A mid-size SaaS company lost 12% of its at-risk accounts last quarter because its retention team took five days to identify and contact dissatisfied customers. Amazon Quick shortens that response window from days to minutes by automating a retention pipeline. The pipeline detects dissatisfied customers from structured data, analyzes sentiment from call transcripts, and scores customers by retention priority. It uses a Quick Dashboard to monitor KPIs including CSAT (a 1–5 rating), FCR, and AHT, identifying customers with CSAT scores at or below 2. A Quick Chat Agent combines quantitative scores with qualitative sentiment signals. Amazon Quick Flows turns the analysis into scheduled or on-demand automation, formatting results as a structured list of at-risk customers. Quick Automate executes a multi-step pipeline that ingests the list, scores customers through a custom MCP Action, ranks them by priority, generates retention letters, and uploads them to Amazon Simple Storage Service (Amazon S3). The pipeline executes five sequential steps, including scoring based on CSAT and recency of issues, and picks the top two highest-priority customers.