This project was created as a marketing data case study using a fictional dataset from Kaggle. The goal was to answer key business questions using SQL views and deliver strategic, data-backed insights for campaign optimization.
You're a new data analyst on the marketing team. The company has run multiple campaigns, and leadership wants a performance review. They care most about ROI, customer engagement, and understanding which campaigns were most effective. They want clear, actionable insights β not just a wall of metrics.
- Which campaign(s) had the highest return on investment (ROI)?
- What demographic segments responded best to which campaigns?
- Which channels (email, social media, etc.) are the most effective?
- Whatβs the profile of a βhigh-valueβ customer?
campaign_roi_summary.sql
β Calculates ROI per campaigncampaign_top_audience.sql
β Highlights top-performing audience segments by campaignchannel_metrics_ranked.sql
β Ranks marketing channels by performancecustomer_segment_value_score.sql
β Profiles high-value customers based on spend and behavior
- MySQL
- SQL Views
- Kaggle: Marketing Campaign Performance Dataset
You can view each SQL file individually to see how each business question was addressed. These queries were written and tested in MySQL Workbench. The views can be recreated by importing the dataset and running the scripts provided.
This project uses fictional marketing data from Kaggle and is not representative of any real clients or campaigns.