Analyze product data for an online sports retail company to optimize revenue.

Applying product segmentation, demand forecasting, and revenue optimization to increase online retailer revenue

Analysis of KMS' Order data from 2009-2012. Includes pivot tables, charts, and insights on sales, regions, customer profitability, and shipping costs. Provides recommendations to optimize operations and enhance revenue for Kultra Mega Stores.

An alternative to Nomis solutions on e-Cars case through Logistic Regression with Lasso regularization

Analyzed hotel booking cancellations, implemented dynamic pricing for a 15% reduction, initiated targeted marketing for 12% rise in peak month bookings, and optimized booking sources. Enhanced revenue and strategy through data-driven insights.

IT contains data analysis and visualizations aimed at improving a landscaping company's revenue and customer satisfaction. The project addresses two sub-goals: increasing revenue and improving customer satisfaction. Four visualizations are provided, each contributing insights toward achieving these objectives.

Analyzed hotel booking cancellations, implemented dynamic pricing for a 15% reduction, initiated targeted marketing for 12% rise in peak month bookings, and optimized booking sources. Enhanced revenue and strategy through data-driven insights.

Performed exploratory data analysis, and utilizing Recency, Frequency, and Monetary (RFM) analysis, followed by the application of K-Means clustering algorithm to define distinct customer segments. Executed targeted revenue-generating strategies tailored to each segment, resulting in increased sales and enhanced overall business performance