1┌──────────────────────────────────────────────────────────┐
2│              Analyst vs Executive Focus                   │
3├──────────────────────────────────────────────────────────┤
4│                                                           │
5│  ANALYST THINKS:              EXECUTIVE THINKS:           │
6│  ─────────────────           ─────────────────            │
7│  "How did I get this?"       "What should I do?"          │
8│  "Is the data accurate?"     "What's the impact?"         │
9│  "What methodology?"         "How confident are we?"      │
10│  "Show all the details"      "Just the bottom line"       │
11│  "Here's what I found"       "Here's what it means"       │
12│                                                           │
13├──────────────────────────────────────────────────────────┤
14│                                                           │
15│  EXECUTIVE REPORT MUST ANSWER:                           │
16│  1. Are we on track? (Performance)                       │
17│  2. What's changed? (Trends)                             │
18│  3. What's at risk? (Issues)                             │
19│  4. What should we do? (Actions)                         │
20│                                                           │
21└──────────────────────────────────────────────────────────┘

1┌─────────────────────────────────────────────────────────┐
2│                  KPI Selection Criteria                  │
3├─────────────────────────────────────────────────────────┤
4│                                                          │
5│  ✅ GOOD KPIs are:                                       │
6│  • Actionable - Can we influence it?                    │
7│  • Aligned - Tied to business strategy                  │
8│  • Accurate - Reliable data source                      │
9│  • Accessible - Easy to understand                      │
10│  • Auditable - Can be verified                          │
11│                                                          │
12│  ❌ AVOID Vanity Metrics:                               │
13│  • Total page views (without context)                   │
14│  • Social media followers (if not converting)           │
15│  • Raw user count (without engagement)                  │
16│                                                          │
17├─────────────────────────────────────────────────────────┤
18│                                                          │
19│  KPI HIERARCHY:                                          │
20│  ┌──────────────────────────────────────────────────┐   │
21│  │ Level 1: North Star Metric                       │   │
22│  │          (e.g., Monthly Active Users)            │   │
23│  └──────────────────────┬───────────────────────────┘   │
24│                         │                                │
25│  ┌──────────┬───────────┼───────────┬──────────┐        │
26│  v          v           v           v          v         │
27│  Revenue  Customers  Engagement  Efficiency  Quality    │
28│  ───────  ─────────  ──────────  ──────────  ───────    │
29│  MRR      CAC        DAU/MAU     OpEx       NPS        │
30│  ARPU     LTV        Session     Margin     CSAT       │
31│  Growth   Churn      Features    Time       Defects    │
32│                                                          │
33└─────────────────────────────────────────────────────────┘

1import pandas as pd
2import numpy as np
3
4# Sample KPI data
5kpis = {
6    'Revenue': {
7        'current': 2.5,
8        'target': 2.3,
9        'previous': 2.1,
10        'unit': 'M',
11        'higher_is_better': True
12    },
13    'Customer Count': {
14        'current': 12500,
15        'target': 13000,
16        'previous': 11800,
17        'unit': '',
18        'higher_is_better': True
19    },
20    'Churn Rate': {
21        'current': 5.2,
22        'target': 4.0,
23        'previous': 4.8,
24        'unit': '%',
25        'higher_is_better': False
26    },
27    'NPS': {
28        'current': 42,
29        'target': 50,
30        'previous': 38,
31        'unit': '',
32        'higher_is_better': True
33    },
34    'CAC': {
35        'current': 125,
36        'target': 100,
37        'previous': 145,
38        'unit': '$',
39        'higher_is_better': False
40    }
41}
42
43def calculate_kpi_status(kpi_data):
44    """Calculate status and variance for each KPI"""
45    results = []
46    
47    for name, data in kpi_data.items():
48        current = data['current']
49        target = data['target']
50        previous = data['previous']
51        higher_better = data['higher_is_better']
52        
53        # Calculate variances
54        vs_target = ((current - target) / target) * 100
55        vs_previous = ((current - previous) / previous) * 100
56        
57        # Determine status
58        if higher_better:
59            on_track = current >= target
60        else:
61            on_track = current <= target
62        
63        status = '✅' if on_track else '⚠️' if abs(vs_target) < 10 else '❌'
64        
65        results.append({
66            'KPI': name,
67            'Current': f"{current}{data['unit']}",
68            'Target': f"{target}{data['unit']}",
69            'vs Target': f"{'+' if vs_target > 0 else ''}{vs_target:.1f}%",
70            'vs Previous': f"{'+' if vs_previous > 0 else ''}{vs_previous:.1f}%",
71            'Status': status
72        })
73    
74    return pd.DataFrame(results)
75
76kpi_summary = calculate_kpi_status(kpis)
77print("Executive KPI Summary")
78print("=" * 70)
79print(kpi_summary.to_string(index=False))

