┌─────────────────────────────────────────────────────────────────────────────┐
│                    MOBILE BANKING EVOLUTION                               │
├─────────────────────────────────────────────────────────────────────────────┤
│                                                                             │
│  Mobile Banking 1.0    Mobile Banking 2.0    Mobile Banking 3.0            │
│  (2010-2015)           (2015-2020)           (2020-Present)                │
│                                                                             │
│  ┌─────────────┐       ┌─────────────┐       ┌─────────────┐              │
│  │ Basic       │       │ Feature     │       │ AI-Powered  │              │
│  │ Transaction │ ──→   │ Rich        │ ──→   │ Intelligent │              │
│  │ Banking     │       │ Banking     │       │ Banking     │              │
│  └─────────────┘       └─────────────┘       └─────────────┘              │
│                                                                             │
│  • Balance checks      • Payments           • Personalisation              │
│  • Transfers           • Bill pay           • Predictive analytics         │
│  • Basic statements    • Card management    • Generative AI                │
│  • Branch locator      • Investment         • Voice/chat                   │
│  • Contact us          • Personal finance   • Hyper-personalisation        │
│                                                                             │
└─────────────────────────────────────────────────────────────────────────────┘

┌─────────────────────────────────────────────────────────────────────────────┐
│                    MOBILE APP DEVELOPMENT LIFECYCLE                        │
├─────────────────────────────────────────────────────────────────────────────┤
│                                                                             │
│  ┌──────────────────────────────────────────────────────────────────────┐   │
│  │                    1. STRATEGY & PLANNING                          │   │
│  │  Define objectives, target audience, key features                   │   │
│  └──────────────────────────────────────────────────────────────────────┘   │
│                                    │                                        │
│                                    v                                        │
│  ┌──────────────────────────────────────────────────────────────────────┐   │
│  │                    2. UX/UI DESIGN                                  │   │
│  │  Wireframes, prototypes, visual design, user testing                │   │
│  └──────────────────────────────────────────────────────────────────────┘   │
│                                    │                                        │
│                                    v                                        │
│  ┌──────────────────────────────────────────────────────────────────────┐   │
│  │                    3. DEVELOPMENT                                   │   │
│  │  Frontend, backend, API integration, security                       │   │
│  └──────────────────────────────────────────────────────────────────────┘   │
│                                    │                                        │
│                                    v                                        │
│  ┌──────────────────────────────────────────────────────────────────────┐   │
│  │                    4. TESTING                                       │   │
│  │  Unit tests, integration tests, user acceptance testing             │   │
│  └──────────────────────────────────────────────────────────────────────┘   │
│                                    │                                        │
│                                    v                                        │
│  ┌──────────────────────────────────────────────────────────────────────┐   │
│  │                    5. DEPLOYMENT                                    │   │
│  │  App store submission, rollout, monitoring                          │   │
│  └──────────────────────────────────────────────────────────────────────┘   │
│                                    │                                        │
│                                    v                                        │
│  ┌──────────────────────────────────────────────────────────────────────┐   │
│  │                    6. MONITORING & ITERATION                       │   │
│  │  Analytics, feedback, updates, continuous improvement               │   │
│  └──────────────────────────────────────────────────────────────────────┘   │
│                                                                             │
└─────────────────────────────────────────────────────────────────────────────┘

# ===================================================================
# MODULE 2, LESSON 5: MOBILE BANKING AND APP STRATEGY
# ===================================================================

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("MOBILE BANKING AND APP STRATEGY")
print("="*70)

# ----------------------------------------------------------------
# PART A: MOBILE BANKING USAGE STATISTICS
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART A: Mobile Banking Usage Statistics")
print("-"*60)

# Define usage statistics
metrics = {
    'Metric': ['Monthly Active Users', 'Daily Active Users', 'DAU/MAU Ratio', 
               'Average Session Duration', 'Screen Views per Session', 
               'Transaction per User', 'Push Notification Open Rate'],
    'Value': ['2.5M', '1.1M', '44%', '8.5 min', '12', '18/month', '28%'],
    'Target': ['3.0M', '1.5M', '> 50%', '> 10 min', '> 15', '> 20/month', '> 30%'],
    'Status': ['🟡', '🟡', '🟡', '🟡', '🔴', '🟡', '🔴']
}

metrics_df = pd.DataFrame(metrics)
print("Mobile Banking Usage Statistics:")
print(metrics_df.to_string(index=False))

# ----------------------------------------------------------------
# PART B: APP FEATURES ADOPTION
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART B: App Features Adoption")
print("-"*60)

