Project Overview:
In this project, I combined marketing campaign performance data (Google Ads, Meta Ads) with e-commerce GA4 data to analyze sales performance, awareness generation, and user behavior. The goal was to understand how different campaigns contributed to impressions, clicks, sessions, and revenue, and to provide recommendations for future campaign improvements.
Business Problem:
A mid-sized e-commerce company is running paid campaigns across Google Ads and Meta Ads, but marketing spend has increased while revenue growth has not kept pace. Management wants to understand which campaigns, channels, audiences, and funnel stages are driving results, where budget is being wasted, and how campaign performance can be improved.
Data Models:
Project Process:
This project evaluates campaign performance across revenue generation, brand awareness, advertising cost, and overall efficiency. It presents key marketing metrics including Total Revenue, Total Costs, CVR, CPA, ROAS, Cost to Revenue, Impressions, Clicks, CTR, CPM, Funnel Dropoff Rate, Funnel Completion Rate, etc., and compares campaign results using quadrant analysis to identify high-performing, cost-efficient, and underperforming campaigns. The project also analyzes the customer purchase journey through funnel analysis, highlighting the stages with the greatest drop-off and identifying opportunities to improve conversion performance and the overall user experience.
Analytical Tools:
Power BI, SQL Server