┌─────────────────────────────────────────────────────────────────────────────┐
│                    SOCIAL MEDIA MATURITY IN BANKING                       │
├─────────────────────────────────────────────────────────────────────────────┤
│                                                                             │
│  Level 1           Level 2           Level 3           Level 4             │
│  ┌─────────┐      ┌─────────┐      ┌─────────┐      ┌─────────┐          │
│  │ Presence│      │ Engagement│      │ Service │      │ Community│         │
│  │ (Basic) │ ──→  │ (Active) │ ──→  │ (Customer│ ──→ │ (Ecosystem)│       │
│  └─────────┘      └─────────┘      │ Support)│      └─────────┘          │
│                                     └─────────┘                             │
│                                                                             │
│  • Brand page     • Regular posts    • Response to      • Proactive         │
│  • Profile setup  • Engagement       • queries          • Community         │
│  • Basic          • Content sharing  • Issue            • Influencer        │
│    visibility                       • resolution        • Advocacy          │
│                                                                             │
└─────────────────────────────────────────────────────────────────────────────┘

# ===================================================================
# MODULE 2, LESSON 8: SOCIAL MEDIA AND EMERGING CHANNELS
# ===================================================================

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("SOCIAL MEDIA AND EMERGING CHANNELS IN BANKING")
print("="*70)

# ----------------------------------------------------------------
# PART A: SOCIAL MEDIA PLATFORM PERFORMANCE
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART A: Social Media Platform Performance")
print("-"*60)

# Define social media performance data
platforms = ['LinkedIn', 'Twitter', 'Facebook', 'Instagram', 'YouTube', 'TikTok']
followers = [250000, 180000, 420000, 350000, 150000, 80000]
engagement_rate = [3.2, 2.8, 4.5, 5.2, 3.8, 6.5]
posts_per_week = [5, 10, 7, 8, 3, 6]
sentiment_score = [0.75, 0.68, 0.82, 0.78, 0.72, 0.70]

social_df = pd.DataFrame({
    'Platform': platforms,
    'Followers': followers,
    'Engagement Rate (%)': engagement_rate,
    'Posts/Week': posts_per_week,
    'Sentiment Score': sentiment_score
})

print("Social Media Platform Performance:")
print(social_df.to_string(index=False))

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

# Followers
ax = axes[0, 0]
bars = ax.barh(platforms, followers, color='blue', alpha=0.7)
ax.set_xlabel('Followers')
ax.set_title('Follower Count by Platform')
for bar, count in zip(bars, followers):
    ax.text(bar.get_width() + 5000, bar.get_y() + bar.get_height()/2, 
            f'{count:,}', ha='left', va='center')
ax.grid(True, alpha=0.3, axis='x')

# Engagement Rate
ax = axes[0, 1]
bars = ax.barh(platforms, engagement_rate, color='green', alpha=0.7)
ax.set_xlabel('Engagement Rate (%)')
ax.set_title('Engagement Rate by Platform')
for bar, rate in zip(bars, engagement_rate):
    ax.text(bar.get_width() + 0.1, bar.get_y() + bar.get_height()/2, 
            f'{rate}%', ha='left', va='center')
ax.grid(True, alpha=0.3, axis='x')

# Sentiment Score
ax = axes[1, 0]
bars = ax.barh(platforms, sentiment_score, color='purple', alpha=0.7)
ax.set_xlabel('Sentiment Score')
ax.set_title('Sentiment Score by Platform')
ax.axvline(x=0.7, color='red', linestyle='--', label='Target (0.7)')
ax.legend()
for bar, score in zip(bars, sentiment_score):
    ax.text(bar.get_width() + 0.02, bar.get_y() + bar.get_height()/2, 
            f'{score:.2f}', ha='left', va='center')
ax.grid(True, alpha=0.3, axis='x')

# Posts per Week
ax = axes[1, 1]
bars = ax.barh(platforms, posts_per_week, color='orange', alpha=0.7)
ax.set_xlabel('Posts per Week')
ax.set_title('Posting Frequency')
for bar, posts in zip(bars, posts_per_week):
    ax.text(bar.get_width() + 0.2, bar.get_y() + bar.get_height()/2, 
            str(posts), ha='left', va='center')
ax.grid(True, alpha=0.3, axis='x')

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

# ----------------------------------------------------------------
# PART B: SOCIAL MEDIA SENTIMENT ANALYSIS
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART B: Social Media Sentiment Analysis")
print("-"*60)