1┌──────────────────────────────────────────────────────────┐
2│                 WEEKLY FLASH REPORT                       │
3│                 Week of [DATE]                            │
4├──────────────────────────────────────────────────────────┤
5│                                                           │
6│  📊 KEY METRICS AT A GLANCE                              │
7│  ┌────────────────┬─────────┬─────────┬────────┐        │
8│  │ Metric         │ Actual  │ Target  │ Status │        │
9│  ├────────────────┼─────────┼─────────┼────────┤        │
10│  │ Revenue        │ $2.5M   │ $2.3M   │   ✅   │        │
11│  │ Orders         │ 12,500  │ 11,000  │   ✅   │        │
12│  │ Conversion     │ 3.2%    │ 3.5%    │   ⚠️   │        │
13│  │ CSAT           │ 4.2     │ 4.5     │   ⚠️   │        │
14│  └────────────────┴─────────┴─────────┴────────┘        │
15│                                                           │
16│  🔴 KEY ISSUES (Need Attention)                          │
17│  1. Mobile conversion down 15% - investigating UX issue  │
18│  2. Support tickets up 20% - staffing being addressed    │
19│                                                           │
20│  🟢 WINS THIS WEEK                                       │
21│  1. Record revenue day on Thursday (+45% vs typical)     │
22│  2. New customer acquisition up 12% from campaign        │
23│                                                           │
24│  📋 KEY ACTIONS FOR NEXT WEEK                            │
25│  1. Deploy mobile checkout fix (Owner: Product)          │
26│  2. Review Q2 forecast (Owner: Finance)                  │
27│                                                           │
28└──────────────────────────────────────────────────────────┘

1def generate_monthly_report(data, month, year):
2    """Generate monthly executive report"""
3    
4    report = f"""
5╔══════════════════════════════════════════════════════════╗
6║           MONTHLY BUSINESS REVIEW                         ║
7║           {month} {year}                                  ║
8╚══════════════════════════════════════════════════════════╝
9
10━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
11 EXECUTIVE SUMMARY
12━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
13
14Overall Status: ✅ ON TRACK
15
16Key Highlight: Revenue exceeded target by 12%, driven by 
17successful product launch and seasonal demand.
18
19Main Concern: Customer acquisition cost increased 18%, 
20requiring marketing efficiency review.
21
22━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
23 FINANCIAL PERFORMANCE
24━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
25
26                    Actual      Target      Variance
27Revenue             $2.8M       $2.5M       +12% ✅
28Gross Margin        68%         65%         +3pp ✅
29Operating Cost      $1.2M       $1.1M       +9%  ⚠️
30EBITDA              $0.7M       $0.6M       +17% ✅
31
32YTD Progress: $8.2M of $12M target (68% complete with 50% time elapsed)
33
34━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
35 CUSTOMER METRICS
36━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
37
38Total Customers:    15,200 (+8% MoM)
39New Customers:      2,400
40Churned:            650
41Net Addition:       1,750
42
43Customer Health:
44├── Active (>3 logins/mo):     72%
45├── At Risk (declining):       18%
46└── Dormant (<1 login/mo):     10%
47
48━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
49 TOP 3 PRIORITIES FOR NEXT MONTH
50━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
51
521. �� Reduce CAC by 15%
53   Owner: Marketing
54   Actions: Review channel mix, optimize ad spend
55
562. �� Launch mobile app v2.0
57   Owner: Product
58   Actions: Complete testing, deploy to 10% users
59
603. �� Expand sales team
61   Owner: HR
62   Actions: Hire 3 SDRs, 2 AEs
63
64━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
65"""
66    return report
67
68print(generate_monthly_report(None, "March", 2024))

