Automating customer retention workflows in Amazon Quick

<p>Automating customer retention workflows in Amazon Quick can turn a five-day churn-response cycle into one that takes minutes. Last quarter, a mid-size SaaS company lost 12% of its at-risk accounts because the retention team took five days to identify and contact dissatisfied customers. By the time someone manually reviewed CSAT spreadsheets and call transcripts, those customers had already churned. Amazon Quick shortens that response window from days to minutes.</p> <p>This post walks through building an automated retention pipeline in Amazon Quick. The pipeline detects dissatisfied customers from structured data. It analyzes sentiment from call transcripts and scores customers by retention priority. It then generates retention offers tailored to each situation. In this post, you learn how to:</p> <ul> <li>Configure a Quick Space with contact center datasets and call transcripts.</li> <li>Create and register a custom MCP Action for customer scoring.</li> <li>Build a Chat Agent that combines quantitative key performance indicators (KPIs) with qualitative transcript analysis.</li> <li>Convert the analysis into a reusable Quick Flow.</li> <li>Orchestrate the full pipeline with Amazon Quick Automate.</li> </ul> <h2 id="solution-overview">Solution overview</h2> <p>The retention pipeline connects four Amazon Quick components in sequence. Quick Dashboard monitors contact center KPIs, including CSAT (Customer Satisfaction Score, a 1–5 rating customers give after each call), FCR (First Call Resolution, whether the team resolved the issue in a single call), and AHT (Average Handle Time). It identifies customers with CSAT scores at or below 2.</p> <p>Quick Chat Agent uses natural language to query structured data and unstructured call transcripts. It combines quantitative scores with qualitative sentiment signals. This surfaces why a customer is at risk, not just that they are.</p> <p>Amazon Quick Flows turns the repeatable Chat-based analysis into a scheduled or on-demand automation that runs without manual intervention. Its final step formats the results as a structured list of at-risk customers that downstream automation can consume directly.</p> <p>Quick Automate executes a multi-step pipeline. It ingests the structured at-risk customer list that the Flow produces and scores those customers through a custom MCP Action. An MCP Action is a serverless endpoint that extends Quick Automate with custom business logic. Refer to the <a href="https://docs.aws.amazon.com/quick/latest/userguide/mcp-integration.html" target="_blank" rel="noopener">Quick Automate MCP developer guide</a> for details. The pipeline then ranks customers by priority, generates retention letters, and uploads them to Amazon Simple Storage Service (Amazon S3) for distribution.</p> <p>You run the workflow on AWS infrastructure and can access audit trails, breakpoints, and role-based access controls.</p> <h3 id="automate-pipeline-architecture">Automate pipeline architecture</h3> <p>The pipeline executes five sequential steps. It downloads the list of at-risk customers and contact center data from Amazon Simple Storage Service (Amazon S3). A scoring step ranks those customers by retention priority (based on CSAT and how recently each customer had an issue) and picks the top two highest-priority cases. The pipeline then drafts bonus-credit letters that reference each customer’s specific issues, saves them as a PDF, and uploads the finished letters to Amazon S3 for delivery and archival.</p> <p>The following table summarizes each step:</p> <table border="1px" width="100%" cellpadding="10px"> <tbody> <tr> <td><strong>Step</strong></td> <td><strong>Action</strong></td> <td><strong>Details</strong></td> </tr> <tr> <td>1</td> <td>Download at-risk customers and contact center dataset</td> <td>Download the at-risk customers file (Negative Sentiment.doc) as well as the structured dataset contact_center_data.csv from Amazon