Built this one for an e-commerce customer behavior project - 18,000+ customers, segmented using RFM (Recency, Frequency, Monetary) analysis.
The interesting part wasn't the segmentation itself, it was what it revealed: nearly 22% of customers were "at risk" of churning, and most businesses wouldn't know that until those customers had already stopped buying.
This is the kind of insight that's usually sitting in your order data already, it just needs the right lens. Happy to take a look at your numbers if you're curious what's hiding in yours.
Tools used: Power BI, SQL, Python (Pandas, Scikit-learn for clustering)
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Executed a Meta Ads campaign for a fitness studio, generating 98 qualified leads with a ₹35.56 cost per lead. Optimized audience targeting, creatives, and campaign performance using Facebook and Instagram Ads.
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Developed an interactive Financial Stock Analysis Dashboard in Power BI using historical stock market data from the Yahoo Finance API. The dashboard compares the performance of AAPL, AMZN, GOOGL, and MSFT through return analysis, volatility metrics, monthly trends, and key performance indicators. Interactive filters and dynamic visualizations help users evaluate stock performance and make data-driven investment decisions.
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Designed a Power BI dashboard to analyze Netflix content using Power Query and DAX. The dashboard highlights key KPIs, genre trends, content distribution, yearly additions, country-wise analysis, and rating insights with interactive visualizations.
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Built an interactive E-Commerce Customer Behavior Dashboard in Power BI to analyze revenue, customer segments, order trends, conversion funnel, and regional performance. Designed to help businesses track KPIs, identify growth opportunities, and make data-driven decisions through clear and actionable visualizations.