1QUARTERLY BUSINESS REVIEW STRUCTURE
2====================================
3 
4SECTION 1: EXECUTIVE SUMMARY (1 page)
5├── Quarter performance vs targets
6├── Key achievements
7├── Critical issues
8└── Top 3 priorities for next quarter
9 
10SECTION 2: FINANCIAL REVIEW (2-3 pages)
11├── Revenue analysis
12│   ├── By product/segment
13│   ├── By region
14│   └── Vs forecast
15├── Profitability
16│   ├── Gross margin trends
17│   └── Operating efficiency
18└── Cash position and forecast
19 
20SECTION 3: CUSTOMER & MARKET (2-3 pages)
21├── Customer growth and retention
22├── Market share trends
23├── Competitive landscape
24└── NPS and satisfaction trends
25 
26SECTION 4: OPERATIONAL EXCELLENCE (1-2 pages)
27├── Key operational metrics
28├── Process improvements
29└── Technology updates
30 
31SECTION 5: PEOPLE & CULTURE (1 page)
32├── Headcount and hiring
33├── Engagement scores
34└── Key organizational changes
35 
36SECTION 6: RISK & OPPORTUNITIES (1 page)
37├── Top 3 risks and mitigation
38└── Top 3 growth opportunities
39 
40SECTION 7: NEXT QUARTER OUTLOOK (1-2 pages)
41├── Forecast and assumptions
42├── Key initiatives
43└── Resource requirements
44 
45APPENDIX
46├── Detailed financial statements
47├── Complete KPI dashboard
48└── Supporting analysis

1import matplotlib.pyplot as plt
2import matplotlib.patches as mpatches
3import numpy as np
4
5def create_executive_dashboard():
6    """Create a clean executive dashboard"""
7    
8    fig = plt.figure(figsize=(16, 10))
9    fig.suptitle('Q1 2024 Executive Dashboard', fontsize=16, fontweight='bold', y=0.98)
10    
11    # Layout: 3 rows
12    gs = fig.add_gridspec(3, 4, hspace=0.3, wspace=0.3)
13    
14    # Row 1: Big Numbers (KPIs)
15    # Revenue
16    ax1 = fig.add_subplot(gs[0, 0])
17    ax1.text(0.5, 0.7, '$8.2M', fontsize=28, fontweight='bold', ha='center', va='center', color='green')
18    ax1.text(0.5, 0.3, 'Revenue\n+15% vs LY', fontsize=10, ha='center', va='center', color='gray')
19    ax1.set_xlim(0, 1)
20    ax1.set_ylim(0, 1)
21    ax1.axis('off')
22    
23    # Customers
24    ax2 = fig.add_subplot(gs[0, 1])
25    ax2.text(0.5, 0.7, '15,200', fontsize=28, fontweight='bold', ha='center', va='center', color='steelblue')
26    ax2.text(0.5, 0.3, 'Customers\n+22% vs LY', fontsize=10, ha='center', va='center', color='gray')
27    ax2.axis('off')
28    
29    # NPS
30    ax3 = fig.add_subplot(gs[0, 2])
31    ax3.text(0.5, 0.7, '48', fontsize=28, fontweight='bold', ha='center', va='center', color='orange')
32    ax3.text(0.5, 0.3, 'NPS Score\n-2 vs target', fontsize=10, ha='center', va='center', color='gray')
33    ax3.axis('off')
34    
35    # Margin
36    ax4 = fig.add_subplot(gs[0, 3])
37    ax4.text(0.5, 0.7, '68%', fontsize=28, fontweight='bold', ha='center', va='center', color='green')
38    ax4.text(0.5, 0.3, 'Gross Margin\n+3pp vs LY', fontsize=10, ha='center', va='center', color='gray')
39    ax4.axis('off')
40    
41    # Row 2: Trend and Breakdown
42    # Revenue trend
43    ax5 = fig.add_subplot(gs[1, :2])
44    months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun']
45    actual = [2.1, 2.3, 2.5, 2.6, 2.8, 3.0]
46    target = [2.0, 2.2, 2.4, 2.5, 2.7, 2.9]
47    ax5.fill_between(months, actual, alpha=0.3, color='green')
48    ax5.plot(months, actual, 'go-', linewidth=2, label='Actual')
49    ax5.plot(months, target, 'k--', label='Target')
50    ax5.set_ylabel('Revenue ($M)')
51    ax5.set_title('Revenue Trend', fontweight='bold')
52    ax5.legend(loc='upper left')
53    ax5.set_ylim(0, 3.5)
54    
55    # Revenue breakdown
56    ax6 = fig.add_subplot(gs[1, 2:])
57    segments = ['Enterprise', 'Mid-Market', 'SMB', 'Consumer']
58    values = [3.5, 2.8, 1.5, 0.4]
59    colors = ['#2ecc71', '#3498db', '#9b59b6', '#e74c3c']
60    bars = ax6.barh(segments, values, color=colors)
61    ax6.set_xlabel('Revenue ($M)')
62    ax6.set_title('Revenue by Segment', fontweight='bold')
63    ax6.bar_label(bars, fmt='$%.1fM')
64    
65    # Row 3: Issues and Actions
66    ax7 = fig.add_subplot(gs[2, :2])
67    ax7.axis('off')
68    issues_text = """
69�� KEY ISSUES
70━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
711. CAC increased 18% - Marketing review in progress
722. Support backlog at 72hrs - Adding 2 headcount
733. Enterprise deal slipped to Q2 - Risk $500K
74    """
75    ax7.text(0.05, 0.95, issues_text, fontsize=10, fontfamily='monospace',
76             verticalalignment='top', transform=ax7.transAxes)
77    
78    ax8 = fig.add_subplot(gs[2, 2:])
79    ax8.axis('off')
80    actions_text = """
81�� KEY ACTIONS (Next 30 Days)
82━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
831. Launch loyalty program - Expected: +5% retention
842. Deploy mobile app v2 - Expected: +10% engagement
853. Close 3 enterprise deals - Expected: +$800K
86    """
87    ax8.text(0.05, 0.95, actions_text, fontsize=10, fontfamily='monospace',
88             verticalalignment='top', transform=ax8.transAxes)
89    
90    plt.savefig('executive_dashboard.png', dpi=150, bbox_inches='tight', facecolor='white')
91    plt.show()
92
93create_executive_dashboard()

