This project explores why revenue growth has slowed even though site traffic remains high. I built it as a learning project using real-world data to understand where the issue could be coming from.
I cleaned and prepared the dataset in Python, ran deeper queries in SQL, and explored patterns through a series of analyses: monthly cohort retention, a conversion funnel split by traffic source, and RFM customer segmentation. I then brought the findings together in a Tableau dashboard and a written report.
The cohort analysis helped me see how quickly engagement drops. Retention starts at about 39% in Month 0 and falls below 25% by Month 3–4. The funnel analysis showed that mobile brings in most visitors—around 90%—yet converts no better than web. The largest drop happened right after users added an item to their cart, which pointed me toward possible checkout confusion or hesitation.
RFM segmentation added a different angle: about 20% of customers (Champions and Big Spenders) bring in most revenue, while more than half fall into Occasional or Lost categories. This helped me link the retention findings with customer value and understand where future improvements could be done.
