What is a Predictive CSAT Score?

A predictive CSAT score estimates how satisfied a customer was without needing a survey. It uses LLM-powered classifiers to detect experience signals like resolution, sentiment, and understanding, turning raw conversation data into consistent insights.

Unlike traditional CSAT, this score works across every interaction, whether it's handled by a bot, a human, or both.

Why Teams Are Moving Beyond Survey-Only Feedback

CSAT response rates are low. Survey feedback often arrives too late. And it only captures a fraction of what's really happening.

As support operations scale and automation expands, teams need a way to understand customer experience in real time. Predictive CSAT closes that gap, providing high-signal feedback from every conversation, not just the ones with surveys.

Why Predictive CSAT Matters

A predictive CSAT score creates a shared, actionable metric. It enables:

✔ Consistent comparison across bots and humans
✔ Early detection of at-risk interactions
✔ Smarter prioritization for QA, coaching, and escalation

It doesn't replace surveys or manual QA, but it fills in the blind spots, making experience measurable at scale.

The Goal: Predictive CSAT Score for Every Interaction

The goal is to generate a consistent AI score that estimates customer satisfaction across 100% of conversations,whether handled by a bot, a human, or both.

Predictive CSAT doesn't replace surveys or QA reviews, it enhances them. Built using AI classifiers that analyze what actually happened in each interaction, this score gives you a scalable way to understand customer experience without relying solely on survey responses.

It helps teams:

Fill in feedback gaps when surveys are missing
Compare AI and human interactions using a shared metric
Align teams around the same actionable experience signals

Powered by Real Signals from LLM Classifiers

At the core of predictive CSAT are LLM-powered classifiers trained to detect qualitative outcomes:

Was the issue resolved?
Did the customer feel understood?
Did the customer express frustration?
Did the agent,or bot,understand the problem?

These signals turn messy conversation data into structured, trackable insights, ready to be aggregated into a single experience score.

Not Just a Score, A Driver of Action

The predictive CSAT score isn't just for tracking trends, it enables action. By identifying at-risk interactions that may lead to dissatisfaction, teams can step in earlier, prioritize coaching, or flag tickets for follow-up before a DSAT survey ever gets submitted.

It also enables strategic comparisons: How does chatbot performance stack up against human agents on key experience dimensions? Where is escalation most effective or most frustrating?

Phase 1: Build Your Experience Classifiers

To create a predictive CSAT score, the first step is defining the key signals that reflect customer experience and training AI to detect them. This begins with building LLM Classifiers (AI classifiers powered by large language models) to evaluate the qualitative aspects of each conversation.

What Are LLM Classifiers

LLM Classifiers are designed to evaluate complex, qualitative aspects of a conversation. Unlike rule-based classifiers that focus on things like handle time or number of touches, LLM Classifiers are designed to evaluate tone, understanding, resolution, and satisfaction, directly from the conversation text.

Examples of questions these classifiers might answer include:

Did the customer feel understood?
Was the tone of the conversation positive or negative?
Was the customer's issue resolved?

These signals are the building blocks of your predictive CSAT score.

Step 1: Select and Approve Your LLMs

Before building classifiers, choose which large language models you'll use. Different LLMs may perform better for different types of evaluation tasks.

Consider testing:

GPT-4 for complex reasoning tasks
Claude for nuanced sentiment analysis
Other models based on your platform's availability

Key Decision: You may use different LLMs for different classifiers based on performance, or standardize on one model for consistency.

Step 2: Define the Key Experience Dimensions

Define the core experience dimensions that matter most to customer satisfaction. A proven framework focuses on four dimensions:

Each dimension will include 2-4 specific classifiers, typically resulting in 8-15 total classifiers.

Step 3: Create Classifier Prompts for Each Dimension

For each dimension, create specific prompts that guide the LLM to evaluate interactions consistently. Here are examples:

Comprehension:

Was the customer's issue fully and correctly understood?
Did the agent or bot acknowledge knowledge gaps?

Sentiment:

Did the customer express frustration during the conversation?
Classify the customer's emotional state.

Resolution:

Was the issue resolved or escalated appropriately?
Did the customer request a human agent?

