AI Workflow for Post-Outage Customer Satisfaction Surveys and Feedback Loops

Manual post-outage surveys are slow, have low response rates, and provide lagging insights. This workflow triggers a survey immediately after service restoration via the customer's preferred channel (SMS, email, IVR), capturing sentiment while the experience is fresh. It automates the collection of structured feedback (e.g., CSAT, communication clarity) and unstructured commentary, turning a cost center into a real-time quality monitor for field operations and customer communications teams.

Instead of manually reviewing every survey, an AI agent analyzes responses using sentiment classification and intent extraction. Negative feedback or mentions of critical issues (e.g., 'multiple outages,' 'unsafe condition') are automatically routed as a high-priority case to the customer relations or field supervision team within the CIS or CRM (e.g., Salesforce, ServiceNow). This ensures urgent concerns are addressed within SLA, preventing escalation and demonstrating responsive care.

The workflow aggregates and analyzes feedback data across outages, crews, and regions. It identifies recurring pain points (e.g., poor ETA accuracy, unhelpful communications) and correlates them with operational data from the OMS and workforce management system. This generates actionable dashboards for operations leadership, linking customer sentiment directly to process failures and enabling data-driven investments in grid hardening, crew training, or communication protocols.

The workflow fully automates survey triggering, distribution, and initial analysis, which is typically a manual, batch-process task for marketing or customer experience teams. By integrating directly with the Outage Management System (OMS) restoration feed and Customer Information System (CIS), it removes the labor of list generation, channel management, and basic reporting, freeing FTEs for higher-value intervention and analysis work.

By systematically capturing the customer perception of outage duration, communication, and restoration quality, this workflow creates a direct feedback link to formal reliability metrics like CAIDI (Customer Average Interruption Duration Index) and Customer Satisfaction (CSAT). It allows utilities to move beyond pure technical metrics and understand the experiential drivers of their public scorecard, enabling targeted improvements that protect regulatory standing and brand reputation.

For regulated utilities, demonstrating a systematic process for capturing and acting on customer feedback is critical. This workflow creates a complete, timestamped audit trail from outage event → survey delivery → response → analysis → escalation/resolution. This documented loop provides evidence of customer-centric operations during rate case proceedings or after major storm events, turning a compliance burden into a strategic asset.

This workflow automates the manual, often neglected process of post-outage surveying, directly linking restoration performance to customer sentiment. By systematically collecting and analyzing feedback, utilities can identify recurring pain points (e.g., poor communication, inaccurate ETAs) and drive measurable improvements in restoration processes, customer satisfaction scores (CSAT), and regulatory reliability metrics (e.g., CAIDI). The operational upside comes from converting passive data into actionable intelligence, reducing the risk of churn among dissatisfied customers, and demonstrating proactive service recovery to regulators.

The workflow is initiated by a restoration closure event from the Outage Management System (OMS). An orchestration agent (built with LangGraph or similar) receives the event, queries the Customer Information System (CIS) for the affected account's contact preferences and outage history, and triggers the survey sequence. This agent manages the entire stateful workflow, handling retries, channel fallbacks (SMS → Email), and handoffs to exception queues if the customer cannot be reached.

A specialized conversational AI agent conducts the survey via the customer's preferred channel (SMS, IVR, or in-app chat). It asks a short, adaptive set of questions (e.g., "How satisfied were you with the accuracy of restoration updates?"). The agent's responses are not static; it uses real-time sentiment analysis on open-text replies to probe deeper on negative feedback or thank customers for positive responses. This layer transforms a simple questionnaire into an intelligent dialogue that captures nuanced feedback.

Raw survey data and sentiment scores are processed by a triage agent. This agent classifies feedback using predefined rules (e.g., sentiment_score < 0.3 AND mentions 'communication'). Negative or complex cases are automatically routed to the appropriate operational queue: severe dissatisfaction to a customer relations team in Salesforce or ServiceNow, specific restoration complaints to the grid operations lead, and billing-related grievances back to the CIS dispute module. Each routed case includes the full interaction transcript and outage context.

Implementation requires a robust API orchestration layer connecting key systems:

1. OMS (e.g., Oracle Utilities, Schneider Electric) for restoration triggers.
2. CIS (e.g., SAP, Vertex) for customer profiles and preferences.
3. Communication Platforms (Twilio, Amazon Connect) for outbound SMS/IVR.
4. CRM/Case Management (Salesforce, ServiceNow) for feedback escalation.
5. Data Lake / BI Tool for aggregating sentiment trends and reporting. The architecture must ensure low-latency data access and maintain audit trails for all customer interactions to support compliance.

A production-grade build requires deliberate controls:

• Consent & Opt-Out Management: Integration with the CIS preference center to respect Do-Not-Contact flags.
• Survey Throttling: Rules to prevent contacting a customer multiple times for linked outages.
• Human-in-the-Loop Escalation: A dashboard for supervisors to monitor sentiment trends and manually pull cases for review.
• Performance Monitoring: Tracking metrics like survey completion rate, sentiment distribution, and case resolution time for the routed feedback. This ensures the workflow remains effective and compliant over time.

The workflow is initiated by a real-time trigger from the Outage Management System (OMS) confirming service restoration. An orchestration agent (e.g., using LangGraph) then queries the Customer Information System (CIS) for affected customers, filters by communication preferences, and initiates the survey sequence. This eliminates the lag and manual list-pulling that delays traditional feedback collection by days.

Specialized channel agents execute outreach via SMS, email, or IVR using customer-defined preferences from the CIS. They conduct short, structured surveys and capture open-ended voice or text feedback. All responses are ingested, transcribed (if voice), and structured into a unified data lake. This direct system integration ensures 100% survey delivery accuracy and captures sentiment at the moment of experience.

A core analysis agent processes all feedback using sentiment classification and entity extraction models. It flags negative sentiment, specific mentions of crew behavior, communication gaps, or prolonged outages. Based on configurable rules, it automatically routes severe cases to a human-in-the-loop queue in the CRM (e.g., Salesforce, ServiceNow) for immediate follow-up, while aggregating positive feedback for operational reporting.

The workflow's value is unlocked by bi-directional integration. Analyzed feedback is written back to the CIS customer profile as a service interaction. Trend data on outage causes or communication failures is pushed to the OMS and workforce management systems to inform process improvements. This creates a closed-loop system where customer feedback directly influences field operations and planning.

Every AI action—survey trigger, response, analysis, and routing—is logged with a full audit trail for compliance. A reporting agent synthesizes data into dashboards showing CSAT trends, root-cause themes, and restoration performance by crew or region. This provides defensible, real-time insights for regulatory reporting and continuous improvement programs, replacing manual survey analysis.

Deployment is phased, starting with a pilot segment of customers. The architecture typically involves a microservices layer hosting the orchestration logic, integrating via APIs or RPA bridges for legacy CIS/OMS systems. Critical to success is designing exception handling for failed deliveries and establishing clear escalation protocols for high-priority feedback before full-scale automation.