SuperSAT – HiOperator Support

Overview

SuperSAT is the AI-powered predictive CSAT tool within the HiQ platform by HiOperator, designed to forecast and improve customer satisfaction with every interaction. From pinpointing potential dissatisfaction to optimizing service recovery, SuperSAT empowers your team to create exceptional customer experiences—proactively.

With SuperSAT, you can:

Dynamic Satisfaction Scoring
Calculate a continually updated CSAT score by analyzing customer and agent messages for tone, tracking key resolution steps (acknowledgment, proposal, closure), and factoring in reply speed.
At-Risk Conversation Detection
Automatically flag tickets that show downward sentiment trends or stalled resolution progress, so you can intervene before a DSAT outcome.
Real-Time VoC Insights
View satisfaction trends by tag, channel, or time period to uncover systemic issues and benchmark performance against past periods.
Advanced Survey Integration
Augment in-thread scoring with conditional follow-up surveys—trigger questions only when CSAT dips below a threshold, offer end-of-survey incentives, and support multiple languages.

Key Benefits of SuperSAT

Proactive Intervention
Step into negative interactions early, reducing escalations and preventing DSAT.
Improved Customer Experience
Continuous CSAT monitoring drives faster resolutions and more positive outcomes.
Data-Driven Coaching
Identify agent behaviors that correlate with satisfaction dips and target training where it matters.
Operational Efficiency
Focus resources on the tickets and categories that pose the greatest risk to overall satisfaction.
Data-Driven Coaching & Agent Development
Identify which agents or scripts correlate with dips in satisfaction. Use granular CSAT trend data to design personalized training programs—improving average agent CSAT

How SuperSAT Works

SuperSAT’s power comes from its ability to blend NLP, ticket–lifecycle signals, and real-time analytics into a single, continuously updating satisfaction score. Here’s a deeper look at each stage of the process:

1. Message Ingestion & Pre-Processing

Real-Time Streaming: As soon as a customer or agent message is posted, it’s routed into superSAT’s processing pipeline.
Normalization & Tokenization: Text is cleaned (HTML stripped, emojis parsed, typos normalized) and broken into tokens for the sentiment and intent models.
Metadata Attachment: Each message carries context—agent ID, customer history, channel (email, chat, social)—so scores can be correlated with source and past performance.

2. Sentiment & Emotion Analysis

Multi-Dimensional Sentiment: Rather than a simple positive/negative binary, superSAT uses a fine-grained classifier to score each message on a –1 to +1 scale, capturing degrees of satisfaction or frustration.
Emotion Detection: An additional layer of modeling spots emotional cues (e.g., “angry,” “confused,” “pleased”) which feed into the overall CSAT trajectory.
Confidence Scoring: Every sentiment/emotion result includes a confidence metric, ensuring only high-certainty signals influence critical risk-detection thresholds.

3. Resolution Progress Tracking