AI Agents

Context

Built for banking within the Customer churn & retention stack, this agent is used when retention teams need to understand why customers are at risk or dissatisfied. Typical scenarios include rising churn signals on deposits, cards, or consumer lending; recurring complaints on specific journeys (onboarding, servicing, disputes); and relationship reviews where interventions must be tied to the actual pain point.

What it does

The agent analyzes recent customer interactions—complaints, service tickets, call/chat transcripts, NPS verbatims, and short-form feedback—and classifies the root cause behind dissatisfaction or attrition risk. It assigns clear labels (e.g., billing issue, card decline experience, digital access, dispute handling) and extracts the specific drivers from the text, linking them to the relevant products and steps in the journey. Findings are returned with short, plain-language rationales and pointers to the exact sentences or events that support the classification, so teams can select the right remediation without rereading entire threads.

Core AI functions

Multichannel text ingestion (cases, transcripts, messages); domain-tuned classification to map narratives to root-cause categories; sentiment and intensity scoring to gauge urgency; key-phrase and event extraction to surface concrete drivers (fees, outages, declines, delays); entity linking to products, channels, and journey stages; and reason-code generation that cites the lines of evidence used. Confidence scoring routes low-certainty cases to review with targeted prompts.

Problem solved

Complaint text and feedback are scattered and unstructured. Teams spend cycles reading and guessing at causes, which delays action and leads to generic responses that don’t address the real issue.

Business impact

Retention actions become more precise and effective because they’re tied to explained causes, not guesses. Resolution shortens, repeat contacts drop, and customer experience improves—supporting higher retention, better retention actions, and improved NPS.

Integration and adjacent use cases

Integration is light–moderate: read complaint/service data, transcripts, and feedback from your CRM/contact-center and data warehouse; write root-cause labels, reasons, and evidence links to CRM or journey orchestration—no core changes required.

Common combinations in this stack:

• Churn signal extractor (to prioritize who needs attention),

• Document-driven feedback & closure validator (to ensure required communications and closure evidence are complete), and

• Churn retention exception handler (to route complex or policy-sensitive saves with full context).