# Define feature adoption data
features = ['Balance Check', 'Fund Transfers', 'Bill Payments', 'Check Deposit', 
            'Card Management', 'Alerts', 'Budgeting Tools', 'Loan Application', 
            'Investment Access', 'Chatbot', 'Personalised Offers']
adoption = [92, 78, 72, 55, 48, 65, 42, 28, 22, 35, 18]
satisfaction = [85, 82, 78, 75, 72, 80, 70, 68, 65, 75, 72]

feature_df = pd.DataFrame({
    'Feature': features,
    'Adoption (%)': adoption,
    'Satisfaction (%)': satisfaction
}).sort_values('Adoption (%)', ascending=False)

print("App Features Adoption:")
print(feature_df.to_string(index=False))

# Visualise
fig, axes = plt.subplots(1, 2, figsize=(14, 6))

# Feature Adoption
ax = axes[0]
bars = ax.barh(feature_df['Feature'], feature_df['Adoption (%)'], color='teal', alpha=0.7)
ax.set_xlabel('Adoption (%)')
ax.set_title('Feature Adoption')
ax.grid(True, alpha=0.3)

# Feature Satisfaction
ax = axes[1]
bars = ax.barh(feature_df['Feature'], feature_df['Satisfaction (%)'], color='green', alpha=0.7)
ax.set_xlabel('Satisfaction (%)')
ax.set_title('Feature Satisfaction')
ax.grid(True, alpha=0.3)

plt.tight_layout()
plt.savefig('feature_adoption.png', dpi=300, bbox_inches='tight')
plt.show()
print("Feature adoption visualisation saved as 'feature_adoption.png'")

# ----------------------------------------------------------------
# PART C: MOBILE APP USER JOURNEY
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART C: Mobile App User Journey")
print("-"*60)

# Define user journey steps
journey_steps = ['App Launch', 'Login', 'Dashboard View', 'Check Balance', 
                 'Transfer Funds', 'Confirm Transfer', 'Logout']
journey_counts = [10000, 9200, 8800, 8200, 6500, 5800, 4800]
journey_conversion = [100, 92, 88, 82, 65, 58, 48]

journey_df = pd.DataFrame({
    'Step': journey_steps,
    'Users': journey_counts,
    'Conversion (%)': journey_conversion
})

print("Mobile App User Journey:")
print(journey_df.to_string(index=False))

# Visualise funnel
fig, ax = plt.subplots(figsize=(10, 6))
colors = ['#2ecc71', '#27ae60', '#f1c40f', '#e67e22', '#e74c3c', '#c0392b', '#95a5a6']
bars = ax.barh(journey_steps, journey_counts, color=colors, alpha=0.7)

for bar, count, conv in zip(bars, journey_counts, journey_conversion):
    ax.text(bar.get_width() + 200, bar.get_y() + bar.get_height()/2, 
            f'{count} ({conv}%)', ha='left', va='center', fontweight='bold')

ax.set_xlabel('Number of Users')
ax.set_title('Mobile App User Journey Funnel')
ax.grid(True, alpha=0.3, axis='x')

plt.tight_layout()
plt.savefig('mobile_journey.png', dpi=300, bbox_inches='tight')
plt.show()
print("Mobile journey visualisation saved as 'mobile_journey.png'")

# ----------------------------------------------------------------
# PART D: APP PERFORMANCE METRICS
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART D: App Performance Metrics")
print("-"*60)

# Simulate app performance data over time
dates = pd.date_range(start='2024-01-01', end='2024-06-30', freq='D')
n_days = len(dates)

np.random.seed(42)
downloads = np.random.poisson(500, n_days) + 200
active_users = np.random.poisson(2000, n_days) + 800
session_length = np.random.normal(8, 2, n_days).clip(3, 15)
crash_rate = np.random.beta(0.5, 20, n_days) * 100

performance_df = pd.DataFrame({
    'date': dates,
    'downloads': downloads,
    'active_users': active_users,
    'session_length': session_length,
    'crash_rate': crash_rate
})

print("App Performance Data Sample:")
print(performance_df.head())

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

# Downloads
ax = axes[0, 0]
ax.plot(performance_df['date'], performance_df['downloads'], 'b-', linewidth=1.5)
ax.set_xlabel('Date')
ax.set_ylabel('Downloads')
ax.set_title('Daily Downloads')
ax.grid(True, alpha=0.3)

