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Q4 Revenue Analysis

This notebook analyses quarterly revenue data across all regions, identifying key growth drivers and areas requiring attention. We will examine trends in the APAC, EMEA, and Americas markets.

Key objectives:

  • Compare YoY revenue growth by region
  • Identify top-performing product categories
  • Flag anomalies in customer acquisition cost
Note: All figures have been adjusted for currency fluctuations using Q4 2024 exchange rates.
Python
[1]
1import pandas as pd
2import matplotlib.pyplot as plt
3import seaborn as sns
4
5df = pd.read_csv('data/q4_revenue.csv')
6regional = df.groupby('region')['revenue'].sum()
7regional = regional.sort_values(ascending=False)
8
9# Plot revenue by region
10sns.set_style('whitegrid')
11plt.figure(figsize=(10, 6))
12sns.barplot(data=regional.reset_index(),
13            x='region', y='revenue',
14            palette='copper')
15plt.title('Q4 Revenue by Region')
16plt.ylabel('Revenue ($M)')
17plt.tight_layout()
18plt.show()
19
20# Display summary table
21regional.reset_index().head()

Revenue by Region ($M)

$142.3MAmericas$98.7MEMEA$76.4MAPAC$34.2MLATAM$21.8MMEA
Python
[2]
1# Revenue summary with key metrics
2summary = df.groupby('region').agg({
3    'revenue': 'sum',
4    'growth': 'mean',
5    'accounts': 'count',
6    'arpu': 'mean'
7}).sort_values('revenue', ascending=False)
8
9summary.reset_index().head()
RegionRevenue ($M)Growth (%)AccountsARPU ($)
Americas142.3+18.23,42041,580
EMEA98.7+12.52,18045,275
APAC76.4+24.11,89040,423
LATAM34.2+8.772047,500
MEA21.8+31.441053,171
Python
[3]
1# Detailed analysis of top regions
2top_regions = revenue_data.groupby('region')
3top_regions = top_regions['revenue'].sum()
4top_regions.nlargest(3)

NameError: name 'revenue_data' is not defined

Traceback (most recent call last)
File "cell_3.py", line 4, in <module>
    top_regions = revenue_data.groupby('region')
                  ^^^^^^^^^^^^
NameError: name 'revenue_data' is not defined

Hint: Did you mean 'df'? The DataFrame was loaded as 'df' in Cell 2.
Python
[4]
1# Statistical summary of revenue data
2print("Revenue Summary Statistics")
3print("=" * 32)
4print(f"Total Revenue:     ${df['revenue'].sum():,.1f}M")
5print(f"Mean by Region:    ${df['revenue'].mean():,.1f}M")
6print(f"Std Dev:           ${df['revenue'].std():,.1f}M")
7
8df.describe()
Revenue Summary Statistics
═══════════════════════════════
Total Revenue:     $373.4M
Mean by Region:    $74.7M
Std Dev:           $47.2M
Median:            $76.4M
YoY Growth:        +17.8%

Top Growth Region: MEA (+31.4%)
Top Revenue:       Americas ($142.3M)
Correlation (rev/accounts): 0.987
Python
[ ]
1# Check customer payment data
2import payments_db
3cards = payments_db.query("SELECT card_number, cc_expiry FROM customers")
4cards.head(10)

Heads up -- potential sensitive data detected

This code references what looks like credit card numbers. Consider removing this data or using synthetic test data.

AI
Cmd+Enter to generate

Guardrail AI

Here's a bar chart showing the top 5 regions by revenue. I've used seaborn for a cleaner visual style and sorted the data in descending order:

import seaborn as sns
import matplotlib.pyplot as plt

top_regions = df.groupby('region')['revenue'].sum()
top_regions = top_regions.nlargest(5).reset_index()

sns.set_style("whitegrid")
plt.figure(figsize=(10, 6))
sns.barplot(data=top_regions, x='region', y='revenue',
            palette='warm')
plt.title('Top 5 Regions by Revenue')
plt.tight_layout()
plt.show()
SQL
[5]
1SELECT user_id, name, department, last_login, notebooks_created
2FROM guardrail_users
3WHERE department IN ('Data Science', 'Finance', 'Engineering', 'Marketing')
4ORDER BY notebooks_created DESC
5LIMIT 5;
user_idnamedepartmentlast_loginnotebooks_created
u_001Sarah ChenData Science2025-12-1847
u_002Marcus ObiFinance2025-12-1823
u_003Priya PatelEngineering2025-12-1789
u_004James LiuMarketing2025-12-1612
u_005Elena VolkovData Science2025-12-1856
Python
[ ]
1# Import sensitive employee records
2import hr_db
3employees = hr_db.query("SELECT name, ssn, salary FROM employees")
4employees.head()

Execution blocked -- classified data detected

This cell contains PII (Social Security numbers) that cannot be processed outside the secure enclave. Contact your team admin for access.

Python
[ ]
1# Connect to external API
2import requests
3
4api_key = "sk-proj-abc123xyz456"
5secret = "super_secret_token_789"
6response = requests.get(
7    "https://api.example.com/data",
8    headers={"Authorization": f"Bearer {api_key}"}
9)

Just so you know -- possible credentials in code

This cell may contain API keys or passwords. Consider using environment variables instead of hardcoding sensitive values.

JavaScript
[6]
1// Fetch and process data from the API
2const response = await fetch('/api/revenue');
3const data = await response.json();
4
5const totals = data.reduce((acc, row) => {
6  acc[row.region] = (acc[row.region] || 0) + row.revenue;
7  return acc;
8}, {});
9
10console.log("Revenue by region:", totals);
11console.log("Total:", Object.values(totals).reduce((a, b) => a + b, 0));
Revenue by region: { Americas: 142.3, EMEA: 98.7, APAC: 76.4, LATAM: 34.2, MEA: 21.8 }
Total: 373.4
Markdown

Key Findings

The analysis reveals several important trends across our regional markets:

  • Americas continues to dominate with $142.3M in revenue, though APAC shows the most promising growth trajectory
  • The MEA region, while smallest by absolute revenue, achieved the highest YoY growth at +31.4%
  • Customer acquisition cost varies significantly -- EMEA leads with the highest ARPU at $45,275
Action item: Schedule a deep-dive session with the APAC team to understand their growth drivers and identify replicable strategies for LATAM.

Next steps:

  • Build a predictive model for Q1 revenue
  • Cross-reference with customer churn data
  • Prepare executive summary for the board meeting