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Marketing Campaign ROI Analysis — Complete Project Guide

SECTION 01Project overview

Here's what you'll build in this project:

• Business problem: A company wants to understand which marketing channels are most effective and how to optimize budget allocation.
• Your solution: A marketing ROI dashboard that tracks campaign performance, attribution, and customer acquisition cost across channels.
• Tools: Python (pandas, matplotlib), Tableau/Power BI, or Excel.
• Outcome: A portfolio-ready project that demonstrates marketing analytics and business strategy skills.

SECTION 02Data source

Here are the best data sources for this project:

SECTION 03Key metrics and KPIs

Here are the key metrics your marketing ROI dashboard should track:

SECTION 04Attribution analysis

Here's how to analyze channel attribution:

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

df = pd.read_csv('marketing_data.csv')

# Channel performance
channel_performance = df.groupby('channel').agg({
    'spend': 'sum',
    'revenue': 'sum',
    'leads': 'sum',
    'conversions': 'sum'
}).reset_index()

# Calculate ROI per channel
channel_performance['roi'] = (channel_performance['revenue'] - channel_performance['spend']) / channel_performance['spend'] * 100
channel_performance['cac'] = channel_performance['spend'] / channel_performance['conversions']
channel_performance['conversion_rate'] = channel_performance['conversions'] / channel_performance['leads'] * 100

# Sort by ROI
channel_performance_sorted = channel_performance.sort_values('roi', ascending=False)

print("Channel Performance:")
print(channel_performance_sorted[['channel', 'roi', 'cac', 'conversion_rate']])

# ROI calculation function
def calculate_roi(spend, revenue):
    return ((revenue - spend) / spend) * 100

# Attribution — last-click analysis
attribution = df.groupby('last_click_channel').agg({
    'spend': 'sum',
    'revenue': 'sum'
}).reset_index()
attribution['roi'] = calculate_roi(attribution['spend'], attribution['revenue'])

# Visualize
fig, axes = plt.subplots(1, 2, figsize=(14, 5))
sns.barplot(data=attribution, x='last_click_channel', y='roi', ax=axes[0])
axes[0].set_title('ROI by Last Click Channel')
axes[0].set_xlabel('Channel')
axes[0].set_ylabel('ROI (%)')

sns.barplot(data=attribution, x='last_click_channel', y='revenue', ax=axes[1])
axes[1].set_title('Revenue by Last Click Channel')
axes[1].set_xlabel('Channel')
axes[1].set_ylabel('Revenue')
plt.tight_layout()
plt.show()

SECTION 05Dashboard design

Here's how to design your marketing ROI dashboard:

• Top section: KPI cards — Overall ROI, CAC, Conversion Rate, Total Revenue.
• Middle section: ROI by channel, revenue trends, conversion trends.
• Bottom section: Channel performance table, attribution analysis, budget allocation recommendations.
• Filters: Date range, campaign, channel.

SECTION 06Portfolio presentation

Here's how to present this project to employers:

• GitHub: Upload your code, data preparation scripts, and dashboard file.
• README: Write a clear README with project overview, metrics, and key insights.
• Executive summary: Include a 1-page summary for business stakeholders.
• Screenshots: Add screenshots of your dashboard and key insights.
• LinkedIn post: Share your project with a brief explanation of the business problem you solved.

SECTION 07Interview Q&A — marketing ROI

SECTION 08Test yourself — marketing ROI quiz

SECTION 09Frequently asked questions

SECTION 10Related reads