Quick Suite Analytics

Quick Suite is AWS’s unified business intelligence platform that combines interactive dashboards, AI-powered analytics agents, and automated workflows to help organizations move from insight to action in a single integrated experience.

Value proposition

Dashboards show what happened, but they can’t tell you why or what to do next—that still requires hours of manual investigation. Quick Suite goes beyond visualization by:

Instead of manually sifting through data and coordinating across departments, agentic workflows instantly generate reports, create approval tasks, and notify the right people via Slack—all without writing code. What used to take days of cross-team coordination now happens in minutes, with built-in human approval checkpoints that keep you in control.

Building dashboards

Quick Suite dashboards visualize customer sentiment, NPS scores, and quality issues across all touchpoints.

Dashboard components

Executive summary: * Current NPS score with trend indicator * Customer health score distribution * Total at-risk revenue * Month-over-month changes

Sentiment analysis: * Sentiment trend over 12 months * Breakdown by channel (mobile, dealer, contact center, in-vehicle) * Top issues driving negative sentiment * Correlation with service quality

Quality issues: * Issue category breakdown (battery, software, connectivity) * Battery issue trend (15% → 40%) * Geographic distribution * Vehicle model comparison

Revenue impact: * Revenue by stream (sales, service, subscriptions) * At-risk revenue by segment * Churn impact on lifetime value * Recovery opportunity sizing

Creating datasets

Quick Suite datasets connect to Athena views:

Pre-built datasets: 1. kpi_trends - Monthly NPS and health scores 2. sentiment_analysis - Sentiment by channel 3. issue_categories - Issue breakdown 4. revenue_streams - Revenue by stream 5. at_risk_revenue - Revenue at risk 6. customer_segments - Segment analysis 7. service_quality - Operational metrics 8. churn_analysis - Churn risk factors

Dashboard features

Interactive elements: * Drill-down to underlying data * Filters for time range, segment, model, region * Parameters for dynamic thresholds * Actions to navigate or link to external systems

AI-powered insights: * Anomaly detection for unusual patterns * Forecasting future trends * Natural language queries * Auto-generated narrative summaries

Using the AI chat agent

Quick Suite integrates Bedrock agents for conversational analytics:

"What's causing declining customer sentiment?"
"Show me battery issues by vehicle model"
"Which segments have highest churn risk?"
"What's the revenue impact of thermal issues?"

Building Quick Suite automations

Quick Automate combines Bedrock agents with your knowledge base to continuously monitor customer sentiment, automatically identify root causes when scores drop, and match issues to proven remediation playbooks. The result is an executive-ready report with recommended actions and approval workflows—delivered in minutes instead of days of manual analysis.

Customer sentiment automation

This workflow monitors sentiment daily and triggers remediation when critical issues are detected. The automation runs without code, using natural language prompts to configure each step.

Workflow steps:

Key capabilities:

Executive sentiment report

The executive sentiment report is generated entirely through natural language prompts—no code, no templates, no IT tickets required. It automatically surfaces critical customer issues, calculates revenue at risk, and presents actionable remediation workflows with clear approval checkpoints.

Report components:

Customization:

Business users can customize the report format, add new workflows, or change approval routing simply by updating the prompt—putting control in the hands of the people closest to the customer. No development required.

Example report sections:

Critical Issues Detected:
- Battery degradation affecting 847 vehicles
- Revenue at risk: $2.3M
- Recommended action: Battery Replacement Program
- [Start Approval Flow] button

Thermal Management Issues:
- 312 vehicles reporting cabin temperature problems
- Revenue at risk: $890K
- Recommended action: BMS Software Update
- [Start Approval Flow] button

Agent prompts

The automation uses natural language prompts to configure Bedrock agents. Here are the actual prompts used in the workflow:

Sentiment analysis prompt (Steps 1-3):

Analyze customer sentiment over the last 90 days. Provide a brief summary in this format:

