# ===================================================================
# MODULE 7, LESSON 2: PERSONAL AND BUSINESS BANKING PRODUCTS
# ===================================================================

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from datetime import datetime, timedelta
import warnings
warnings.filterwarnings('ignore')

print("="*70)
print("PERSONAL AND BUSINESS BANKING PRODUCTS")
print("="*70)

# ----------------------------------------------------------------
# PART A: PERSONAL BANKING PRODUCT PORTFOLIO
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART A: Personal Banking Product Portfolio")
print("-"*60)

personal_products = pd.DataFrame({
    'Product': [
        'Current Account',
        'Savings Account',
        'Credit Card',
        'Personal Loan',
        'Mortgage',
        'Investment Account',
        'Digital Wallet',
        'BNPL',
        'Insurance',
        'Pension'
    ],
    'Adoption Rate (%)': [85, 65, 55, 30, 20, 25, 60, 35, 20, 15],
    'Satisfaction (CSAT)': [82, 78, 75, 72, 70, 76, 80, 74, 68, 65],
    'Revenue ($M)': [150, 100, 180, 120, 250, 80, 60, 50, 40, 30],
    'Growth (%)': [8, 5, 10, 12, 6, 18, 25, 30, 20, 12],
    'Profit Margin (%)': [25, 30, 35, 40, 45, 20, 28, 18, 15, 22]
})

print("Personal Banking Product Portfolio:")
print(personal_products.to_string(index=False))

# Visualise
fig, axes = plt.subplots(2, 2, figsize=(14, 10))

# Adoption vs Satisfaction
ax = axes[0, 0]
scatter = ax.scatter(personal_products['Adoption Rate (%)'], personal_products['Satisfaction (CSAT)'], 
                     s=personal_products['Revenue ($M)'] * 2, alpha=0.7)
for i, row in personal_products.iterrows():
    ax.annotate(row['Product'], (row['Adoption Rate (%)'] + 0.5, row['Satisfaction (CSAT)'] + 0.5))
ax.set_xlabel('Adoption Rate (%)')
ax.set_ylabel('Satisfaction (CSAT)')
ax.set_title('Adoption vs Satisfaction (size = Revenue)')
ax.grid(True, alpha=0.3)

# Revenue by Product
ax = axes[0, 1]
personal_products_sorted = personal_products.sort_values('Revenue ($M)', ascending=True)
ax.barh(personal_products_sorted['Product'], personal_products_sorted['Revenue ($M)'], color='teal', alpha=0.7)
ax.set_xlabel('Revenue ($M)')
ax.set_title('Revenue by Personal Banking Product')
ax.grid(True, alpha=0.3)

# Product Category Distribution
ax = axes[1, 0]
categories = ['Deposit', 'Credit', 'Lending', 'Investment', 'Payments', 'Insurance']
category_revenue = {}
for cat in categories:
    cat_products = personal_products[personal_products['Product'].isin(
        ['Current Account', 'Savings Account'] if cat == 'Deposit' else
        ['Credit Card'] if cat == 'Credit' else
        ['Personal Loan', 'Mortgage'] if cat == 'Lending' else
        ['Investment Account', 'Pension'] if cat == 'Investment' else
        ['Digital Wallet', 'BNPL'] if cat == 'Payments' else
        ['Insurance']
    )]
    category_revenue[cat] = cat_products['Revenue ($M)'].sum() if not cat_products.empty else 0

ax.pie(category_revenue.values(), labels=category_revenue.keys(), autopct='%1.1f%%')
ax.set_title('Revenue by Product Category')

# Growth vs Profit Margin
ax = axes[1, 1]
scatter = ax.scatter(personal_products['Growth (%)'], personal_products['Profit Margin (%)'], 
                     s=personal_products['Revenue ($M)'] * 2, alpha=0.7)
for i, row in personal_products.iterrows():
    ax.annotate(row['Product'], (row['Growth (%)'] + 0.5, row['Profit Margin (%)'] + 0.5))
ax.set_xlabel('Growth (%)')
ax.set_ylabel('Profit Margin (%)')
ax.set_title('Growth vs Profit Margin')
ax.grid(True, alpha=0.3)

plt.tight_layout()
plt.savefig('personal_products.png', dpi=300, bbox_inches='tight')
plt.show()
print("Personal banking product visualisation saved as 'personal_products.png'")