1def create_status_indicator(value, target, higher_is_better=True):
2    """Create traffic light status"""
3    if higher_is_better:
4        pct = (value - target) / target * 100
5        if pct >= 0:
6            return '🟢', 'On Track'
7        elif pct >= -10:
8            return '🟡', 'Monitor'
9        else:
10            return '🔴', 'At Risk'
11    else:
12        pct = (target - value) / target * 100
13        if pct >= 0:
14            return '🟢', 'On Track'
15        elif pct >= -10:
16            return '🟡', 'Monitor'
17        else:
18            return '🔴', 'At Risk'
19
20# Example usage
21metrics = [
22    ('Revenue', 2.5, 2.3, True),
23    ('Customers', 12500, 13000, True),
24    ('Churn Rate', 5.2, 4.0, False),
25    ('CAC', 125, 100, False),
26    ('NPS', 42, 50, True)
27]
28
29print("\nKPI Status Summary")
30print("=" * 50)
31for name, actual, target, higher_better in metrics:
32    light, status = create_status_indicator(actual, target, higher_better)
33    print(f"{light} {name:15s} | Actual: {actual:>8} | Target: {target:>8} | {status}")

1def add_sparkline(ax, data, color='steelblue'):
2    """Add simple sparkline to axis"""
3    x = range(len(data))
4    ax.plot(x, data, color=color, linewidth=1.5)
5    ax.fill_between(x, data, alpha=0.2, color=color)
6    ax.scatter([len(data)-1], [data[-1]], color=color, s=30, zorder=5)
7    ax.axis('off')
8    ax.set_ylim(min(data) * 0.9, max(data) * 1.1)
9
10# Create sparkline dashboard
11fig, axes = plt.subplots(4, 1, figsize=(3, 6))
12
13metrics_data = {
14    'Revenue': [2.1, 2.2, 2.3, 2.5, 2.6, 2.8],
15    'Customers': [11.0, 11.5, 12.0, 12.5, 13.0, 13.8],
16    'NPS': [45, 44, 46, 48, 47, 48],
17    'Churn': [4.5, 4.8, 5.0, 5.2, 5.1, 5.0]
18}
19
20for ax, (name, data) in zip(axes, metrics_data.items()):
21    add_sparkline(ax, data)
22    ax.set_ylabel(name, rotation=0, ha='right', va='center', fontsize=9)
23    # Add current value
24    ax.text(1.05, 0.5, f'{data[-1]}', transform=ax.transAxes, 
25            fontsize=10, fontweight='bold', va='center')
26
27plt.suptitle('6-Month Trends', fontsize=11, fontweight='bold')
28plt.tight_layout()
29plt.show()

1EXECUTIVE PRESENTATION STRUCTURE
2================================
3 
4MINUTE 1: THE HEADLINE
5└── Start with the conclusion
6    "Revenue is up 15%, we're on track for annual target,
7     but customer acquisition cost needs attention."
8 
9MINUTE 2: THE EVIDENCE
10└── 3-5 key numbers that prove your headline
11    • Revenue: $8.2M (+15% YoY)
12    • Customers: 15,200 (+22%)
13    • CAC: $125 (+18% - above target)
14 
15MINUTE 3: THE INSIGHT
16└── What's driving the numbers
17    "Growth is strong because of product launch.
18     CAC increase due to competitive pressure in paid channels."
19 
20MINUTE 4: THE RECOMMENDATION
21└── What action you're proposing
22    "Reallocate $200K from paid to organic content.
23     Expected result: CAC back to $100 in Q2."
24 
25MINUTE 5: THE ASK
26└── What you need from them
27    "I need approval for budget reallocation by Friday
28     and a decision on expanding the sales team."
29 
30━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
31Then: Answer questions with backup slides
32━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