Satisfaction:

Was the customer delighted by the support provided?
What was the customer's sentiment after the resolution was offered?

Important: Customize these prompts based on your specific support model (bot vs. human, escalation workflows, etc.).

Step 4: Build Initial Classifiers

Using your AI platform, create classifiers for each prompt. Start with clear, specific language, knowing you'll refine based on performance testing.

Expect iteration: Your initial classifiers are starting points. Plan for multiple rounds of refinement based on accuracy testing.

Phase 1 Completion Checklist:

✔️ LLM selection completed and approved

✔️ Core experience dimensions defined (typically 4 dimensions)

✔️ 8-15 classifier prompts created across all dimensions

✔️ Initial classifiers built in your AI platform

✔️ Test dataset prepared for accuracy evaluation

✔️ Ready to begin iterative accuracy testing

Phase 2: Test and Refine Classifier Accuracy (Iterative Process)

This phase involves continuous testing and refinement of your classifiers. Unlike a linear process, expect multiple rounds of testing, prompt adjustments, and retesting until each classifier meets your accuracy requirements before combining classifiers into a predictive score.

Step 1: Test Classifier Performance

Test each classifier against a human calibration. For each classifier, evaluate:

Accuracy: How often does it match human judgment?
Consistency: Does it behave reliably across different ticket types?
Coverage: How often does it return "unknown" or "not enough information"?

Don't expect perfect results initially. Some classifiers will perform well immediately, others will need significant refinement.

Step 2: Set Accuracy Targets

Establish minimum accuracy thresholds, understanding that different types of classifiers have different performance expectations:

Behavioral/Factual classifiers: 85%+ accuracy (e.g., "Did customer ask for human?")
Sentiment detection: 75-85% accuracy (emotional state, frustration detection)
Complex satisfaction assessment: 65-75% accuracy (customer delight, overall satisfaction)

Some classifiers may consistently underperform these targets and may need to be removed or significantly redesigned.

Step 3: Identify and Address Performance Issues

Common Issues and Solutions:

High "Unknown" or "Not Enough Information" Rates

Simplify overly complex prompts
Add more conversation context to the classifier
Consider if the conversation data provides sufficient signals

Inconsistent Results Across Ticket Types

Test classifier performance by ticket category
Adjust prompts to handle edge cases
Consider separate classifiers for different interaction types

Low Overall Accuracy

Try different LLMs for problematic classifiers
Revise prompt language based on common misclassifications
Expand test coverage to include more scenarios

Step 4: Plan for Ongoing Refinement

Expect Multiple Iterations: Some classifiers will require several rounds of testing and refinement. Document which classifiers need additional work and plan accordingly.

Common Refinement Needs:

"More test cases needed" for reliable evaluation
Prompt language adjustments based on false positives/negatives
LLM selection changes for specific classifiers

Only advance classifiers that meet your accuracy targets. It's better to have fewer, reliable classifiers than many unreliable ones.

Phase 2 Completion Checklist:

✔️ All classifiers tested against representative dataset

✔️ Accuracy targets defined and documented

✔️ Underperforming classifiers refined through multiple iterations

✔️ Classifiers requiring additional test cases identified and improved

✔️ Only reliable classifiers approved for score calculation

✔️ Performance documentation ready for weighting decisions

Phase 3: Combine Classifiers into a Predictive Score

Once your classifiers show reliable performance, combine them into a single predictive CSAT score. Start simple and evolve based on performance data.

Step 1: Choose Your Scoring Approach

Option A: Unweighted Average

All classifiers contribute equally to the final score
Simple to implement and explain
Good starting point when classifier accuracy varies

Option B: Weighted Score

Classifiers receive different weights based on performance or business priority
More complex but potentially more accurate
Requires clear weighting methodology

Recommended Starting Point: Use an unweighted average of all approved classifiers.