# ----------------------------------------------------------------
# PART B: BUSINESS BANKING PRODUCT PORTFOLIO
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART B: Business Banking Product Portfolio")
print("-"*60)

business_products = pd.DataFrame({
    'Product': [
        'Business Current Account',
        'Business Savings',
        'Business Loan',
        'Commercial Mortgage',
        'Business Credit Card',
        'Payment Processing',
        'Trade Finance',
        'Cash Management',
        'Payroll Services',
        'Insurance'
    ],
    'Adoption Rate (%)': [75, 50, 40, 25, 45, 55, 30, 35, 40, 30],
    'Satisfaction (CSAT)': [80, 75, 72, 70, 76, 78, 72, 74, 76, 70],
    'Revenue ($M)': [80, 60, 120, 150, 70, 90, 60, 50, 40, 35],
    'Growth (%)': [10, 8, 15, 8, 12, 18, 20, 12, 10, 15],
    'Profit Margin (%)': [22, 28, 35, 38, 30, 25, 20, 28, 18, 15]
})

print("Business Banking Product Portfolio:")
print(business_products.to_string(index=False))

# Visualise
fig, axes = plt.subplots(2, 2, figsize=(14, 10))

# Adoption vs Satisfaction
ax = axes[0, 0]
scatter = ax.scatter(business_products['Adoption Rate (%)'], business_products['Satisfaction (CSAT)'], 
                     s=business_products['Revenue ($M)'] * 2, alpha=0.7)
for i, row in business_products.iterrows():
    ax.annotate(row['Product'], (row['Adoption Rate (%)'] + 0.5, row['Satisfaction (CSAT)'] + 0.5))
ax.set_xlabel('Adoption Rate (%)')
ax.set_ylabel('Satisfaction (CSAT)')
ax.set_title('Business Products: Adoption vs Satisfaction')
ax.grid(True, alpha=0.3)

# Revenue by Product
ax = axes[0, 1]
business_products_sorted = business_products.sort_values('Revenue ($M)', ascending=True)
ax.barh(business_products_sorted['Product'], business_products_sorted['Revenue ($M)'], color='orange', alpha=0.7)
ax.set_xlabel('Revenue ($M)')
ax.set_title('Revenue by Business Banking Product')
ax.grid(True, alpha=0.3)

# SME vs Corporate Focus
ax = axes[1, 0]
sme_products = ['Business Current Account', 'Business Savings', 'Business Loan', 'Business Credit Card', 'Payroll Services']
corporate_products = ['Commercial Mortgage', 'Trade Finance', 'Cash Management', 'Payment Processing', 'Insurance']

sme_revenue = business_products[business_products['Product'].isin(sme_products)]['Revenue ($M)'].sum()
corporate_revenue = business_products[business_products['Product'].isin(corporate_products)]['Revenue ($M)'].sum()

ax.pie([sme_revenue, corporate_revenue], labels=['SME Banking', 'Corporate Banking'], autopct='%1.1f%%')
ax.set_title('Revenue: SME vs Corporate Banking')

# Growth vs Profit Margin
ax = axes[1, 1]
scatter = ax.scatter(business_products['Growth (%)'], business_products['Profit Margin (%)'], 
                     s=business_products['Revenue ($M)'] * 2, alpha=0.7)
for i, row in business_products.iterrows():
    ax.annotate(row['Product'], (row['Growth (%)'] + 0.5, row['Profit Margin (%)'] + 0.5))
ax.set_xlabel('Growth (%)')
ax.set_ylabel('Profit Margin (%)')
ax.set_title('Business Products: Growth vs Profit Margin')
ax.grid(True, alpha=0.3)

plt.tight_layout()
plt.savefig('business_products.png', dpi=300, bbox_inches='tight')
plt.show()
print("Business banking product visualisation saved as 'business_products.png'")

# ----------------------------------------------------------------
# PART C: PRODUCT METRICS DASHBOARD
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART C: Product Metrics Dashboard")
print("-"*60)

product_metrics = pd.DataFrame({
    'Metric': [
        'Product Adoption Rate',
        'Customer Satisfaction (CSAT)',
        'Net Promoter Score (NPS)',
        'Revenue Growth',
        'Profit Margin',
        'Market Share',
        'Product Development Time',
        'Customer Retention'
    ],
    'Personal Banking': [
        '72%',
        '78%',
        '55',
        '14%',
        '30%',
        '18%',
        '6 months',
        '88%'
    ],
    'Business Banking': [
        '55%',
        '74%',
        '48',
        '12%',
        '26%',
        '12%',
        '8 months',
        '82%'
    ],
    'Target': [
        '> 70%',
        '> 85%',
        '> 65',
        '> 20%',
        '> 35%',
        '> 20%',
        '< 4 months',
        '> 90%'
    ]
})

print("Product Metrics Dashboard:")
print(product_metrics.to_string(index=False))

