Ad Performance Intelligence: Facebook vs Instagram
Project Type
Marketing Analytics | Performance Intelligence | Predictive Modeling | Revenue Optimization
Project Type
Marketing Analytics | Performance Intelligence | Predictive Modeling | Revenue Optimization
This project analyzed ad performance across Facebook and Instagram to determine which platform delivers better conversion efficiency, cost-effectiveness, and revenue impact.
Using a combination of Power BI, statistical analysis, and regression modeling, the study revealed that Facebook consistently outperforms Instagram in conversion rate, cost efficiency, and revenue generation potential.
A predictive model was developed to estimate conversions based on ad clicks, enabling forward-looking campaign planning rather than purely historical reporting.
The findings support a clear budget optimization strategy: shift investment toward Facebook due to higher ROI and stronger conversion efficiency.
The business needed to understand:
Which platform (Facebook or Instagram) delivers better marketing ROI
How efficiently clicks translate into conversions across platforms
Whether future conversions can be predicted from engagement data
How to optimize ad spend allocation across channels
Identified Facebook as the higher-performing conversion platform across all months
Discovered Instagram has consistently higher cost per click and weaker conversion efficiency
Built a predictive model to estimate expected conversions from click volume
Provided evidence-based guidance for budget reallocation toward higher ROI channel (Facebook)
Supported decision-making for conversion-focused marketing optimization
Facebook consistently delivered higher conversion rates (CVR) than Instagram across the full time period
Instagram showed higher volatility in engagement and weaker conversion consistency
Cost per click (CPC) was significantly lower on Facebook, improving overall campaign efficiency
Engagement metrics (CTR) were similar across platforms, but conversion efficiency differed significantly, showing CTR alone is not a reliable success metric
A statistical test confirmed that Facebook’s conversion performance is significantly stronger than Instagram’s, validating that the difference is not due to random variation.
A linear regression model was developed:
Formula:
Conversions = -0.89277 + 0.8182 × Clicks
Positive relationship between clicks and conversions
Higher clicks lead to increased conversions
Model explains a measurable portion of conversion behavior and can be used for forecasting future campaign outcomes
Assuming $5,000 revenue per conversion:
Total clicks: 67,044
Conversions: 604
Revenue: $3,020,000
Profit: $2,520,000
ROI: 504% (≈ $5.04 return per $1 spent)
Facebook ads demonstrate strong scalability potential with high return efficiency under optimized targeting conditions.
Reallocate advertising budget toward Facebook due to superior conversion efficiency
Reduce Instagram spend or restructure targeting strategy
Prioritize conversion-focused optimization over engagement metrics (CTR)
Run A/B tests to improve ad creative and landing page conversion rates
Use predictive model outputs to forecast campaign performance before scaling spend
This analysis demonstrates that marketing performance should not be evaluated based on engagement alone.
Instead, combining statistical analysis, predictive modeling, and revenue simulation enables:
Better platform selection
More efficient ad spend allocation
Higher ROI-driven marketing decisions
Data-backed forecasting of campaign outcomes
Power BI | Python (Pandas, Statsmodels) | Regression Analysis | Hypothesis Testing | Marketing Analytics | Predictive Modeling | Data Visualization | ROI Analysis
I independently executed the full analytics workflow:
Designed data pipeline for ad performance tracking
Automated data collection using Google Forms and Sheets
Performed data cleaning and transformation
Built Power BI dashboards for descriptive insights
Conducted statistical hypothesis testing (Welch T-test)
Developed regression model to predict conversions from clicks
Performed ROI and profitability analysis
Translated insights into actionable marketing recommendations