Expand to Business Analytics: Add Customer, Sales, and KPI models

Major expansion from fraud detection to comprehensive business analytics: DATABASE CHANGES: - Renamed database from 'fraud_detection' to 'business_analytics' - Renamed user from 'fraud_analyst' to 'data_analyst' - Expanded from 20 to 39 tables across 4 business models NEW MODELS (19 tables): 1. Customer Analytics (5 tables): - customer_segments, customer_lifetime_value, churn_predictions - customer_satisfaction, engagement_metrics 2. Sales & Revenue Analytics (6 tables): - product_catalog, sales_transactions, sales_targets - sales_performance, revenue_forecasts 3. KPI & Metrics (8 tables): - kpi_definitions, daily_metrics, monthly_summaries - trend_analysis, dashboard_snapshots - report_definitions, report_executions, data_quality_checks DATA GENERATION: - Extended generate_data.sh with 6 new steps (now 15 total) - Added CLV calculations for all customers - Added churn predictions based on transaction recency - Added 30K customer satisfaction surveys - Added 1M sales transactions linked to 24 products - Added 90 days of daily KPI metrics - Added 24 months of business summaries SQL EXERCISES (3 new levels): - Level 2: Customer Analytics (10 exercises + 3 challenges) - Level 3: Sales & Revenue Analysis (12 exercises + 3 challenges) - Level 4: KPI Dashboards & Metrics (12 exercises + 3 challenges) DOCUMENTATION: - Updated README.md with business analytics focus - Updated QUICKSTART.md with new data generation steps - Updated SETUP_COMPLETE.md with 39-table architecture - Added DATA_MODELS.md with complete model specifications - Added WHATS_NEW.md with migration guide SEED DATA: - Added 8 customer segments (VIP, High Value, etc.) - Added 24 products across 5 categories - Added 16 KPI definitions across 4 categories - Added 8 standard report definitions All changes maintain idempotency and backward compatibility with existing fraud detection functionality.
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+# Level 2: Customer Analytics
+
+## Introduction
+Learn to analyze customer behavior, calculate lifetime value, identify churn risks, and segment customers - essential skills for Data Analysts working with customer data.
+
+## Learning Objectives
+- Calculate customer lifetime value (CLV)
+- Identify at-risk customers
+- Segment customers by behavior
+- Analyze satisfaction trends
+- Track engagement metrics
+
+---
+
+## Exercise 2.1: Customer Segmentation
+**Objective:** Find the distribution of customers across segments
+
+```sql
+SELECT 
+    cs.segment_name,
+    COUNT(clv.customer_id) as customer_count,
+    AVG(clv.clv_score) as avg_clv_score,
+    SUM(clv.total_revenue) as total_segment_revenue
+FROM customer_segments cs
+LEFT JOIN customer_lifetime_value clv ON cs.segment_id = clv.segment_id
+GROUP BY cs.segment_id, cs.segment_name
+ORDER BY total_segment_revenue DESC;
+```
+
+**Expected Result:** Segment breakdown with counts and revenue
+
+---
+
+## Exercise 2.2: High-Value Customers
+**Objective:** Identify top 10 customers by lifetime value
+
+```sql
+-- Your query here
+
+```
+
+**Hint:** Use customer_lifetime_value table and ORDER BY
+
+**Solution:**
+```sql
+SELECT 
+    c.customer_id,
+    c.first_name,
+    c.last_name,
+    c.email,
+    clv.total_revenue,
+    clv.total_transactions,
+    clv.clv_score,
+    cs.segment_name
+FROM customer_lifetime_value clv
+JOIN customers c ON clv.customer_id = c.customer_id
