Feedback intelligence glossary.
Short, accurate definitions for the terms consumer brand teams run into when building a voice of customer program — the metrics (CSAT, CES, Net Sentiment Score), the methods (sentiment analysis, topic modeling, zero-shot classification), the plumbing (MCP, ASIN, Bazaarvoice), and the category words Indellia is built around (feedback intelligence, agentic VoC, SKU-level analytics).
The short answer
This glossary defines 30 terms used across voice of customer (VoC), feedback analytics, and agentic AI work for consumer brands. Each entry is 250–500 words, follows a fixed structure — definition, why it matters, example, related terms — and links to the guides and product capabilities at Indellia that apply the concept in practice.
A
- Agentic VoC — voice of customer practice in which named AI agents act on feedback, not just dashboards.
- AI Agents for Feedback — software agents that read feedback and produce decisions or actions.
- ASIN — Amazon Standard Identification Number; Amazon's per-listing product identifier.
- Aspect-Based Sentiment Analysis — sentiment scoring per product attribute (battery, screen, build).
B
- Bazaarvoice — syndicated review network that powers retailer review pages (Walmart, Target, Home Depot, and others).
C
- Closed-Loop Feedback — the process of responding to feedback, fixing the underlying cause, and reporting the fix back.
- Consumer Insights — the discipline of turning consumer behavior and opinion data into brand decisions.
- Customer Effort Score (CES) — survey metric measuring how much effort a customer spent resolving an interaction.
- Customer Intelligence — the broader category covering customer data, behavior, and feedback signals.
- Customer Satisfaction Score (CSAT) — percentage of respondents rating an interaction satisfied or very satisfied.
D
- Deterministic AI — retrieval-grounded AI systems that return the same answer for the same question, with citations.
F
- Feedback Agents — named AI agents scoped to a specific feedback job (themes, anomalies, responses, defects).
- Feedback Analytics — the analysis layer: clustering, sentiment, trend detection over a feedback corpus.
- Feedback Intelligence — the operational capability that turns feedback into near-real-time decisions.
- Feedback Taxonomy — the labeled tree of themes, categories, and tags applied to feedback records.
L
- LLM Hallucination — a large language model producing confident output that is not grounded in retrieved evidence.
M
- Market Intelligence — the category tracking competitor, category, and shopper trends across retail and review data.
- MCP (Model Context Protocol) — open protocol for exposing tools and data to AI clients like Claude, ChatGPT, and Cursor.
N
- Net Promoter Score (NPS) — 0–10 likelihood-to-recommend score. Trademark of Bain, Satmetrix, and Fred Reichheld.
- Net Sentiment Score — (positive − negative) / total, calculated across a feedback corpus.
P
- Product Feedback — feedback tied to a specific product (SKU, UPC, or Model#), not to a brand or service.
R
- Review Aggregation — pulling reviews from multiple retail and review sources into a single normalized store.
- Root Cause Analysis — tracing a surface symptom (a theme, an anomaly) to its underlying cause.
S
- Sentiment Analysis — classifying text as positive, negative, or neutral, with intensity.
- SKU-Level Analytics — feedback, sentiment, and theme analysis resolved at the individual SKU.
T
- Thematic Analysis — qualitative research method for identifying patterns of meaning in text.
- Topic Modeling — statistical method for discovering the topics in a corpus without labeled data.
V
- VoC Program — the standing organizational practice for collecting, analyzing, and acting on customer feedback.
- Voice of Customer — feedback from every channel a brand operates, treated as evidence for decisions.
Z
- Zero-Shot Classification — assigning a label to text without training examples for that label.
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