Marketing Analytics / Python Project
E-commerce Marketing Dashboard
An interactive dashboard that brings store sales and advertising performance into one place, helping marketers evaluate acquisition costs, campaign efficiency, and revenue trends.
Interactive demo
Explore the dashboard
Adjust the date range, compare Google Ads with Meta Ads, inspect campaign results, and export reports. The demo uses fictional data.
Portfolio demonstration only. All included sales, customer, and advertising figures are fictional. If the embedded app does not load, use “Launch Live Dashboard” to open it directly. Avoid uploading sensitive business or customer data to a publicly hosted demonstration.
What it measures
From reporting to decisions
Store performance
Review net sales, order volume, average order value, new customers, and daily revenue trends.
Paid media efficiency
Compare Google and Meta spend, platform-reported ROAS, blended acquisition cost, and marketing efficiency ratio.
Interactive exploration
Filter by date and advertising platform, examine campaign summaries, and export results for further analysis.
How it was built
Practical marketing analytics, written in Python.
I built this project to demonstrate how e-commerce and advertising exports can be combined into an accessible decision-making interface. The dashboard uses Python for data processing, Pandas for analysis, and Streamlit for its interactive front end.
Storewide sales and platform-attributed revenue are deliberately kept distinct: advertising platforms may claim overlapping conversions, so platform revenue should not be added to store sales.
- CSV-based Shopify sales and paid-media sample inputs
- Reusable KPI calculations and campaign comparisons
- Automated unit tests and documented setup
- Public GitHub source and live demo
Work with me
Want a clearer view of your marketing performance?
I help e-commerce businesses connect advertising data with sales results and identify practical opportunities to improve performance.