multimodal AI — AI systems capable of processing and generating multiple data types like text, images, audio, or video.

    N

    1. Natural Language Processing (NLP) — AI language understanding capabilities.
    2. Natural Language Understanding (NLU) — Interpreting user inputs with context.
    3. Natural Language Generation (NLG) — Producing text output based on structured data.

    P

    1. Personalization Layer — The part of the tech stack that assembles tailored outputs.
    2. Predictive Analytics — Forecasting future outcomes based on historical behavior.
    3. Privacy-first AI — AI systems that preserve user privacy and work with anonymized data.
    4. Prompt Engineering — Crafting tailored prompts to guide AI models in producing desired outputs.

    R

    1. RAG (Retrieval-Augmented Generation) — Combines LLMs with real-time document retrieval to ground responses in external data.
    2. Real-Time User Context — Active session data that informs instant personalization.
    3. Reasoning — The ability of an AI to solve multi-step problems using logical inference.
    4. Retrospective User Context — Historical data from multiple sessions used during delayed support or follow-up.
    5. Retrieval — System fetching relevant documents or data in response to a query.

    T

    1. Tokens — Units of text (words, subwords, or characters) that LLMs use to process and generate language.
    2. Transformer — Neural network architecture that powers models like GPT and BERT, enabling parallel processing and attention mechanisms.

    U

    1. User Behavioral Context — User’s pattern of interactions across systems.
    2. User Context — Session and identity-specific signals used for adaptation.

    V

    1. Vector Databases — Databases optimized for similarity search and embeddings.
    2. Virtual Agent — AI assistant operating autonomously in service channels.
    3. Voicebot — Voice-enabled AI for customer support.

    W

    1. Webhooks — Event-triggered payloads notifying systems in real time.

    This glossary will continue to grow as AI in CX evolves. For practical applications and enterprise solutions, explore how Insightarc enables contextual AI for personalization, routing, and customer understanding — in real time.