The goal of this project was to analyze social media advertising data to identify key trends and performance metrics that can help optimize marketing strategies.
Using Microsoft Power BI, I transformed raw data into an interactive dashboard that visualizes campaign performance across various dimensions such as demographics, platforms, and timeframes.
Dataset Used : Kaggle Link
The dashboard tracks the following high-level metrics:
- Total Impressions: 340K
- Total Clicks: 40K
- Click-Through Rate (CTR): 11.79%
- Total Revenue: $101.55K
- Conversion Rate (CVR): 5.07%
- Return on Investment (ROI): 333.2%
Platform Analysis:
- Comparative analysis of CTR reveals a tight competition, with Instagram (11.86%) maintaining a marginal lead over Facebook (11.76%).
Demographic Performance:
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Age Group: The 35-44 age group is the dominant revenue driver, contributing $42.6K (41.68%) of the total, followed significantly by the 55-65 age group ($32.2K).
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Gender: Male users generated the highest revenue at $56K, significantly outperforming Female users ($36K) and the 'Other' category ($10K).
Temporal Trends:
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Monthly Performance: Both Total Clicks and ROI showed a steady upward trend peaking in June and July, before experiencing a sharp decline in August.
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Time of Day: Engagement (CTR) is highest in the Afternoon (11.91%) and Evening (11.83%), suggesting these are the optimal windows for ad scheduling compared to Night (11.69%).
Dynamic Filtering:
- The dashboard includes interactive slicers allowing users to drill down data by specific Countries (e.g., Australia, Brazil, Canada) and Ad Types (Carousel, Image, Stories, Video).
- Microsoft Power BI (Data Visualization & Modeling)
- Power Query (Data Cleaning & Transformation)
- DAX (Calculated Measures for ROI, CTR, etc.)
This analysis provides actionable insights for marketing teams to allocate budgets more effectively, target high-performing age groups, and schedule ads during peak engagement times.