- Sentiment Status: [Declining/Stable/Improving]
- Decline Percentage: [X%]
- Top 3 Root Causes (identify SPECIFIC, MEASURABLE issues from the data -
  examples: Battery degradation, Service appointment delays, ADAS malfunctions,
  Connectivity failures, Support response time. Do NOT use vague categories
  like 'General Dissatisfaction'):
  1. [Specific Cause] - $[Amount] at risk, [X] customers
  2. [Specific Cause] - $[Amount] at risk, [X] customers
  3. [Specific Cause] - $[Amount] at risk, [X] customers
- Recommended Action: [One sentence]

Root cause analysis prompt:

What specific product defects or issues are causing {sentiment_data['top_cause']}?
Provide exactly 3 issues in this format:

1. [Issue Name] - [Brief description]
2. [Issue Name] - [Brief description]
3. [Issue Name] - [Brief description]

Return nothing more.

Technical defect analysis prompt:

What specific battery, charging, or vehicle component defects are causing
{agent_response_34['top_issue']}? List 3 specific technical problems in this format:

1. [Issue Name] - [Brief description]
2. [Issue Name] - [Brief description]
3. [Issue Name] - [Brief description]

Deep dive diagnostic prompt:

Deep dive: {agent_response_37['defect_1']}

Return:
- severity: Critical/High/Medium
- customers: [count]
- revenue_at_risk: $[amount]
- diagnostic_pattern: [symptom matching playbook]
- key_indicator: [primary telemetry signal]
- evidence: [1 sentence]

Key prompt design principles:

Creating a workflow

Best practices

Workflow design: * Keep focused on single process * Use clear naming conventions * Add error handling * Include retry logic

Agent prompts: * Be specific about requirements * Include expected output format * Reference knowledge base * Iterate based on quality

Creating approval flows with Quick Flows

Quick Flows transforms the executive report from a static document into an interactive action center—one click launches a guided approval workflow that gathers context, notifies stakeholders via Slack, and routes decisions to the right people. Business users build these flows in minutes using plain English, connecting approvals to notifications without writing code or waiting on IT. The flows integrate directly with your existing tools like Slack and Task Center, so approvers act where they already work instead of logging into another system.

Campaign approval flow

Enables marketing managers to instantly notify stakeholders via Slack and route critical customer outreach campaigns for approval—turning a multi-day email chain into a one-click workflow.

Flow components:

Service repair authorization flow

Empowers service managers to review repair scope, cost estimates, and customer impact in seconds, then trigger approvals directly from Jira without logging into multiple systems.

Flow components:

Building a flow

Integration with existing tools

Quick Suite workflows integrate with collaboration tools.

Slack integration

Capabilities: * Real-time notifications for critical issues * One-click approval buttons * Thread-based discussions * Status updates posted automatically

Message template:

🚨 *Critical Customer Sentiment Alert*

*Issue*: Battery complaints increased 15%
*Affected Customers*: 12,450 (Premium)
*Revenue at Risk*: $2.3M
*Action*: Battery replacement program

📊 <link|Executive Report>
✅ <link|Approve> ❌ <link|Reject>

Jira integration

Capabilities: * Automatic ticket creation * Bi-directional status sync * Custom fields from workflow data * Links back to Quick Suite reports

Email notifications

Capabilities: * Executive summaries on schedule * Approval requests with links * Digest emails for multiple issues * Customizable templates

Measuring effectiveness

Track metrics to optimize workflows and demonstrate value.

Automation metrics

Workflow performance: * Time from detection to action: Target < 1 hour * Completion rate: Target > 95% * False positive rate: Target < 5% * Manual intervention: Target < 20%

Business impact metrics

Customer outcomes: * Issues resolved proactively * Customer satisfaction improvement * Churn reduction vs baseline * Response time improvement

Financial impact: * Revenue protected monthly * Cost savings from automation * Campaign ROI * Service program effectiveness

Approval metrics

Approval efficiency: * Average response time: Target < 4 hours * Approval rate by workflow type * Escalation frequency: Target < 10% * Approver engagement