┌─────────────────────────────────────────────────────────────────────────────┐
│                         OMNICHANNEL BANKING                               │
├─────────────────────────────────────────────────────────────────────────────┤
│                                                                             │
│  ┌──────────────────────────────────────────────────────────────────────┐   │
│  │                    CUSTOMER JOURNEY                                 │   │
│  │  (Start in one channel, continue in another seamlessly)              │   │
│  └──────────────────────────────────────────────────────────────────────┘   │
│                                                                             │
│  ┌─────────────┐  ┌─────────────┐  ┌─────────────┐  ┌─────────────┐       │
│  │   Mobile    │  │   Web       │  │   Branch    │  │   Contact   │       │
│  │   App       │  │   Banking   │  │             │  │   Centre    │       │
│  └─────────────┘  └─────────────┘  └─────────────┘  └─────────────┘       │
│        │              │              │              │                       │
│        └──────────────┴──────────────┴──────────────┘                       │
│                              │                                              │
│                    ┌─────────▼─────────┐                                  │
│                    │    UNIFIED DATA   │                                  │
│                    │  (360° Customer   │                                  │
│                    │   View)           │                                  │
│                    └───────────────────┘                                  │
│                                                                             │
└─────────────────────────────────────────────────────────────────────────────┘

┌─────────────────────────────────────────────────────────────────────────────┐
│                       CHANNEL MIGRATION                                    │
├─────────────────────────────────────────────────────────────────────────────┤
│                                                                             │
│  Stage 1           Stage 2           Stage 3           Stage 4             │
│  ┌─────────┐      ┌─────────┐      ┌─────────┐      ┌─────────┐          │
│  │ Branch  │      │ Mobile  │      │ Branch  │      │ Mobile  │          │
│  │ First   │ ──→  │ First   │ ──→  │ +       │ ──→  │ +       │          │
│  │ (70%)   │      │ (60%)   │      │ Mobile  │      │ Branch  │          │
│  │         │      │         │      │ (40%)   │      │ (20%)   │          │
│  └─────────┘      └─────────┘      └─────────┘      └─────────┘          │
│                                                                             │
│  Traditional       Digital-First    Hybrid            Digital-First        │
│  Dominance         Adoption         Balance           with Select          │
│                                      Customer         Branch               │
│                                      Choice                                │
│                                                                             │
└─────────────────────────────────────────────────────────────────────────────┘

# ===================================================================
# MODULE 1, LESSON 4: DIGITAL CHANNELS AND OMNICHANNEL BANKING
# ===================================================================

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

print("="*70)
print("DIGITAL CHANNELS AND OMNICHANNEL BANKING")
print("="*70)

# ----------------------------------------------------------------
# PART A: CHANNEL PERFORMANCE ANALYSIS
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART A: Channel Performance Analysis")
print("-"*60)

# Define channel data
channels = ['Mobile App', 'Online Banking', 'Branch', 'Contact Centre', 'ATM', 'Chatbot']
usage = [75, 60, 35, 25, 65, 15]
satisfaction = [88, 82, 79, 75, 85, 80]
cost_per_transaction = [0.25, 0.50, 4.50, 3.00, 1.00, 0.15]
trend = ['📈', '📈', '📉', '📉', '📊', '📈']

channel_df = pd.DataFrame({
    'Channel': channels,
    'Usage (%)': usage,
    'Satisfaction (%)': satisfaction,
    'Cost per Transaction ($)': cost_per_transaction,
    'Trend': trend
})

print("Channel Performance Data:")
print(channel_df.to_string(index=False))

# ----------------------------------------------------------------
# PART B: CHANNEL MIGRATION VISUALISATION
# -----------------------------------------------------------------

print("\n" + "-"*60)
print("PART B: Channel Migration Visualisation")
print("-"*60)

# Simulate channel migration over time
years = ['2020', '2021', '2022', '2023', '2024']
channels_migration = ['Mobile App', 'Online Banking', 'Branch', 'Contact Centre', 'ATM', 'Chatbot']

migration_data = {
    'Mobile App': [45, 52, 60, 68, 75],
    'Online Banking': [55, 57, 58, 59, 60],
    'Branch': [55, 48, 42, 38, 35],
    'Contact Centre': [35, 32, 28, 26, 25],
    'ATM': [60, 62, 63, 64, 65],
    'Chatbot': [2, 5, 8, 11, 15]
}

migration_df = pd.DataFrame(migration_data, index=years)
print("Channel Migration Data (% usage):")
print(migration_df)

# Visualise channel migration
fig, ax = plt.subplots(figsize=(12, 8))
migration_df.plot(kind='line', marker='o', ax=ax, linewidth=2, markersize=8)
ax.set_xlabel('Year')
ax.set_ylabel('Usage (%)')
ax.set_title('Channel Migration Over Time')
ax.legend(loc='best')
ax.grid(True, alpha=0.3)
plt.tight_layout()
plt.savefig('channel_migration.png', dpi=300, bbox_inches='tight')
plt.show()
print("Channel migration visualisation saved as 'channel_migration.png'")