1COMMON EXECUTIVE QUESTIONS & HOW TO PREPARE
2 
3Q: "What's driving this?"
4A: Have root cause analysis ready
5   "The increase is driven by: 60% pricing, 25% competition, 15% seasonality"
6 
7Q: "How confident are you?"
8A: Be honest with uncertainty ranges
9   "We're 80% confident in this range. Best case: +20%, worst case: +5%"
10 
11Q: "What are the risks?"
12A: Top 3 risks + mitigation
13   "Main risk is competitive response. Mitigation: loyalty program launch"
14 
15Q: "What do you need from us?"
16A: Clear ask with options
17   "Option A: $200K budget (recommended). Option B: $100K with slower timeline"
18 
19Q: "When will we see results?"
20A: Specific timeline with milestones
21   "Initial results in 4 weeks. Full impact visible by end of Q2"
22 
23Q: "What happens if we do nothing?"
24A: Quantify the cost of inaction
25   "If we don't act, we'll lose $500K in Q2 based on current trajectory"

1import pandas as pd
2import numpy as np
3from datetime import datetime
4from jinja2 import Template
5
6def generate_automated_report(data, period='Weekly'):
7    """Generate automated executive report"""
8    
9    template = Template("""
10{{ header }}
11Report Generated: {{ timestamp }}
12Period: {{ period }}
13
14═══════════════════════════════════════════════════════════
15 EXECUTIVE SUMMARY
16═══════════════════════════════════════════════════════════
17
18Overall Status: {{ overall_status }}
19
20Revenue:      ${{ revenue }}M ({{ revenue_vs_target }} vs target)
21Customers:    {{ customers }} ({{ customer_vs_target }} vs target)
22Key Concern:  {{ key_concern }}
23
24═══════════════════════════════════════════════════════════
25 KPI DASHBOARD
26═══════════════════════════════════════════════════════════
27
28{% for kpi in kpis %}
29{{ kpi.status }} {{ kpi.name }}: {{ kpi.value }} (Target: {{ kpi.target }})
30{% endfor %}
31
32═══════════════════════════════════════════════════════════
33 ACTION ITEMS
34═══════════════════════════════════════════════════════════
35
36{% for action in actions %}
37{{ loop.index }}. {{ action }}
38{% endfor %}
39
40═══════════════════════════════════════════════════════════
41    """)
42    
43    # Calculate metrics
44    revenue = 2.5
45    revenue_target = 2.3
46    revenue_vs = '+8.7%' if revenue > revenue_target else '-8.7%'
47    
48    customers = 15200
49    customer_target = 14000
50    customer_vs = '+8.6%' if customers > customer_target else '-8.6%'
51    
52    kpis = [
53        {'name': 'Revenue', 'value': f'${revenue}M', 'target': f'${revenue_target}M', 'status': '✅'},
54        {'name': 'Customers', 'value': f'{customers:,}', 'target': f'{customer_target:,}', 'status': '✅'},
55        {'name': 'Churn Rate', 'value': '5.2%', 'target': '4.0%', 'status': '⚠️'},
56        {'name': 'NPS', 'value': '48', 'target': '50', 'status': '⚠️'},
57        {'name': 'CAC', 'value': '$125', 'target': '$100', 'status': '❌'}
58    ]
59    
60    actions = [
61        'Review marketing spend allocation (Owner: CMO, Due: Friday)',
62        'Implement churn prevention campaign (Owner: CS, Due: Next Monday)',
63        'Schedule enterprise pipeline review (Owner: Sales, Due: Thursday)'
64    ]
65    
66    report = template.render(
67        header='EXECUTIVE FLASH REPORT',
68        timestamp=datetime.now().strftime('%Y-%m-%d %H:%M'),
69        period=period,
70        overall_status='✅ ON TRACK',
71        revenue=revenue,
72        revenue_vs_target=revenue_vs,
73        customers=f'{customers:,}',
74        customer_vs_target=customer_vs,
75        key_concern='CAC above target - marketing efficiency review needed',
76        kpis=kpis,
77        actions=actions
78    )
79    
80    return report
81
82# Generate report
83report = generate_automated_report(None)
84print(report)