Project: E-Commerce Sales Insights

📊 Business Problem

Identifying seasonal churn rates and declining customer lifetime value (CLV) across the Q3-Q4 period. The e-commerce platform was experiencing unexpected customer attrition during peak shopping seasons.

🧪 Methodology

Extracted data via SQL from transactional databases, performed exploratory data analysis (EDA) in Python (Pandas, NumPy), and handled missing values using mean-imputation and forward-fill strategies. Applied RFM segmentation and cohort analysis.

Tech Stack: Python Pandas SQL Tableau

Duration: 3 weeks

[Visualization: Sales Trend Correlation Heatmap]

📈 Key Insights & Results

• Discovered a 15% drop in retention linked to checkout latency issues.
• Automated the weekly reporting pipeline, saving 4 hours of manual entry per week.
• Recommended a targeted email campaign that recovered 5% of churned users.
• Identified product categories with highest churn, enabling strategic inventory optimization.

Project: Healthcare Patient Analytics

📊 Business Problem

A healthcare provider needed to reduce patient no-show rates (currently at 22%) and optimize appointment scheduling to improve operational efficiency and reduce lost revenue.

🧪 Methodology

Built a predictive model using logistic regression and random forest algorithms on historical patient data (35,000+ records). Engineered features from appointment history, patient demographics, and clinical data. Implemented cross-validation with stratified k-fold approach.

Tech Stack: Python Scikit-learn XGBoost SQL

Duration: 4 weeks

[Visualization: Model Performance Comparison - ROC Curves & Confusion Matrix]

📈 Key Insights & Results

• Developed predictive model achieving 87% accuracy in identifying high-risk no-show patients.
• Implemented automated reminder system targeting high-risk appointments, reducing no-shows by 31%.
• Quantified impact: $150K+ annual revenue recovery from reduced missed appointments.
• Created actionable dashboard for clinic managers to monitor real-time prediction scores.

Project: Multi-Channel Marketing Attribution

📊 Business Problem

Marketing team was unable to accurately attribute revenue to different marketing channels (email, social media, paid search, organic). Current last-click attribution model was undervaluing top-of-funnel activities.

🧪 Methodology

Implemented data-driven attribution modeling using multi-touch attribution techniques. Analyzed 6 months of customer journey data (500K+ journeys) across all touchpoints. Applied Shapley value approach for fair credit allocation between channels.

Tech Stack: Python Pandas Google Analytics 4 Power BI

Duration: 5 weeks

[Visualization: Attribution Model Comparison - Revenue Distribution Across Channels]

📈 Key Insights & Results

• Revealed that organic search was undervalued by 45% under last-click model; adjusted budget allocation accordingly.
• Implemented new attribution model reducing channel overlap misattribution by 60%.
• Enabled data-driven marketing budget optimization, resulting in 23% increase in ROI.
• Created automated monthly attribution reports, replacing manual Excel-based process.

Project: Supply Chain Demand Forecasting

📊 Business Problem

Manufacturing company faced significant inventory challenges: overstock in slow-moving items (15% excess inventory) and stockouts in high-demand products (8% lost sales). Traditional manual forecasting methods were inaccurate.

🧪 Methodology

Implemented time-series forecasting using ARIMA, Prophet, and LSTM neural networks. Analyzed 3 years of historical sales data (2,500+ SKUs). Incorporated external factors: seasonality, promotions, economic indicators. Validated models using MAPE and RMSE metrics.

Tech Stack: Python TensorFlow Statsmodels SQL

Duration: 6 weeks

[Visualization: Demand Forecast vs. Actual Sales - Time Series Analysis]

📈 Key Insights & Results

• Achieved 94% forecast accuracy (MAPE) using ensemble of Prophet and LSTM models.
• Reduced excess inventory by 22%, freeing up $2.3M in working capital.
• Decreased stockouts by 67%, improving customer satisfaction scores by 12%.
• Implemented automated forecast dashboard with weekly retraining pipeline.

Project: Customer Segmentation & Lifetime Value

📊 Business Problem

SaaS company was using a one-size-fits-all approach to customer management, resulting in inefficient marketing spend and low retention rates. Leadership needed data-driven customer segments for targeted strategies.

🧪 Methodology

Conducted behavioral clustering using K-means and hierarchical clustering algorithms on 50K+ customer records. Engineered 25+ features from usage patterns, engagement metrics, and subscription data. Validated segment quality using silhouette analysis and domain expertise review.

Tech Stack: Python Scikit-learn Tableau SQL

Duration: 3 weeks

[Visualization: Customer Segments - 2D Projection & Segment Characteristics Heatmap]

📈 Key Insights & Results

• Identified 5 distinct customer segments with up to 4x difference in lifetime value.
• Developed segment-specific retention strategies, improving overall retention by 18%.
• Implemented predictive churn model for high-value segments, enabling proactive interventions.
• Reduced customer acquisition cost via better targeting, improving marketing efficiency by 31%.