# ----------------------------------------------------------------
# PART D: PRODUCT SEGMENTATION
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART D: Product Segmentation")
print("-"*60)

# Customer segmentation for product targeting
segments = pd.DataFrame({
    'Segment': ['Digital Natives', 'Savers', 'Borrowers', 'Investors', 'Families', 'Retirees'],
    'Size (%)': [25, 20, 15, 20, 12, 8],
    'Digital Engagement': ['High', 'Medium', 'High', 'High', 'Medium', 'Low'],
    'Top Products': [
        'Digital Wallet, Current Account, BNPL',
        'Savings Account, Fixed Deposit',
        'Personal Loan, Credit Card',
        'Investment Account, Pension',
        'Joint Account, Children\'s Account',
        'Pension, Savings Account'
    ],
    'Growth Potential': ['High', 'Medium', 'High', 'High', 'Medium', 'Low']
})

print("Customer Segment Analysis:")
print(segments.to_string(index=False))

# ----------------------------------------------------------------
# PART E: PRODUCT DEVELOPMENT ROADMAP
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART E: Product Development Roadmap")
print("-"*60)

roadmap = {
    "Phase 1 (0-6 months) – Foundation": {
        "Focus": "Enhance core products.",
        "Activities": [
            "Improve current account features.",
            "Launch digital wallet with rewards.",
            "Enhance mobile app experience.",
            "Implement real-time payments."
        ],
        "Success Metrics": ["Adoption rate > 60%", "CSAT > 80%"]
    },
    "Phase 2 (6-12 months) – Growth": {
        "Focus": "Launch new products.",
        "Activities": [
            "Launch BNPL product.",
            "Launch robo-advisory investment.",
            "Launch business payment processing.",
            "Implement open banking integrations."
        ],
        "Success Metrics": ["New product adoption > 30%", "Revenue growth > 15%"]
    },
    "Phase 3 (12-24 months) – Expansion": {
        "Focus": "Expand product portfolio.",
        "Activities": [
            "Launch mortgage products.",
            "Launch business lending.",
            "Launch insurance products.",
            "Build embedded finance capabilities."
        ],
        "Success Metrics": ["Product portfolio expanded", "Market share > 20%"]
    },
    "Phase 4 (24+ months) – Innovation": {
        "Focus": "Innovate and lead.",
        "Activities": [
            "Launch generative AI products.",
            "Launch sustainable finance products.",
            "Build platform banking.",
            "Achieve industry leadership."
        ],
        "Success Metrics": ["Industry-leading products", "Continuous innovation"]
    }
}

for phase, details in roadmap.items():
    print(f"\n{phase}:")
    print(f"  Focus: {details['Focus']}")
    print("  Activities:")
    for activity in details['Activities']:
        print(f"    • {activity}")
    print("  Success Metrics:")
    for metric in details['Success Metrics']:
        print(f"    • {metric}")

# ----------------------------------------------------------------
# PART F: SUMMARY AND RECOMMENDATIONS
# ----------------------------------------------------------------

print("\n" + "="*70)
print("PART F: Summary and Recommendations")
print("="*70)

print("""
Personal and Business Banking Products – Key Takeaways:

1. Personal banking products: current accounts, savings, credit cards, loans, mortgages, investments, wallets, BNPL, insurance, pensions.
2. Business banking products: business current accounts, savings, loans, commercial mortgages, credit cards, payment processing, trade finance, cash management, payroll, insurance.
3. Product design principles: simplicity, accessibility, transparency, flexibility, security, innovation.
4. Customer segmentation: digital natives, savers, borrowers, investors, families, retirees.
5. Key metrics: adoption rate, satisfaction, NPS, revenue growth, profit margin, market share.
6. Product development roadmap: foundation → growth → expansion → innovation.

Recommendations:
  - Optimise core products for customer satisfaction.
  - Launch new products for underserved segments.
  - Invest in digital wallets and BNPL.
  - Build business banking capabilities.
  - Use data for product personalisation.
  - Continuously innovate and improve products.
""")

print("="*70)
print("END OF LESSON 2 – MODULE 7")
print("="*70)