┌─────────────────────────────────────────────────────────────────────────────┐
│                    PRODUCT LIFECYCLE IN DIGITAL BANKING                   │
├─────────────────────────────────────────────────────────────────────────────┤
│                                                                             │
│  ┌─────────────┐    ┌─────────────┐    ┌─────────────┐    ┌─────────────┐  │
│  │  Ideation   │    │  Design &   │    │  Launch &   │    │  Growth &   │  │
│  │  (Discovery)│ ──→ │  Development│ ──→ │  Go-to-    │ ──→ │  Optimisation│  │
│  └─────────────┘    └─────────────┘    │  Market    │    └─────────────┘  │
│                                         └─────────────┘                    │
│                                                                             │
│  ┌─────────────┐    ┌─────────────┐                                      │
│  │  Maturity   │    │  Retirement │                                      │
│  │  (Stable)   │ ──→ │  (Sunset)   │                                      │
│  └─────────────┘    └─────────────┘                                      │
│                                                                             │
└─────────────────────────────────────────────────────────────────────────────┘

# ===================================================================
# MODULE 7, LESSON 1: DIGITAL BANKING PRODUCTS – OVERVIEW AND 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("DIGITAL BANKING PRODUCTS – OVERVIEW AND STRATEGY")
print("="*70)

# ----------------------------------------------------------------
# PART A: PRODUCT PORTFOLIO ANALYSIS
# ----------------------------------------------------------------

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

# Define product portfolio
products = pd.DataFrame({
    'Product': [
        'Current Account',
        'Savings Account',
        'Credit Card',
        'Personal Loan',
        'Mortgage',
        'Investment Account',
        'Insurance',
        'Digital Wallet',
        'BNPL'
    ],
    'Category': [
        'Deposit', 'Deposit', 'Credit', 'Lending', 'Lending',
        'Investment', 'Insurance', 'Payments', 'Lending'
    ],
    'Revenue ($M)': [120, 80, 150, 200, 300, 75, 50, 90, 60],
    'Growth (%)': [8, 5, 12, 15, 6, 20, 25, 18, 35],
    'Market Share (%)': [15, 12, 10, 8, 5, 6, 4, 20, 8],
    'Profit Margin (%)': [25, 30, 35, 40, 45, 20, 15, 28, 18]
})

print("Product Portfolio:")
print(products.to_string(index=False))

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

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

# Growth vs Profit Margin
ax = axes[0, 1]
scatter = ax.scatter(products['Growth (%)'], products['Profit Margin (%)'], 
                     s=products['Revenue ($M)'] * 2, alpha=0.7)
for i, row in 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 (size = Revenue)')
ax.grid(True, alpha=0.3)

# Product Category Distribution
ax = axes[1, 0]
category_revenue = products.groupby('Category')['Revenue ($M)'].sum()
ax.pie(category_revenue.values, labels=category_revenue.index, autopct='%1.1f%%')
ax.set_title('Revenue by Product Category')

# Product Performance Matrix
ax = axes[1, 1]
categories = ['Stars', 'Question Marks', 'Cash Cows', 'Dogs']
# Simple BCG-like matrix
growth_threshold = 15
share_threshold = 10
colors = []
for _, row in products.iterrows():
    if row['Growth (%)'] > growth_threshold and row['Market Share (%)'] > share_threshold:
        colors.append('green')  # Star
    elif row['Growth (%)'] > growth_threshold and row['Market Share (%)'] <= share_threshold:
        colors.append('orange')  # Question Mark
    elif row['Growth (%)'] <= growth_threshold and row['Market Share (%)'] > share_threshold:
        colors.append('blue')  # Cash Cow
    else:
        colors.append('red')  # Dog

ax.scatter(products['Market Share (%)'], products['Growth (%)'], 
           s=products['Revenue ($M)'] * 2, c=colors, alpha=0.7)
for i, row in products.iterrows():
    ax.annotate(row['Product'], (row['Market Share (%)'] + 0.3, row['Growth (%)'] + 0.3))
ax.set_xlabel('Market Share (%)')
ax.set_ylabel('Growth (%)')
ax.set_title('Product Portfolio Matrix')
ax.axhline(y=growth_threshold, color='black', linestyle='--', alpha=0.3)
ax.axvline(x=share_threshold, color='black', linestyle='--', alpha=0.3)
ax.grid(True, alpha=0.3)

plt.tight_layout()
plt.savefig('product_portfolio.png', dpi=300, bbox_inches='tight')
plt.show()
print("Product portfolio visualisation saved as 'product_portfolio.png'")

# ----------------------------------------------------------------
# PART B: PRODUCT LIFE CYCLE ANALYSIS
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART B: Product Life Cycle Analysis")
print("-"*60)

