AI Customer Sentiment Dashboard

Overview

A D2C brand with strong online presence was relying on quarterly NPS surveys to understand customer sentiment. Twice in one year, negative sentiment erupted on social media around product quality issues that the surveys hadn't captured yet. By the time they responded, the damage was done. We built a real-time sentiment intelligence dashboard that monitors every customer touchpoint and surfaces emerging issues before they become crises.

The Challenge

Customer sentiment is expressed across dozens of channels in different ways — a 1-star review, a frustrated support ticket, a sarcastic tweet, and a Facebook comment all express dissatisfaction differently. The AI needed to normalize sentiment across all channels, detect emerging negative trends before they go viral, distinguish between individual complaints and systemic issues, and generate actionable insights rather than just charts.

Our Approach

We built data pipelines from every customer touchpoint: review platforms (G2, Trustpilot, Amazon), social media (Twitter, Reddit, Instagram), support tickets (Zendesk), NPS surveys, and app store reviews. A unified NLP model classifies sentiment, extracts specific topics and themes, and detects anomalies in sentiment patterns. The dashboard surfaces emerging issues ranked by severity and velocity. AI-generated insight summaries explain what's happening, why, and what to do about it.

Key Features

• Real-time sentiment aggregation across all channels
• Emerging issue detection with velocity tracking
• Topic and theme extraction from unstructured feedback
• AI-generated insight summaries and recommendations
• Competitor sentiment benchmarking
• Product-level and feature-level sentiment breakdown
• Automated alerts for sentiment anomalies

Results

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