# ----------------------------------------------------------------
# PART C: COST PER TRANSACTION COMPARISON
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART C: Cost per Transaction Comparison")
print("-"*60)

fig, ax = plt.subplots(figsize=(12, 6))
colors = ['green' if c < 1 else 'orange' if c < 3 else 'red' for c in cost_per_transaction]
bars = ax.bar(channels, cost_per_transaction, color=colors, alpha=0.7)
ax.set_ylabel('Cost per Transaction ($)')
ax.set_title('Cost per Transaction by Channel')
for bar, cost in zip(bars, cost_per_transaction):
    ax.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 0.1, 
            f'${cost:.2f}', ha='center', va='bottom', fontweight='bold')
ax.axhline(y=1.0, color='green', linestyle='--', label='Target Cost (< $1)')
ax.legend()
ax.grid(True, alpha=0.3)
plt.tight_layout()
plt.savefig('cost_per_transaction.png', dpi=300, bbox_inches='tight')
plt.show()
print("Cost per transaction visualisation saved as 'cost_per_transaction.png'")

# ----------------------------------------------------------------
# PART D: CHANNEL JOURNEY MAP
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART D: Channel Journey Map")
print("-"*60)

# Define a sample customer journey across channels
journey_stages = ['Discovery', 'Research', 'Application', 'Approval', 'Onboarding', 'Usage', 'Support']
channels_used = {
    'Discovery': ['Social Media', 'Website'],
    'Research': ['Website', 'Reviews', 'Branch'],
    'Application': ['Mobile App', 'Website'],
    'Approval': ['Email', 'SMS'],
    'Onboarding': ['Mobile App', 'Email', 'Video Call'],
    'Usage': ['Mobile App', 'Online Banking', 'ATM'],
    'Support': ['Chatbot', 'Contact Centre', 'Branch']
}

journey_channel_df = pd.DataFrame([{
    'Stage': stage,
    'Channels': ', '.join(channels_used[stage]),
    'Emotion': np.random.choice(['Positive', 'Neutral', 'Negative']),
    'Friction Level': np.random.choice(['Low', 'Medium', 'High'])
} for stage in journey_stages])

print("Sample Customer Journey Across Channels:")
print(journey_channel_df.to_string(index=False))

# ----------------------------------------------------------------
# PART E: CHANNEL STRATEGY RECOMMENDATIONS
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART E: Channel Strategy Recommendations")
print("-"*60)

strategies = {
    'Mobile App': {
        'Priority': 'High',
        'Strategy': 'Invest in AI-driven personalisation and features.',
        'Investment': 'High',
        'Timeline': 'Now'
    },
    'Online Banking': {
        'Priority': 'Medium',
        'Strategy': 'Optimise for complex transactions and reporting.',
        'Investment': 'Medium',
        'Timeline': '6 months'
    },
    'Branch': {
        'Priority': 'Medium',
        'Strategy': 'Transform from transactional to advisory role.',
        'Investment': 'Medium',
        'Timeline': '12 months'
    },
    'Contact Centre': {
        'Priority': 'Medium',
        'Strategy': 'Integrate AI for intelligent routing and self-service.',
        'Investment': 'Medium',
        'Timeline': '12 months'
    },
    'ATM': {
        'Priority': 'Low',
        'Strategy': 'Maintain and modernise with new features.',
        'Investment': 'Low',
        'Timeline': 'Ongoing'
    },
    'Chatbot': {
        'Priority': 'High',
        'Strategy': 'Expand capabilities with generative AI.',
        'Investment': 'High',
        'Timeline': 'Now'
    }
}

strategy_df = pd.DataFrame(strategies).T
print("Channel Strategy Recommendations:")
print(strategy_df.to_string())

# ----------------------------------------------------------------
# PART F: CHANNEL BALANCE SCORECARD
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART F: Channel Balance Scorecard")
print("-"*60)

scorecard = pd.DataFrame({
    'Dimension': ['Usage', 'Satisfaction', 'Cost Efficiency', 'Growth Potential', 'Innovation'],
    'Mobile App': [9, 8, 9, 9, 9],
    'Online Banking': [7, 7, 8, 6, 5],
    'Branch': [4, 6, 2, 2, 3],
    'Contact Centre': [3, 5, 3, 3, 4],
    'ATM': [7, 8, 7, 5, 3],
    'Chatbot': [2, 7, 9, 9, 9]
})

print("Channel Balance Scorecard (1-10):")
print(scorecard.to_string(index=False))

# Heatmap of scorecard
fig, ax = plt.subplots(figsize=(12, 6))
scorecard_heatmap = scorecard.set_index('Dimension')
sns.heatmap(scorecard_heatmap, annot=True, fmt='.0f', cmap='RdYlGn', ax=ax)
ax.set_title('Channel Balance Scorecard Heatmap')
plt.tight_layout()
plt.savefig('channel_scorecard.png', dpi=300, bbox_inches='tight')
plt.show()
print("Channel scorecard visualisation saved as 'channel_scorecard.png'")

# ----------------------------------------------------------------
# PART G: SUMMARY AND RECOMMENDATIONS
# ----------------------------------------------------------------

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

print("""
Digital Channels and Omnichannel Banking – Key Takeaways:

1. Omnichannel banking provides a seamless, integrated customer experience.
2. Key channels: Mobile app, online banking, branch, contact centre, ATM, chatbots.
3. Channel migration is shifting customers from high-cost to low-cost channels.
4. Omnichannel requires unified data, API-first architecture, and journey orchestration.
5. Channel performance metrics: usage, satisfaction, cost per transaction.
6. Channel strategy should balance investment across channels.
7. Digital channels are the future; branches are transforming.

Recommendations:
  - Invest in mobile app and chatbot capabilities.
  - Transform branches from transactional to advisory.
  - Implement omnichannel journey orchestration.
  - Measure and optimise channel performance.
  - Drive channel migration to digital channels.
  - Create a unified customer view across all channels.
""")

print("="*70)
print("END OF LESSON 4 – MODULE 1")
print("="*70)