# Simulate social media posts with sentiment
np.random.seed(42)
n_posts = 1000

sentiment_data = pd.DataFrame({
    'post_id': range(1, n_posts+1),
    'date': [datetime.now() - timedelta(days=np.random.randint(0, 365)) for _ in range(n_posts)],
    'platform': np.random.choice(platforms, n_posts),
    'sentiment': np.random.choice(['Positive', 'Neutral', 'Negative'], n_posts, 
                                 p=[0.45, 0.35, 0.20]),
    'engagement': np.random.poisson(50, n_posts).clip(0, 500),
    'topic': np.random.choice(['Customer Service', 'Products', 'Rates', 'Mobile App', 
                               'Security', 'Fees', 'Marketing', 'General'], n_posts)
})

print("Sentiment Data Sample:")
print(sentiment_data.head())

# Sentiment by platform
sentiment_by_platform = pd.crosstab(sentiment_data['platform'], sentiment_data['sentiment'])
sentiment_by_platform['Total'] = sentiment_by_platform.sum(axis=1)
sentiment_by_platform['Positive %'] = (sentiment_by_platform['Positive'] / sentiment_by_platform['Total']) * 100

print("\nSentiment by Platform:")
print(sentiment_by_platform)

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

# Sentiment distribution
ax = axes[0]
sentiment_counts = sentiment_data['sentiment'].value_counts()
colors = {'Positive': 'green', 'Neutral': 'yellow', 'Negative': 'red'}
ax.pie(sentiment_counts.values, labels=sentiment_counts.index, autopct='%1.1f%%', 
       colors=[colors[s] for s in sentiment_counts.index])
ax.set_title('Overall Sentiment Distribution')

# Sentiment by platform
ax = axes[1]
sentiment_pivot = sentiment_data.pivot_table(index='platform', columns='sentiment', aggfunc='size', fill_value=0)
sentiment_pct = sentiment_pivot.div(sentiment_pivot.sum(axis=1), axis=0) * 100
sentiment_pct.plot(kind='bar', stacked=True, ax=ax, color=['green', 'yellow', 'red'])
ax.set_xlabel('Platform')
ax.set_ylabel('Percentage')
ax.set_title('Sentiment by Platform')
ax.legend(loc='best')
ax.grid(True, alpha=0.3)

plt.tight_layout()
plt.savefig('sentiment_analysis.png', dpi=300, bbox_inches='tight')
plt.show()
print("Sentiment analysis visualisation saved as 'sentiment_analysis.png'")

# ----------------------------------------------------------------
# PART C: SOCIAL MEDIA METRICS DASHBOARD
# ----------------------------------------------------------------

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

metrics_dashboard = pd.DataFrame({
    'Metric': [
        'Total Followers',
        'Total Engagement',
        'Average Engagement Rate',
        'Posts per Week',
        'Average Sentiment Score',
        'Response Rate',
        'Response Time (hours)',
        'Positive Sentiment %',
        'Negative Sentiment %',
        'Brand Awareness Score'
    ],
    'Value': [
        '1.43M',
        '245,000',
        '4.3%',
        '6.5',
        '0.75',
        '92%',
        '2.4',
        '45%',
        '20%',
        '78/100'
    ],
    'Target': [
        '2.0M',
        '350,000',
        '> 5%',
        '> 8',
        '> 0.80',
        '> 95%',
        '< 2 hours',
        '> 50%',
        '< 15%',
        '> 85/100'
    ],
    'Status': ['🟡', '🔴', '🔴', '🔴', '🔴', '🔴', '🟡', '🔴', '🔴', '🔴']
})

print("Social Media Metrics Dashboard:")
print(metrics_dashboard.to_string(index=False))

# ----------------------------------------------------------------
# PART D: SOCIAL MEDIA STRATEGY
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART D: Social Media Strategy")
print("-"*60)

