Using Python and Tableau to analyze 540,000 international e-commerce transactions, this project engineered an advanced behavioral segmentation pipeline. By overcoming heavily right-skewed data and extreme spending outliers, unsupervised K-Means clustering divided 4,300 unique buyers into distinct personas, uncovering a massive £130,000 short-term revenue recovery opportunity hidden within a dormant, at-risk customer cohort.
This optimization study evaluated performance data across 200,000 multi-channel marketing campaigns spanning two years to isolate the exact drivers of ROI variation. The analysis revealed that performance remains remarkably uniform across distribution channels and demographic segments; instead, outcomes are driven primarily by campaign-level execution, including creative quality, messaging, and post-click strategies.
Analyzing over 1.7 million analytical records, this user journey mapping study uncovers hidden behavioral bottlenecks along the conversion funnel. It reveals that 64% of product page viewers drop off before cart addition and demonstrates that while paid search drives 67% of traffic volume, highly focused social campaigns deliver peak conversion efficiency.
This initiative transformed unstructured retail logs into an operational dashboard to analyse sales performance. By overcoming data silos, the project evaluated $733,215 in sales and $93,439 in profit across 3,312 orders. The resulting framework identifies growth vectors, optimizes inventory, and mitigates customer concentration risks.
This advanced predictive modeling and revenue management initiative used supervised machine learning on a 5,000-flight dataset from Dubai to analyze drivers of passenger volume, price elasticity, and marketing efficiency. Findings revealed price-inelastic demand, indicating latent pricing power, while an advanced Random Forest model mapped non-linear interactions to vastly outperform standard linear metrics.
