1. Overview of the Problem
In the competitive retail industry, retaining customers is more cost-effective than acquiring new ones. My client, a mid-sized e-commerce platform, noticed a 25% drop in repeat purchases over six months. I was tasked to find the "Why" behind this trend.
2. The Data Stack Used
- SQL: For querying 1.2 million rows of transaction data.
- Python (Pandas): For data cleaning and handling missing values.
- Power BI: For creating a high-impact executive dashboard.
3. Key Insights Discovered
After performing RFM (Recency, Frequency, Monetary) Analysis, I uncovered three critical insights:
Insight A: 40% of churned customers had a shipping delay of more than 5 days.
Insight B: Customers who used "Discount Codes" on their first purchase were 3x more likely to churn.
4. Strategic Recommendations
Based on the data, I proposed the following actions:
- Implement an Automated Email Trigger for customers who haven't visited in 30 days.
- Revise the "First Purchase" discount to a "Second Purchase" loyalty reward.
5. Final Impact
The implementation of these strategies resulted in a 12% reduction in churn within the first quarter and an estimated $50,000 increase in projected annual revenue.
