Marketing Campaign Performance Analysis

Objective

To evaluate and optimize Facebook and Google AdWords marketing campaigns using Python and Power BI, aiming to improve conversions and reduce cost per acquisition.

Tools Used

• Python (Pandas, Matplotlib, Seaborn)
• Power BI (for dashboard visualizations)
• Jupyter Notebook

Dataset Overview

Daily-level ad campaign data covering 1 year for both Facebook and Google AdWords. Key features include:

• Views, Clicks, Conversions, Spend
• Derived metrics: CTR, CPC, Conversion Rate, Cost per Conversion

Business Questions

• Which platform performs better in terms of CTR and conversion rate?
• What are the monthly trends in cost-efficiency?
• Do higher costs result in more conversions?
• Which campaigns offer the best ROI?

Key Insights (Summary)

• Conversions increased over the year by ~16%
• Cost per conversion dropped from $14.4 to $12.1
• Facebook outperformed AdWords in both CTR and Conversion Rate consistently
• Clicks showed a stronger correlation with conversions (0.76) than cost
• Lower CPC leads to better conversion efficiency

Python Data Analysis

I used Python to clean the data, perform exploratory data analysis (EDA), and generate insights before building dashboards in Power BI.

Python Tasks Performed

• Data cleaning: handling nulls, formatting types
• Feature engineering: derived CTR, CPC, Conv. Rate
• Visualizations: Line plots, Correlation heatmaps
• Correlation analysis for identifying drivers of conversions

Example Python Visuals

Conversions Over Time

Cost per Conversion Over Time

Click Through Rate (CTR) Over Time

Conversion Rate Over Time

Correlation Heatmap

Python Insights

• Conversions steadily increased across the months
• Strong correlation between clicks and conversions (0.76)
• Negative correlation between CPC and Conv. Rate (–0.54 to –0.64)
• Facebook showed higher efficiency in cost per conversion

Power BI Dashboards

I used Power BI to visualize the cleaned data and answer key business questions interactively.

Dashboard Pages:

• Executive Summary
• Monthly Trend Analysis
• Funnel & Cost Efficiency
• Correlation Insights

Sample Visuals

Dashboard Video

Final Recommendations

• Allocate more budget to Facebook for better ROI
• Optimize CPC to increase conversion rate
• Monitor cost-efficiency monthly and watch for seasonal patterns
• Prioritize increasing high-quality clicks, not just spend