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.

Automated sales dashboard for AtliQ Hardware enabling data-driven decision making.

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.

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.

End-to-end retail sales analysis using Excel | Revenue optimization insights | Interactive dashboards with actionable business intelligence

Healthcare Operations Analyst | Transforming hospital data into $427K revenue recovery opportunities through Python analytics and interactive Streamlit dashboards. Specialized in predictive modeling, operational optimization, and executive reporting for healthcare systems.

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.

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

Reductions of 15% were achieved by using dynamic pricing, 12% more bookings during peak months were made thanks to focused marketing, and booking sources were streamlined. improved income and strategy as a result of insights derived from data.

Dynamic Pricing is an application of data science that involves adjusting the prices of a product or service based on various factors in real time. It is used by companies to optimize revenue by setting flexible prices that respond to market demand, demographics, customer behaviour and competitor prices.

"A comprehensive SQL-based analysis of a banking database for 'The Big Bank'. This project explores customer banking behavior, transaction trends, and account management. The analysis aims to optimize banking operations, enhance fraud detection, improve customer segmentation, and increase revenue using 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

Visualizing retail revenue, customer segmentation, and seasonal trends through SQL-driven analysis and Tableau.

Using Python Analyzing hospitality data to optimize operations, enhance customer experience, and boost revenue. Focus areas: occupancy trends, personalized services, pricing strategies, and risk management. Tools: Python (Pandas) and EDA. Enabling data-driven decisions for business success.

Comprehensive customer acquisition systems covering modern lead generation, conversion optimization, and customer onboarding methodologies. Build sustainable customer acquisition processes.

Анализ A/B-тестирования новой механики оплаты: оценка метрик, статистические тесты, рекомендации по внедрению и оптимизации.