Customer Sentiment AI

Understand customer emotions at scale. Predict churn from feedback. Improve satisfaction in real-time.

95%+

Accuracy

60-90d

Churn Lead Time

15-30%

Churn Reduction

The Problem

✗ Millions of feedback data points scattered across channels
✗ Manual analysis is impossible at scale
✗ Churn signals missed until customers leave
✗ No unified view of customer satisfaction

Our Solution

✓ NLP + ML: 95%+ sentiment classification accuracy
✓ Multi-channel consolidation (email, chat, calls, social, surveys)
✓ Aspect-based analysis (pricing, service, product, digital)
✓ Predict churn 60-90 days before it happens

What's Included

Real-Time Sentiment Scoring

Analyze customer feedback instantly across all channels

Aspect-Based Analysis

Decompose feedback by topic (pricing, service, product, UX)

Emotion Detection

Extract fine-grained emotions (anger, frustration, joy, trust)

Churn Risk Prediction

Combine sentiment with behavioral data to predict at-risk customers

NPS/CSAT Insights

Analyze open-ended responses; identify promoter/detractor drivers

Automated Alerts

Trigger intervention when negative sentiment spikes detected

How We Deploy

Cloud

AWS/Azure/GCP - process millions of feedback records

On-Premises

Full data control, legacy CRM integration

Hybrid

Feedback on-prem, NLP processing in cloud

Feedback Channels We Analyze

Customer service surveys (CSAT, CES)

Net Promoter Score (NPS) open responses

Call center recordings & transcripts

Chat & email interactions

Social media & online reviews

In-branch feedback & comments

Product feedback submissions

Complaint/issue submissions

Key Use Cases

1Churn prevention via sentiment-based early warnings

2NPS analysis: identify promoter vs. detractor drivers

3Service quality monitoring across branches/channels

4Product feedback prioritization for development

5Employee coaching: sentiment in customer interactions

6Competitive intelligence from social media

7Compliance risk detection from complaint signals

Implementation Timeline

Week 1

Assessment

Audit feedback sources, volume, and sentiment objectives

Week 2-3

Pilot

Deploy on 1 feedback source; validate 95%+ accuracy

Week 4+

Production

Full deployment across all channels; integrate with CRM

Ongoing

Optimization

Monitor trends; identify emerging issues; refine models

Business Impact

15-30%

Churn Reduction

+5-10 pts

NPS Improvement

25-50%

Service Cost Reduction

95%+

Sentiment Classification Accuracy

Technology Stack

NLP & Language Models

Core NLP

• BERT, GPT-4 - 95%+ sentiment classification
• Aspect-Based Analysis - Decompose by topic
• Named Entity Recognition - Extract key products/services

Advanced NLP

• Emotion Detection - Extract anger, frustration, joy
• Topic Modeling - LDA, UMAP for theme discovery
• Speech-to-Text - Analyze call recordings

ML & Probabilistic Methods

Sentiment Models

• XGBoost, LightGBM - Supervised sentiment classification
• Neural Networks - Deep learning for context
• Imbalanced Data Handling - SMOTE for rare emotions

Churn Integration

• Bayesian Networks - Model sentiment → churn causality
• Time-Series Analysis - Sentiment trends predict churn
• Monte Carlo Simulations - Quantify retention ROI

Infrastructure & Channels

Real-Time Processing

• Apache Kafka (streaming feedback)
• FastAPI (sub-second inference)
• Redis (feature caching)

Data & Integration

• PostgreSQL (feedback storage)
• CRM API integration (Salesforce, HubSpot)
• Contact center APIs (Genesys, NICE)

Why This Matters

→Churn Prevention: Identify at-risk customers from negative sentiment; intervene 60-90 days before departure
→Rapid Response: Find emerging issues in real-time; respond faster than competitors
→Data-Driven Service: Understand what drives satisfaction; prioritize improvements
→Operational Efficiency: Automate analysis; free teams to focus on resolution

Understand Your Customers Better

Turn feedback into actionable insights. Predict churn before it happens.

Schedule a Demo →

Quick Questions

How accurate is your sentiment classification?

Our fine-tuned models achieve 95%+ accuracy on financial services feedback. Accuracy varies by feedback type (structured surveys score higher than raw complaints).

Can you analyze call recordings?

Yes. We convert speech-to-text using industry-leading ASR models, then analyze sentiment. This includes meeting transcripts, customer service calls, and more.

How does this integrate with our CRM?

We provide REST APIs and pre-built connectors for Salesforce, HubSpot, Microsoft Dynamics. Sentiment scores feed directly into customer records and trigger workflows.

Can sentiment predict churn?

Yes. Combined with behavioral and transactional data, sentiment signals predict churn 60-90 days in advance with 85%+ accuracy.

What languages do you support?

We support 20+ languages including English, Spanish, French, German, Chinese, Japanese, and Arabic. Domain adaptation available for financial services terminology.

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