Skip to main content

CSAT

Overview

HiQ’s CSAT model transforms passive survey data into proactive insights, enabling teams to understand what drives customer satisfaction and uncover hidden pain points in real time. By combining numeric CSAT scores with AI‐powered analysis of free-text comments, HiQ delivers an integrated view of customer sentiment—surface issues early, align feedback with support history, and course-correct before small problems scale.

CSAT Analysis will:

  • Real-Time CSAT Scoring & Alerting
    Instantly flag negative survey responses and set custom alerts when DSAT rates climb beyond defined thresholds.

  • AI-Driven Text Analysis
    Automatically parse open-text comments to detect root causes—whether it’s scam concerns, automation frustration, or usability issues.

  • Pattern Detection Across Dimensions
    Slice and dice satisfaction trends by tag, team, channel, or customer segment to pinpoint recurring service breakdowns.

  • Predictive Dissatisfaction Tracking
    Surface emerging negative trends before they escalate into churn or support load spikes.

  • Contextual Alignment with QA & Ticket Data
    Merge survey feedback with ticket metadata and QA scores for a 360° view of each interaction.

What Is Survey Satisfaction Analysis?

HiQ’s Survey Satisfaction Analysis ingests both numerical CSAT ratings and open-text feedback to surface actionable insights:

  • Real-Time Score Monitoring. Track CSAT across tags, teams, and contact types as responses arrive—spotting negative shifts before they escalate.

  • AI-Powered Comment Analysis. Automatically extract themes and sentiment from free-text feedback to identify root causes of dissatisfaction.

  • Contextual Correlation. Align survey data with QA outcomes and ticket metadata for a 360° view of customer experience.

This holistic approach transforms surveys from a lagging indicator into an early warning system for CX teams.

 

Key Benefits of CSAT Analysis

  • Active Feedback Loop
    Turn passive CSAT data into strategic signals—triggering reviews, coaching, or process changes in response to real-time insights.

  • Early Risk Detection
    Detect negative trends and high-risk themes immediately, reducing potential churn and support escalations.

  • Deeper Insight with AI
    Uncover hidden pain points across interactions through automated comment analysis, beyond what numeric scores alone can reveal.

  • Total Visibility
    Align survey responses with QA results and ticket histories for complete context—ensuring your team acts on accurate, actionable data.

 

How It Works

 

    1. Analyze Low CSAT Feedback Automatically
      AI highlights and categorizes negative survey scores, extracts sentiment from free-text comments, and links each comment to common issue themes.

    2. Identify Patterns Across Tags, Teams, or Channels
      Filter satisfaction data by contact reason, department, or channel to surface systemic issues and prioritize corrective action.

    3. Surface Emerging Risk Areas Early
      Continuously monitor DSAT rates and trigger alerts when certain tags or segments show an upward trend in dissatisfaction.

    4. Align Survey Data with QA & Ticket History
      Enrich survey feedback with ticket metadata, tag history, and QA outcomes—providing full context for each customer interaction.

 

 

 

 

 

 

 

Was this article helpful?