+JOIN customer_segments cs ON clv.segment_id = cs.segment_id
+ORDER BY clv.clv_score DESC
+LIMIT 10;
+```
+
+---
+
+## Exercise 2.3: Churn Risk Analysis
+**Objective:** Find customers at CRITICAL or HIGH churn risk
+
+```sql
+-- Your query here
+
+```
+
+**Solution:**
+```sql
+SELECT 
+    c.customer_id,
+    c.first_name,
+    c.last_name,
+    c.email,
+    cp.churn_probability,
+    cp.risk_level,
+    cp.days_since_last_transaction,
+    cp.engagement_score
+FROM churn_predictions cp
+JOIN customers c ON cp.customer_id = c.customer_id
+WHERE cp.risk_level IN ('CRITICAL', 'HIGH')
+ORDER BY cp.churn_probability DESC;
+```
+
+---
+
+## Exercise 2.4: Customer Satisfaction Trends
+**Objective:** Calculate average NPS score by month
+
+```sql
+-- Your query here
+
+```
+
+**Hint:** Use DATE_TRUNC or EXTRACT to group by month
+
+**Solution:**
+```sql
+SELECT 
+    DATE_TRUNC('month', survey_date) as month,
+    COUNT(*) as survey_count,
+    AVG(nps_score) as avg_nps,
+    AVG(csat_score) as avg_csat,
+    COUNT(CASE WHEN sentiment = 'POSITIVE' THEN 1 END) as positive_count,
+    COUNT(CASE WHEN sentiment = 'NEGATIVE' THEN 1 END) as negative_count
+FROM customer_satisfaction
+GROUP BY DATE_TRUNC('month', survey_date)
+ORDER BY month DESC;
+```
+
+---
+
+## Exercise 2.5: Engagement Score Analysis
+**Objective:** Find customers with declining engagement
+
+```sql
+-- Your query here
+
+```
+
+**Hint:** Compare recent engagement to historical average
+
+**Solution:**
+```sql
+WITH recent_engagement AS (
+    SELECT 
+        customer_id,
+        AVG(engagement_score) as recent_score
+    FROM engagement_metrics
+    WHERE metric_date >= CURRENT_DATE - INTERVAL '30 days'
+    GROUP BY customer_id
+),
+historical_engagement AS (
+    SELECT 
+        customer_id,
+        AVG(engagement_score) as historical_score
+    FROM engagement_metrics
+    WHERE metric_date < CURRENT_DATE - INTERVAL '30 days'
+    GROUP BY customer_id
+)
+SELECT 
+    c.customer_id,
+    c.first_name,
+    c.last_name,
+    re.recent_score,
+    he.historical_score,
+    (re.recent_score - he.historical_score) as score_change
+FROM customers c
+JOIN recent_engagement re ON c.customer_id = re.customer_id
+JOIN historical_engagement he ON c.customer_id = he.customer_id
+WHERE re.recent_score < he.historical_score
+ORDER BY score_change ASC
+LIMIT 20;
+```
+
+---
+
+## Exercise 2.6: Customer Cohort Analysis
+**Objective:** Analyze customer retention by registration cohort
+
+```sql
+-- Your query here
+
+```
+
+**Solution:**
+```sql
+SELECT 
+    DATE_TRUNC('month', c.registration_date) as cohort_month,
+    COUNT(DISTINCT c.customer_id) as total_customers,
+    COUNT(DISTINCT CASE WHEN cp.risk_level = 'LOW' THEN c.customer_id END) as active_customers,
+    COUNT(DISTINCT CASE WHEN cp.risk_level IN ('HIGH', 'CRITICAL') THEN c.customer_id END) as at_risk_customers,
+    ROUND(100.0 * COUNT(DISTINCT CASE WHEN cp.risk_level = 'LOW' THEN c.customer_id END) / 
+          COUNT(DISTINCT c.customer_id), 2) as retention_rate
+FROM customers c
+LEFT JOIN churn_predictions cp ON c.customer_id = cp.customer_id
+WHERE c.registration_date >= CURRENT_DATE - INTERVAL '12 months'
+GROUP BY DATE_TRUNC('month', c.registration_date)
+ORDER BY cohort_month DESC;
+```
+
+---
+
+## Exercise 2.7: Customer Lifetime Value by Segment
+**Objective:** Compare CLV metrics across customer segments
+
+```sql