strategy = {
    "1. Content Strategy": {
        "Actions": [
            "Create educational content (financial literacy).",
            "Share success stories and testimonials.",
            "Post product updates and new features.",
            "Engage with trending topics and hashtags."
        ],
        "Priority": "High",
        "Timeline": "Now"
    },
    "2. Customer Service": {
        "Actions": [
            "Implement 24/7 social media monitoring.",
            "Respond to queries within 30 minutes.",
            "Use chatbots for initial triage.",
            "Escalate complex issues to human agents."
        ],
        "Priority": "High",
        "Timeline": "Now"
    },
    "3. Influencer Marketing": {
        "Actions": [
            "Identify relevant influencers in finance.",
            "Develop influencer partnerships.",
            "Create co-branded content.",
            "Measure influencer ROI."
        ],
        "Priority": "Medium",
        "Timeline": "6 months"
    },
    "4. Social Listening": {
        "Actions": [
            "Implement social listening tools.",
            "Monitor brand mentions and sentiment.",
            "Track competitor activity.",
            "Identify trends and opportunities."
        ],
        "Priority": "High",
        "Timeline": "Now"
    },
    "5. Emerging Channels": {
        "Actions": [
            "Explore metaverse banking opportunities.",
            "Develop wearable banking app.",
            "Experiment with AR/VR experiences.",
            "Monitor Web3 trends."
        ],
        "Priority": "Medium",
        "Timeline": "12 months"
    }
}

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

# ----------------------------------------------------------------
# PART E: SOCIAL MEDIA RISK MANAGEMENT
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART E: Social Media Risk Management")
print("-"*60)

risks = pd.DataFrame({
    'Risk': [
        'Reputational Damage',
        'Negative Publicity',
        'Security Breach',
        'Regulatory Non-Compliance',
        'Customer Privacy Breach',
        'Misinformation',
        'Crisis Escalation'
    ],
    'Likelihood': ['Medium', 'Medium', 'Low', 'Medium', 'Low', 'High', 'Medium'],
    'Impact': ['High', 'High', 'Critical', 'Critical', 'Critical', 'Medium', 'High'],
    'Mitigation': [
        'Social media policy, monitoring',
        'Crisis communication plan',
        'Security protocols, monitoring',
        'Compliance review, training',
        'Privacy policy, consent',
        'Fact-checking, response plan',
        'Escalation process, response team'
    ]
})

print("Social Media Risk Management:")
print(risks.to_string(index=False))

# ----------------------------------------------------------------
# PART F: EMERGING CHANNELS READINESS
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART F: Emerging Channels Readiness")
print("-"*60)

emerging_channels = pd.DataFrame({
    'Channel': ['Metaverse', 'Wearables', 'AR/VR', 'Web3/Blockchain', 'Quantum Computing'],
    'Maturity (1-5)': [2, 3, 2, 3, 1],
    'Potential Impact (1-5)': [4, 4, 4, 5, 5],
    'Investment Priority': ['Medium', 'Medium', 'Low', 'Medium', 'Low'],
    'Timeline': ['12-24 months', '6-12 months', '24+ months', '6-12 months', '24+ months']
})

print("Emerging Channels Readiness:")
print(emerging_channels.to_string(index=False))

# Visualise
fig, ax = plt.subplots(figsize=(10, 6))
scatter = ax.scatter(emerging_channels['Maturity (1-5)'], emerging_channels['Potential Impact (1-5)'], 
                     s=200, alpha=0.7)
for i, row in emerging_channels.iterrows():
    ax.annotate(row['Channel'], (row['Maturity (1-5)'] + 0.1, row['Potential Impact (1-5)'] + 0.1))
ax.set_xlabel('Maturity (1-5)')
ax.set_ylabel('Potential Impact (1-5)')
ax.set_title('Emerging Channels: Maturity vs Impact')
ax.grid(True, alpha=0.3)

plt.tight_layout()
plt.savefig('emerging_channels.png', dpi=300, bbox_inches='tight')
plt.show()
print("Emerging channels visualisation saved as 'emerging_channels.png'")

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

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

print("""
Social Media and Emerging Channels – Key Takeaways:

1. Social media is a critical channel for customer service, engagement, and marketing.
2. Key platforms: LinkedIn, Twitter, Facebook, Instagram, YouTube, TikTok.
3. Use cases: customer service, marketing, social listening, community building.
4. Emerging channels: metaverse, wearables, AR/VR, Web3.
5. Key metrics: followers, engagement, sentiment, response rate.
6. Risks: reputational damage, security, compliance, privacy.
7. Strategy: content, customer service, listening, influencer marketing.

Recommendations:
  - Develop a comprehensive social media strategy.
  - Implement social listening and sentiment analysis.
  - Respond to customer queries quickly.
  - Monitor and manage social media risks.
  - Explore emerging channels (metaverse, wearables).
  - Measure and optimise social media performance.
""")

print("="*70)
print("END OF LESSON 8 – MODULE 2")
print("="*70)