agentic-data-stack-community/examples/simple-ecommerce-analytics/project-setup/business-requirements.md

AI Agentic Data Stack Framework - Community Edition. Open source data engineering framework with 4 core agents, essential templates, and 3-dimensional quality validation.

# Business Requirements - Simple E-commerce Analytics
## AI Agentic Data Stack Framework - Community Edition

### 📋 Project Overview

**Project Name**: Trendy Fashion Customer Analytics  
**Version**: Community Edition v1.0  
**Framework**: AI Agentic Data Stack Framework  
**Last Updated**: July 2025  

### 🎯 Business Objective

Trendy Fashion, an online clothing retailer, wants to implement basic customer segmentation to improve marketing campaign effectiveness and customer retention.

### 🔍 Problem Statement

Currently, Trendy Fashion:
- Sends the same marketing messages to all customers
- Cannot identify high-value customers for special treatment
- Lacks understanding of customer purchase behavior
- Has no systematic approach to win back inactive customers
- Cannot measure the effectiveness of marketing campaigns

### 🎯 Success Criteria

| Metric | Target | Measurement Method |
|--------|--------|--------------------|
| Customer Segmentation Accuracy | >80% | RFM analysis validation |
| Data Quality Score | >85% | 3-dimensional quality framework |
| Marketing Campaign Precision | >75% | Segment-specific targeting |
| Customer Retention Rate | +15% | Year-over-year comparison |

### 👥 Stakeholders

#### Primary Stakeholders
- **Marketing Team** (Primary Users)
  - Create targeted campaigns
  - Manage customer communications
  - Track campaign performance

- **Business Owner** (Decision Maker)
  - Monitor business growth
  - Make strategic decisions
  - Approve budget allocation

#### Secondary Stakeholders
- **Customer Service Team** (Data Consumers)
  - Understand customer context
  - Provide personalized service

- **IT Team** (Implementation Support)
  - Data pipeline maintenance
  - System integration

### 📊 Business Use Cases

#### Use Case 1: Customer Segmentation
**As a** Marketing Manager  
**I want to** automatically segment customers based on their purchase behavior  
**So that** I can create targeted marketing campaigns  

**Acceptance Criteria:**
- Customers are segmented into 5-7 distinct groups
- Each segment has clear characteristics and behaviors
- Segmentation updates automatically with new data
- Segments are actionable for marketing campaigns

#### Use Case 2: Campaign Targeting
**As a** Marketing Specialist  
**I want to** export customer lists by segment with campaign recommendations  
**So that** I can execute targeted email and promotional campaigns  

**Acceptance Criteria:**
- Export customer lists filtered by segment
- Include recommended messaging and offers for each segment
- Respect customer marketing consent preferences
- Provide campaign frequency recommendations

#### Use Case 3: Performance Monitoring
**As a** Business Owner  
**I want to** monitor segment performance and customer movement  
**So that** I can understand business health and trends  

**Acceptance Criteria:**
- Track revenue contribution by segment
- Monitor customer migration between segments
- Identify growing and declining segments
- Generate monthly performance reports

### 🔧 Functional Requirements

#### Data Requirements
1. **Customer Data**
   - Customer ID (unique identifier)
   - Contact information (email, name)
   - Registration date and demographics
   - Marketing consent status

2. **Order Data**
   - Order history with dates and amounts
   - Order status (completed, cancelled, refunded)
   - Product information and categories

3. **Quality Requirements**
   - Customer ID: 100% completeness
   - Email: 95% completeness and format validation
   - Order data: 95% accuracy and consistency

#### Segmentation Requirements
1. **RFM Analysis**
   - **Recency**: Days since last purchase
   - **Frequency**: Number of completed orders
   - **Monetary**: Total amount spent

2. **Segment Definitions**
   - Champions: High R, F, M (top customers)
   - Loyal Customers: High F and M, medium R
   - At Risk: Low R, high F and M (win-back needed)
   - New Customers: Recent registration, low F and M
   - Big Spenders: High M, lower F

#### Output Requirements
1. **Customer Segment Dashboard**
   - Segment overview with counts and metrics
   - Revenue contribution by segment
   - Geographic distribution by segment

2. **Marketing Campaign Lists**
   - Exportable customer lists by segment
   - Campaign recommendations and messaging
   - Discount and frequency recommendations

3. **Performance Reports**
   - Monthly segment performance tracking
   - Customer migration analysis
   - Data quality monitoring

### 🔒 Non-Functional Requirements

#### Performance
- Segmentation refresh: Daily (automated)
- Report generation: <30 seconds
- Data export: <60 seconds for 10,000 customers

#### Security
- Customer data encryption at rest and in transit
- Role-based access control
- Audit logging for data access
- Compliance with basic data protection standards

#### Reliability
- 99% uptime for dashboard access
- Automated backup of customer data
- Error handling and data validation

#### Usability
- Web-based dashboard requiring no technical training
- Export formats compatible with email marketing tools
- Clear documentation and user guides

### 📈 Expected Outcomes

#### Immediate Benefits (0-3 months)
- Clear customer segments for targeted marketing
- Improved email campaign open rates (+20%)
- Better understanding of customer behavior

#### Medium-term Benefits (3-6 months)
- Increased customer retention (+15%)
- Higher average order value (+10%)
- Reduced marketing costs through better targeting

#### Long-term Benefits (6+ months)
- Customer lifetime value optimization
- Predictable revenue from segment performance
- Foundation for advanced analytics

### 🛡️ Data Governance

#### Data Quality Standards
- **Completeness**: Core fields must be 90%+ complete
- **Accuracy**: Email formats and IDs must be valid
- **Consistency**: Customer records must be unique
- **Timeliness**: Data updated daily

#### Privacy and Compliance
- Honor customer marketing consent preferences
- Secure storage of personal information
- Regular data quality audits
- Clear data retention policies

### 🚀 Implementation Approach

#### Phase 1: Foundation (Weeks 1-2)
- Set up data ingestion pipeline
- Implement basic quality validation
- Create customer and order data models

#### Phase 2: Segmentation (Weeks 3-4)
- Implement RFM analysis
- Create segment assignment logic
- Build automated refresh process

#### Phase 3: Dashboard (Weeks 5-6)
- Create segment overview dashboard
- Build export functionality
- Implement performance tracking

#### Phase 4: Campaign Integration (Weeks 7-8)
- Integrate with email marketing platform
- Create campaign templates
- Set up performance monitoring

### 📋 Success Validation

#### Technical Validation
- All customer records have valid segments assigned
- Data quality scores meet minimum thresholds
- Dashboard loads within performance requirements
- Export functionality works with marketing tools

#### Business Validation
- Marketing team can identify target customers
- Campaign performance improves measurably
- Customer retention metrics show improvement
- Business stakeholders report valuable insights

### 🔄 Maintenance and Support

#### Ongoing Activities
- Daily data quality monitoring
- Weekly performance report review
- Monthly segment performance analysis
- Quarterly business requirement review

#### Success Metrics Tracking
- Monitor segmentation accuracy
- Track campaign performance by segment
- Measure customer retention improvements
- Assess data quality trend

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**Framework**: AI Agentic Data Stack Framework - Community Edition  
**Contact**: community@agenticdatastack.com  
**Documentation**: See project README.md for implementation details