Why Start Simple:

Easier to implement and explain to stakeholders
Provides baseline performance for comparison
Avoids complexity when classifier accuracy is still stabilizing
Allows you to see overall performance before optimizing weights

Step 2: Determine Weighting Strategy (If Using Weighted Approach)

When to Consider Weighting

After you have stable baseline performance with unweighted approach
When you have clear data on which classifiers perform best
If unweighted approach shows issues that weighting could address

Potential Weighting Approaches

Accuracy-Based Weighting:

Higher accuracy classifiers receive more weight
Reduces impact of less reliable signals
Data-driven and objective

Business Priority Weighting:

Weight dimensions based on business impact
Emphasize experience factors most tied to retention/satisfaction
Requires stakeholder alignment on priorities

Hybrid Approach:

Combine accuracy and business priority factors
Balance model performance with strategic importance

Step 3: Implement and Document Scoring Logic

Create transparent, documented logic for your predictive CSAT calculation:

Ensure the scoring methodology is clearly documented for stakeholders across QA, CX, and operations teams.

Technical Implementation

Add scoring calculation to your QA platform
Ensure the logic is clearly documented and auditable
Build in flexibility to adjust weighting as you learn more

Documentation Requirements

Which classifiers are included and why
How the final score is calculated
What the score represents (and doesn't represent)

Step 4: Prepare for Score Validation

Before deploying widely, prepare for validation testing:

Ensure your scoring logic is working correctly
Document the methodology for stakeholder review
Prepare to gather correlation data against actual CSAT

Phase 3 Completion Checklist:

✔️ Scoring approach selected (recommend starting unweighted)

✔️ Scoring logic implemented in your platform

✔️ Score calculation tested and validated

✔️ Documentation created for stakeholder reference

✔️ Baseline performance established

✔️ Ready for correlation testing against real CSAT data

Phase 4: Calibrate Against CSAT

The final validation step is testing how well your predictive score correlates with actual customer satisfaction. This calibration process ensures your score reliably reflects real customer sentiment.

Step 1: Build Your Test Dataset

Gather interactions that include both:

Your predictive CSAT score
Actual CSAT survey responses from customers

Aim for a clean dataset of 200+ interactions with both predictive scores and survey responses.

Step 2: Measure Correlation

Calculate the correlation between your predictive score and actual CSAT responses. A strong correlation (typically 70%+ for initial implementations, 75%+ for mature models) indicates your score reliably predicts customer satisfaction.

Step 3: Identify and Fix Misalignment

If correlation is below target, analyze patterns:

High Predictive Score, Low CSAT (False Positives):

Which classifiers are overestimating satisfaction?
Are technical resolutions being confused with customer satisfaction?

Low Predictive Score, High CSAT (False Negatives):

Which classifiers are underestimating satisfaction?
Are you missing important satisfaction signals?

Step 4: Iterate and Rebalance

Based on correlation analysis:

Adjust classifier weights to reduce noise
Refine prompts for better accuracy
Remove classifiers that hurt correlation
Add new classifiers to fill experience gaps

Step 5: Validate Final Performance

Continue testing until you achieve consistent correlation at your target threshold. Document final performance and establish ongoing monitoring.

Phase 4 Completion Checklist:

✔️ Clean test dataset created (200+ interactions with both scores)

✔️ Correlation analysis completed

✔️ Target correlation threshold achieved (typically 70-75%+)

✔️ Final weighting and classifier adjustments documented

✔️ Ongoing monitoring process established

✔️ Ready for full production deployment

Real World Example: Predictive CSAT Score Implementation

The Challenge

A leading healthtech company partnered with MaestroQA to implement a Universal Member Experience Score (Predictive CSAT) to measure customer satisfaction across both AI chatbot and human support interactions. Their goal was to create consistent experience measurement across all customer touchpoints.

Project Goals:

Establish a Universal Member Experience Score n MaestroQA for all customer contacts
Achieve 75% correlation with actual CSAT responses for validation
Enable comparison between bot and human interaction performance

Phase 1: Classifier Development

Working within MaestroQA's AI platform, the team developed classifiers across four experience dimensions.

