Power BI dashboard analyzing customer drop-off, conversion behavior, revenue leakage, and high-risk segments in an e-commerce environment using 3,500+ simulated customer journey records.
Customer Intelligence dashboard built using SQL and Power BI to analyze customer behavior, retention patterns, sales trends, and revenue contribution across customer segments.
Customer segmentation project using the RFM (Recency, Frequency, Monetary) model to categorize customers based on purchasing behavior. Includes automated data cleaning, RFM scoring, business segment labeling, visualization, and K-Means clustering for unsupervised segmentation.
This project is built for the **Predictive Analytics & Customer Intelligence Track**. It addresses real-world transactional datasets by converting unstructured purchase logs into structured behavioral metrics, supervised training datasets, and practical prioritization strategies.
Power BI dashboard analyzing WaveCon’s post-5G performance—revenue impact, churn signals, city trends, plan-wise performance, and strategic recommendations.
End-to-end Python & SQL churn analysis pipeline built on 1,000+ customer records. Features database integration, exploratory data analysis, and stakeholder-ready Matplotlib/Seaborn visualizations uncovering revenue risk and key retention drivers.
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