# Active Users
ax = axes[0, 1]
ax.plot(performance_df['date'], performance_df['active_users'], 'g-', linewidth=1.5)
ax.set_xlabel('Date')
ax.set_ylabel('Active Users')
ax.set_title('Daily Active Users')
ax.grid(True, alpha=0.3)

# Session Length
ax = axes[1, 0]
ax.plot(performance_df['date'], performance_df['session_length'], 'orange', linewidth=1.5)
ax.axhline(y=8, color='red', linestyle='--', label='Target (8 min)')
ax.set_xlabel('Date')
ax.set_ylabel('Session Length (min)')
ax.set_title('Average Session Length')
ax.legend()
ax.grid(True, alpha=0.3)

# Crash Rate
ax = axes[1, 1]
ax.plot(performance_df['date'], performance_df['crash_rate'], 'red', linewidth=1.5)
ax.axhline(y=1, color='green', linestyle='--', label='Target (1%)')
ax.set_xlabel('Date')
ax.set_ylabel('Crash Rate (%)')
ax.set_title('App Crash Rate')
ax.legend()
ax.grid(True, alpha=0.3)

plt.tight_layout()
plt.savefig('app_performance.png', dpi=300, bbox_inches='tight')
plt.show()
print("App performance visualisation saved as 'app_performance.png'")

# ----------------------------------------------------------------
# PART E: MOBILE APP STRATEGY
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART E: Mobile App Strategy")
print("-"*60)

strategy = {
    "1. Customer Acquisition": {
        "Tactics": [
            "App store optimisation (ASO).",
            "Referral programs.",
            "Digital marketing campaigns.",
            "Partnerships with fintechs."
        ],
        "Priority": "High",
        "Timeline": "Now"
    },
    "2. Feature Development": {
        "Tactics": [
            "Implement AI-powered personalisation.",
            "Add predictive analytics features.",
            "Develop voice banking capabilities.",
            "Integrate open banking features."
        ],
        "Priority": "High",
        "Timeline": "6 months"
    },
    "3. User Engagement": {
        "Tactics": [
            "Personalised push notifications.",
            "In-app messaging and support.",
            "Gamification and rewards.",
            "Financial wellness features."
        ],
        "Priority": "High",
        "Timeline": "Now"
    },
    "4. Performance Optimisation": {
        "Tactics": [
            "Optimise app loading speed.",
            "Reduce crash rates.",
            "Improve app store rating.",
            "Implement A/B testing."
        ],
        "Priority": "High",
        "Timeline": "Ongoing"
    },
    "5. Security": {
        "Tactics": [
            "Implement advanced biometrics.",
            "Add real-time fraud detection.",
            "Ensure data encryption.",
            "Regular security audits."
        ],
        "Priority": "Critical",
        "Timeline": "Now"
    }
}

for item, details in strategy.items():
    print(f"\n{item}:")
    for tactic in details['Tactics']:
        print(f"  • {tactic}")
    print(f"  Priority: {details['Priority']}")
    print(f"  Timeline: {details['Timeline']}")

# ----------------------------------------------------------------
# PART F: APP STORE OPTIMISATION (ASO) CHECKLIST
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART F: App Store Optimisation (ASO) Checklist")
print("-"*60)

aso_checklist = [
    "✅ App title with keywords.",
    "✅ Compelling app description.",
    "✅ High-quality screenshots and video.",
    "✅ Positive reviews and ratings.",
    "✅ Regular app updates.",
    "✅ Localisation for target markets.",
    "✅ Keyword optimisation.",
    "✅ App category selection.",
    "✅ App icon design.",
    "✅ In-app events and promotions."
]

print("ASO Checklist:")
for item in aso_checklist:
    print(item)

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

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

print("""
Mobile Banking and App Strategy – Key Takeaways:

1. Mobile banking is the dominant digital channel for customer engagement.
2. Key app features: account management, payments, card management, biometrics.
3. Mobile-first design principles: simplicity, intuitiveness, speed, accessibility.
4. App development approaches: native, cross-platform, hybrid, PWA.
5. Key metrics: downloads, active users, session length, crash rate, retention.
6. Strategy must balance acquisition, engagement, performance, and security.

Recommendations:
  - Prioritise mobile banking investment.
  - Implement AI-powered personalisation.
  - Optimise app performance and user experience.
  - Use analytics to drive continuous improvement.
  - Ensure robust security and compliance.
  - Leverage app store optimisation for acquisition.
""")

print("="*70)
print("END OF LESSON 5 – MODULE 2")
print("="*70)