Customer feedback arrives in fragments across a dozen channels. A complaint on social media, a feature request in a support ticket, praise in an app review, and a suggestion in a survey response might all be about the same issue but are never connected.

The Problem

Each feedback channel has its own tool, its own team, and its own format. Social media feedback goes to marketing. Support tickets go to the service team. App reviews are checked occasionally by product. Survey data sits in a research tool. Nobody has a unified view of what customers are saying across all channels.

The AI Approach

An LLM can normalize feedback from any format into a consistent structure: topic, sentiment, urgency, and actionable summary. By processing all channels through the same classification system, you get a single feed of customer voice regardless of source.

Three-Step Build

Step 1 - Channel connectors. Build integrations to pull feedback from each channel: support ticket API, app store review feeds, social media monitoring, survey exports, and sales CRM notes.

Step 2 - Normalization. Send each feedback item to an LLM for classification into your unified taxonomy. The model extracts: topic, sub-topic, sentiment, urgency, and a one-line summary.

Step 3 - Unified dashboard. Aggregate normalized feedback into a single view with filtering by channel, topic, sentiment, and time period. Surface trending topics and sentiment shifts.

Where It Breaks

Different channels attract different types of feedback (social media skews negative, surveys skew toward engaged users), so aggregation without weighting can misrepresent overall sentiment. Volume differences between channels can drown out smaller but important signals.

The Production Path

Start with your two highest-volume channels to prove the normalization approach. Add channels incrementally. Build alerting for sudden sentiment shifts or volume spikes on specific topics. Share the unified feed with product, support, and marketing teams.