Predict CSAT from Support Interactions in Real Time

Q: What data do we need to start?

Ticket/chats/call transcripts, resolution codes, CSAT labels (historical), and case metadata. Optional: CRM attributes for richer context.

Q: How is prediction accuracy measured?

Benchmarked on a hold-out set against actual CSAT (e.g., AUC, calibration error) with continuous drift monitoring in production.

Q: Will AI replace agents or supervisors?

No. AI augments teams by surfacing risks and recommended actions; humans approve and coach, especially for low-confidence cases.

Q: How do we prevent bias?

We remove sensitive attributes, evaluate subgroup performance, and use explainability to understand drivers behind each prediction.

Executive Summary

Predictive CSAT models analyze language, tone, escalation paths, first-contact resolution, and agent behaviors to forecast satisfaction before the survey arrives. By unifying support data with customer context, teams prioritize saves, trigger make-good offers, and coach agents in the moment—compressing 12–22 hours of manual analysis into 2–3 hours while protecting retention and advocacy.

How Does AI Predict Satisfaction from Support Data?

AI converts every message and call into features (sentiment trajectory, effort signals, resolution depth). Models forecast CSAT at the case and account level, then recommend targeted actions—before dissatisfaction hardens.

TPG configures agents that watch live conversations, detect risk patterns (repeat contacts, negative sentiment rebounds), and route playbooks (supervisor assist, proactive credit, knowledge fix) to the right owner with SLAs.

What Changes with Predictive CSAT?

🔴 Manual Process (12–22 Hours, 9 Steps)

Collect interaction data from systems
Correlate behaviors to satisfaction
Build predictive model concepts
Validate on samples & tests
Implement scoring & reports
Monitor accuracy & drift
Refine features & thresholds
Compile stakeholder reporting
Continuous improvements

REACTIVE, LABOR-INTENSIVE ANALYSIS

🟢 AI-Enhanced Process (2–3 Hours, 4 Steps)

Real-time CSAT prediction & account risk assessment (1–2h)
Automated defense strategy & next-best-action (30–60m)
Proactive intervention implementation (30m)
Performance monitoring & model optimization (15–30m)

≈88% TIME SAVINGS • UP TO 87% PREDICTION ACCURACY

TPG standard practice: Keep raw transcripts for auditability, require human approval on low-confidence actions, and close the loop by tying interventions to CSAT lift and churn avoidance.

Key Metrics to Track

Prediction Accuracy

CSAT forecast precision vs. actuals

At-Risk Detection

% of low-CSAT cases flagged early

Resolution Satisfaction

Post-intervention CSAT improvement

Time to Intervention

Minutes from risk signal to action

Measure not just accuracy—prove value by linking early interventions to CSAT lift, repeat contact reduction, and decreased escalations.

Which AI Tools Power This?

Chattermill

Unifies tickets, chats, and reviews; advanced sentiment & driver analysis with CSAT correlation.

Brandwatch

Surfaces public conversation signals that correlate with support experience and reputation.

Qualtrics AI

Predictive text analytics and closed-loop CX workflows tied to CSAT/NPS outcomes.

Integrate with your operations stack (CRM, help desk, data warehouse) to score in-flight cases and trigger actions automatically.

Implementation Timeline

Phase	Duration	Key Activities	Deliverables
Assessment	Week 1–2	Map data sources (tickets, chat, calls), define CSAT labels & outcomes, set risk thresholds	Predictive CSAT design & metrics spec
Integration	Week 3–4	Connect Chattermill/Brandwatch/Qualtrics AI; normalize transcripts & metadata	Unified scoring pipeline
Training	Week 5–6	Train models on history; calibrate confidence bands; define action playbooks	Brand-tuned models & playbooks
Pilot	Week 7–8	Score live cases; validate accuracy & time-to-intervention	Pilot readout & SOPs
Scale	Week 9–10	Roll out across queues/regions; enable auto-triggers & coaching	Production deployment
Optimize	Ongoing	Monitor drift, refine features, expand coverage	Continuous improvement

Frequently Asked Questions

What data do we need to start?

Ticket/chats/call transcripts, resolution codes, CSAT survey labels (historical), and case metadata (priority, product, channel). Optional: CRM attributes for richer context.

How is prediction accuracy measured?

We benchmark on a hold-out set against actual CSAT using metrics like AUC and calibration error, then monitor live performance with drift alerts.

Will AI replace agents or supervisors?

No. AI surfaces risks and suggested actions; humans decide and coach. Low-confidence cases require review before any proactive outreach.

How do we prevent bias?

We remove sensitive attributes, test subgroup performance, and add fairness constraints where needed. Explanations show which signals influenced each score.