# Simulate product life cycle data
np.random.seed(42)
n_quarters = 20

product_lifecycle = pd.DataFrame({
    'quarter': range(1, n_quarters + 1),
    'adoption_rate': np.concatenate([
        np.linspace(0.02, 0.15, 6),
        np.linspace(0.15, 0.45, 6),
        np.linspace(0.45, 0.55, 4),
        np.linspace(0.55, 0.60, 4)
    ]) + np.random.normal(0, 0.02, n_quarters),
    'growth_rate': np.concatenate([
        np.linspace(0.05, 0.20, 6),
        np.linspace(0.20, 0.40, 6),
        np.linspace(0.40, 0.15, 4),
        np.linspace(0.15, 0.02, 4)
    ]) + np.random.normal(0, 0.02, n_quarters),
    'revenue': np.concatenate([
        np.linspace(1, 10, 6),
        np.linspace(10, 30, 6),
        np.linspace(30, 35, 4),
        np.linspace(35, 30, 4)
    ]) + np.random.normal(0, 1, n_quarters)
})

product_lifecycle['adoption_rate'] = product_lifecycle['adoption_rate'].clip(0, 1)

print("Product Life Cycle Data:")
print(product_lifecycle.head())

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

# Adoption Rate
ax = axes[0, 0]
ax.plot(product_lifecycle['quarter'], product_lifecycle['adoption_rate'], 'b-', linewidth=2)
ax.set_xlabel('Quarter')
ax.set_ylabel('Adoption Rate')
ax.set_title('Product Adoption Rate')
ax.grid(True, alpha=0.3)

# Growth Rate
ax = axes[0, 1]
ax.plot(product_lifecycle['quarter'], product_lifecycle['growth_rate'], 'g-', linewidth=2)
ax.set_xlabel('Quarter')
ax.set_ylabel('Growth Rate')
ax.set_title('Product Growth Rate')
ax.grid(True, alpha=0.3)

# Revenue
ax = axes[1, 0]
ax.plot(product_lifecycle['quarter'], product_lifecycle['revenue'], 'r-', linewidth=2)
ax.set_xlabel('Quarter')
ax.set_ylabel('Revenue ($M)')
ax.set_title('Product Revenue')
ax.grid(True, alpha=0.3)

# Life Cycle Stages
ax = axes[1, 1]
stages = ['Introduction', 'Growth', 'Maturity', 'Decline']
stage_quarters = [0, 6, 12, 16]
stage_colors = ['blue', 'green', 'orange', 'red']

for i, stage in enumerate(stages):
    start = stage_quarters[i]
    end = stage_quarters[i+1] if i < len(stage_quarters)-1 else n_quarters
    q_range = range(start, end)
    adoption_values = product_lifecycle.iloc[start:end]['adoption_rate'].values
    ax.plot(q_range, adoption_values, color=stage_colors[i], linewidth=2, label=stage)

ax.set_xlabel('Quarter')
ax.set_ylabel('Adoption Rate')
ax.set_title('Product Life Cycle Stages')
ax.legend()
ax.grid(True, alpha=0.3)

plt.tight_layout()
plt.savefig('product_lifecycle.png', dpi=300, bbox_inches='tight')
plt.show()
print("Product lifecycle visualisation saved as 'product_lifecycle.png'")

# ----------------------------------------------------------------
# PART C: PRODUCT STRATEGY METRICS
# ----------------------------------------------------------------

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

strategy_metrics = pd.DataFrame({
    'Metric': [
        'Product Adoption Rate',
        'Customer Satisfaction (CSAT)',
        'Net Promoter Score (NPS)',
        'Revenue Growth',
        'Profit Margin',
        'Market Share',
        'Product Development Time',
        'Innovation Pipeline'
    ],
    'Current Value': [
        '45%',
        '78%',
        '55',
        '12%',
        '28%',
        '15%',
        '6 months',
        '8 products'
    ],
    'Target Value': [
        '> 70%',
        '> 85%',
        '> 65',
        '> 20%',
        '> 35%',
        '> 20%',
        '< 4 months',
        '> 15 products'
    ],
    'Status': ['🟡', '🟡', '🟡', '🟡', '🟡', '🟡', '🟡', '🟡']
})

print("Product Strategy Metrics:")
print(strategy_metrics.to_string(index=False))

# ----------------------------------------------------------------
# PART D: PRODUCT INNOVATION PIPELINE
# ----------------------------------------------------------------