Experience Dimensions Selected

Comprehension (3 classifiers)
Sentiment (3 classifiers)
Resolution (3 classifiers)
Satisfaction (4 classifiers)

Total Classifiers Built: 13 specific LLM-powered classifiers

Specific Classifiers Implemented:

"Was the customer's issue fully and correctly understood?"
"Did the bot say it has knowledge gaps?"
"Did the customer express frustration about the quality of service they are receiving?"
"Classify the customer emotion"
"Did the customer express frustration with the Bot?"
"Did the customer ask for a human?"
"Was the customer's Issue resolved or properly escalated"
"Escalated Bot Chats: did the bot struggle with troubleshooting?"
"Was the customer delighted by the support provided?"
"Assess the overall satisfaction level expressed by the customer"
"Customer Sentiment After Solution Analysis"
"Issue Identification and Understanding Assessment"
"Agent contributes to Negative CSAT score"

Phase 2: Accuracy Testing Results

Using MaestroQA's prompting, testing, and evaluation capabilities in AI Platform, the team achieved the following accuracy results.

High-Performing Classifiers:

"Classify the customer emotion" - 100% accuracy
"Did the bot say it has knowledge gaps?" - 80-97% accuracy
"Did the customer ask for a human?" - 84-90% accuracy

Moderate-Performing Classifiers:

"Did the customer express frustration about the quality of service?" - 79-89% accuracy
"Did the customer express frustration with the Bot?" - 89-98% accuracy
"Was the customer's issue fully and correctly understood?" - 75% accuracy
"Assess the overall satisfaction level expressed by the customer" - 82-100% accuracy

Classifiers Requiring Additional Work:

"Was the customer delighted by the support provided?" - 65-81% accuracy
"Escalated Bot Chats: did the bot struggle with troubleshooting?" - 57-92% accuracy

Phase 3: Scoring Approach

Key Decision Point: Whether to use weighted or unweighted classifier combination

Considerations Under Review:

Weighted approach based on classifier accuracy (more accurate classifiers receive higher weight)
Alternative weighting based on business priorities
Starting with unweighted approach for initial implementation

Phase 4: Validation Requirements

Success Criteria: Achieve 75% correlation to CSAT for cleaned dataset of CSAT/DSAT QA evaluated interactions

Planned Methodology:

Generate clean dataset of interactions with both predictive scores and actual CSAT/DSAT responses
Use MaestroQA's analytics capabilities to evaluate correlation between Universal Member Experience Score and survey results
Refine classifier weighting in MaestroQA based on correlation analysis to optimize alignment

MaestroQA Platform Allowed:

Integrated AI classifier development
Centralized testing and validation of classifier accuracy
Built-in dashboards for real-time data monitoring
Seamless integration with existing QA workflows and CSAT data

💡 This was one phase of this health tech brand’s initiative to expand QA across bot and agent support models. Read about their journey in our Blueprint for QA Across Bot and Agent Support Models Guide.

Applications and Next Steps

Once your predictive CSAT score is calibrated and live, put it to work across quality programs, operations, and strategic initiatives. This score becomes a flexible, high-signal input for smarter decisions across your support organization.

1. Monitor Experience at Scale

Apply the score across 100% of tickets to track overall experience trends, providing a more complete picture than CSAT surveys alone.

2. Compare Performance Across Channels

Use consistent experience signals to compare automated and human-assisted interactions, identifying where each channel excels.

3. Prioritize QA and Coaching

Focus review and coaching efforts on low-scoring interactions, especially those without CSAT responses.

4. Enable Early Intervention

Surface at-risk conversations before customers submit negative feedback, enabling proactive follow-up and issue resolution.

5. Drive Continuous Improvement

Use the score to guide ongoing refinement of support processes, agent training, and automation workflows.

Turning Signals into Strategy

Predictive CSAT brings new depth to customer experience understanding—filling survey gaps, surfacing issues early, and unifying insights across all support channels. But a score is only as powerful as what you do with it.

With MaestroQA, you're not just generating experience signals, you're turning them into strategy:

AI Platform: Build, test, and refine LLM-powered classifiers tailored to your business
AutoQA: Run your predictive CSAT model across 100% of interactions at scale
Performance Dashboards: Track trends, surface patterns, and guide continuous improvement
Actionable Insights: Take action through coaching, bot optimization, policy changes, etc

Whether optimizing bot experiences, guiding coaching, or intervening before customer churn, predictive CSAT becomes a force multiplier when integrated into your broader quality program.

🚀 Ready to see it in action? Book a demo today

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Predictive CSAT Playbook: How to Roll Out a Predictive CSAT Score