Manufacturing
Logistics
Real Estate
Retail
Finance
Healthcare
Hospitality
Automotive
The Customer Support Sentiment Analysis Agent, developed by ZBrain, helps organizations uncover insights hidden within large volumes of customer support interactions. Support teams manage thousands of conversations across chat, email and phone, but the valuable feedback buried in these exchanges often goes unanalyzed. This creates blind spots where early signs of dissatisfaction or recurring issues are missed, limiting opportunities to improve the customer experience.
The agent addresses this challenge by continuously analyzing support transcripts and categorizing them through sentiment-driven reporting. Using large language model (LLM) capabilities, it interprets tone, emotion and context to surface key themes – ranging from recurring product complaints to moments when service exceeds expectations. Unlike keyword-based analysis, it can detect frustration, urgency or satisfaction even in subtle expressions, providing a more accurate and nuanced understanding of customer sentiment.
The result is a sharper understanding of customer sentiment that drives proactive improvement. Organizations can identify issues before they escalate, coach support agents with sentiment insights and continuously refine service delivery. By transforming fragmented conversations into structured intelligence, the Customer Support Sentiment Analysis Agent reduces churn risk, builds loyalty and equips teams with a real-time pulse on the customer experience.
Accuracy
TBD
Speed
TBD
Input Data Set
Sample of data set required for Customer Support Sentiment Analysis Agent:
Request: Generate a Customer Service Interaction Analysis Report for August 2025.
Deliverable Example
Sample output delivered by the Customer Support Sentiment Analysis Agent:
Customer Service Interaction Analysis Report
Company: Acme Tech Solutions (U.S. Operations)
Agent: Customer Support Sentiment Analysis Agent
Period Analyzed: August 1–31, 2025
1. Executive Summary
- Overall sentiment stable, with a 2% drop in negatives MoM.
- Billing remains the #1 driver of dissatisfaction, up 15% MoM.
- Live chat continues to outperform other channels in satisfaction.
- Two agents received outstanding recognition; one flagged for retraining.
2. Sentiment Trends (MoM)
| Month | Positive | Neutral | Negative | Volume |
|---|---|---|---|---|
| July 2025 | 52% | 22% | 26% | 4,900 |
| August 2025 | 54% | 23% | 23% | 5,005 |
Insight: Gains in live chat satisfaction offset higher billing frustrations.
3. Overall Sentiment Breakdown (August 2025)
| Sentiment Category | % of Interactions | Volume of Interactions |
|---|---|---|
| Positive | 54% | 2,710 |
| Neutral | 23% | 1,155 |
| Negative | 23% | 1,140 |
4. Channel-Level Breakdown
| Channel | Positive | Neutral | Negative | Key Highlights |
|---|---|---|---|---|
| Zendesk Chat | 64% | 21% | 15% | Fast resolution praised. |
| Salesforce Email | 40% | 40% | 20% | Lack of personalization flagged. |
| Amazon Connect | 42% | 30% | 28% | Wait times biggest driver of negatives. |
5. Segment Insights
- Enterprise Clients: Higher billing concerns, but positive on proactive outage comms.
- SMB Clients: Stronger chat satisfaction, but voiced dissatisfaction with call queues.
6. Top Themes from Negative Sentiment
| Theme | Frequency | Example Snippet |
|---|---|---|
| Delayed Issue Resolution | 420 cases | “It took three follow-ups to get my case escalated.” |
| Billing Discrepancies | 360 cases | “The invoice still shows last quarter’s rate despite adjustments.” |
| Technical Outages | 260 cases | “The platform was down during peak hours again.” |
| Agent Knowledge Gaps | 100 cases | “The rep couldn’t explain the new policy clearly.” |
Root Cause Analysis: Billing discrepancies traced to outdated ERP sync; resolution delays linked to Tier-1 escalation bottlenecks; knowledge gaps tied to new compliance policy rollout.
7. Positive Recognition Highlights
- Customers praised live chat agents for fast resolutions (“I got an answer in under 5 minutes!”).
- Several corporate clients appreciated proactive outage notifications, reducing frustration compared to July.
- Agent Jennifer Lee (Support Tier 2) was mentioned by name in 42 positive cases, most often for professionalism and empathy.
8. Agent Performance Dashboard
| Agent | Recognition Cases | Negative Mentions | Action |
|---|---|---|---|
| Jennifer Lee | 42 | 0 | Recognition |
| Maria Torres | 25 | 3 | Recognition |
| Mark Daniels | 0 | 15 | Training |
| Others (Top 10%) | 120 combined | 5 combined | Recognition |
9. Proactive Recommendations
- Risk: Billing dissatisfaction may spike further in Q4 if ERP alignment isn’t fixed.
- Opportunity: Scaling live chat hours could improve overall sentiment by ~5%.
- Action: Prepare September “fast-track billing resolution” workflow.
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