+-- Your query here
+
+```
+
+**Solution:**
+```sql
+SELECT 
+    cs.segment_name,
+    COUNT(clv.customer_id) as customer_count,
+    MIN(clv.clv_score) as min_clv,
+    AVG(clv.clv_score) as avg_clv,
+    MAX(clv.clv_score) as max_clv,
+    PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY clv.clv_score) as median_clv,
+    SUM(clv.total_revenue) as total_revenue,
+    AVG(clv.total_transactions) as avg_transactions
+FROM customer_segments cs
+JOIN customer_lifetime_value clv ON cs.segment_id = clv.segment_id
+GROUP BY cs.segment_id, cs.segment_name
+ORDER BY avg_clv DESC;
+```
+
+---
+
+## Exercise 2.8: Satisfaction by Category
+**Objective:** Analyze satisfaction scores by feedback category
+
+```sql
+-- Your query here
+
+```
+
+**Solution:**
+```sql
+SELECT 
+    category,
+    COUNT(*) as feedback_count,
+    AVG(nps_score) as avg_nps,
+    AVG(csat_score) as avg_csat,
+    ROUND(100.0 * COUNT(CASE WHEN sentiment = 'POSITIVE' THEN 1 END) / COUNT(*), 2) as positive_pct,
+    ROUND(100.0 * COUNT(CASE WHEN sentiment = 'NEGATIVE' THEN 1 END) / COUNT(*), 2) as negative_pct
+FROM customer_satisfaction
+GROUP BY category
+ORDER BY avg_nps DESC;
+```
+
+---
+
+## Exercise 2.9: Customer Engagement Patterns
+**Objective:** Find most engaged customers in the last 30 days
+
+```sql
+-- Your query here
+
+```
+
+**Solution:**
+```sql
+SELECT 
+    c.customer_id,
+    c.first_name,
+    c.last_name,
+    SUM(em.login_count) as total_logins,
+    SUM(em.page_views) as total_page_views,
+    SUM(em.time_spent_minutes) as total_time_spent,
+    AVG(em.engagement_score) as avg_engagement_score
+FROM customers c
+JOIN engagement_metrics em ON c.customer_id = em.customer_id
+WHERE em.metric_date >= CURRENT_DATE - INTERVAL '30 days'
+GROUP BY c.customer_id, c.first_name, c.last_name
+HAVING AVG(em.engagement_score) > 75
+ORDER BY avg_engagement_score DESC
+LIMIT 20;
+```
+
+---
+
+## Exercise 2.10: Churn Prevention Priority List
+**Objective:** Create a priority list for customer retention efforts
+
+```sql
+-- Your query here
+
+```
+
+**Hint:** Combine churn risk with customer value
+
+**Solution:**
+```sql
+SELECT 
+    c.customer_id,
+    c.first_name,
+    c.last_name,
+    c.email,
+    clv.clv_score,
+    clv.total_revenue,
+    cp.churn_probability,
+    cp.risk_level,
+    cp.days_since_last_transaction,
+    (clv.clv_score * cp.churn_probability / 100) as retention_priority_score
+FROM customers c
+JOIN customer_lifetime_value clv ON c.customer_id = clv.customer_id
+JOIN churn_predictions cp ON c.customer_id = cp.customer_id
+WHERE cp.risk_level IN ('HIGH', 'CRITICAL')
+  AND clv.clv_score > 1000
+ORDER BY retention_priority_score DESC
+LIMIT 50;
+```
+
+---
+
+## Challenge Exercises
+
+### Challenge 2.1: Customer Journey Analysis
+Create a query that shows the customer journey from registration to current status, including:
+- Registration date
+- First transaction date
+- Total transactions
+- Current segment
+- Churn risk
+- Latest satisfaction score
+
+### Challenge 2.2: Segment Migration Analysis
+Identify customers who have moved between segments over time (requires historical CLV data)
+
+### Challenge 2.3: Engagement Correlation
+Analyze the correlation between engagement scores and satisfaction scores
+
+---
+
+## Next Steps
+Once you're comfortable with customer analytics, move on to:
+- **Level 3:** Sales & Revenue Analysis
+- **Level 4:** KPI Dashboards & Metrics
+