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

innovation_pipeline = pd.DataFrame({
    'Product Idea': [
        'AI-Powered Budgeting Tool',
        'Embedded Insurance',
        'ESG Investment Platform',
        'Buy Now Pay Later (BNPL)',
        'Digital Mortgage',
        'Smart Savings Assistant',
        'Open Banking Aggregator',
        'Voice Banking'
    ],
    'Stage': [
        'Ideation',
        'Design',
        'Prototype',
        'Launch',
        'Growth',
        'Prototype',
        'Design',
        'Ideation'
    ],
    'Priority': [
        'High',
        'High',
        'High',
        'High',
        'Medium',
        'Medium',
        'Medium',
        'Low'
    ],
    'Expected Launch': [
        'Q3 2025',
        'Q4 2025',
        'Q1 2026',
        'Q2 2025',
        'Q3 2025',
        'Q1 2026',
        'Q2 2026',
        'Q4 2026'
    ],
    'Status': ['🟢', '🟡', '🟡', '🟢', '🟢', '🟡', '🟡', '🔴']
})

print("Product Innovation Pipeline:")
print(innovation_pipeline.to_string(index=False))

# Visualise pipeline
fig, ax = plt.subplots(figsize=(12, 6))
pipeline_stages = ['Ideation', 'Design', 'Prototype', 'Launch', 'Growth']
stage_colors = {'Ideation': 'blue', 'Design': 'orange', 'Prototype': 'yellow', 
                'Launch': 'green', 'Growth': 'teal'}

stage_counts = innovation_pipeline['Stage'].value_counts()
ax.bar(stage_counts.index, stage_counts.values, 
       color=[stage_colors.get(s, 'gray') for s in stage_counts.index], alpha=0.7)
ax.set_xlabel('Stage')
ax.set_ylabel('Count')
ax.set_title('Product Innovation Pipeline')
ax.grid(True, alpha=0.3)

plt.tight_layout()
plt.savefig('innovation_pipeline.png', dpi=300, bbox_inches='tight')
plt.show()
print("Innovation pipeline visualisation saved as 'innovation_pipeline.png'")

# ----------------------------------------------------------------
# PART E: PRODUCT STRATEGY RECOMMENDATIONS
# ----------------------------------------------------------------

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

strategy = {
    "1. Product Portfolio Optimisation": {
        "Actions": [
            "Invest in high-growth products (BNPL, digital wallets).",
            "Optimise mature products for efficiency.",
            "Phase out underperforming products.",
            "Diversify product categories."
        ],
        "Priority": "High",
        "Timeline": "0-12 months"
    },
    "2. Innovation Pipeline": {
        "Actions": [
            "Accelerate high-priority product ideas.",
            "Establish innovation lab for rapid prototyping.",
            "Implement agile product development.",
            "Foster cross-functional collaboration."
        ],
        "Priority": "High",
        "Timeline": "0-6 months"
    },
    "3. Customer-Centric Design": {
        "Actions": [
            "Implement design thinking for all products.",
            "Gather continuous customer feedback.",
            "Personalise product offerings.",
            "Improve user experience."
        ],
        "Priority": "High",
        "Timeline": "0-12 months"
    },
    "4. Data-Driven Product Management": {
        "Actions": [
            "Implement product analytics.",
            "Track product performance metrics.",
            "Use A/B testing for optimisation.",
            "Leverage customer data for insights."
        ],
        "Priority": "Medium",
        "Timeline": "6-12 months"
    },
    "5. Ecosystem Partnerships": {
        "Actions": [
            "Partner with fintechs for innovation.",
            "Build open banking API ecosystem.",
            "Integrate with third-party platforms.",
            "Develop BaaS offerings."
        ],
        "Priority": "Medium",
        "Timeline": "12-24 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 F: SUMMARY AND RECOMMENDATIONS
# ----------------------------------------------------------------

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

print("""
Digital Banking Products – Key Takeaways:

1. Digital banking products are designed, delivered, and managed through digital channels.
2. Key categories: deposit, lending, payment, investment, insurance, open banking, sustainable finance.
3. Product lifecycle: ideation → design → launch → growth → maturity → retirement.
4. Product strategy: vision, mission, objectives, target segments, value proposition, roadmap.
5. Customer-centric design: design thinking, customer needs, personalisation.
6. Key metrics: adoption rate, satisfaction, NPS, revenue growth, profit margin.
7. Innovation pipeline: continuous product development and improvement.

Recommendations:
  - Optimise product portfolio for growth and profitability.
  - Build a strong innovation pipeline.
  - Design products with a customer-centric approach.
  - Use data analytics for product optimisation.
  - Build ecosystem partnerships for innovation.
  - Continuously measure and improve product performance.
""")

print("="*70)
print("END OF LESSON 1 – MODULE 7")
print("="*70)