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

"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."

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.

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.

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

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.

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.

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

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.

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