Portfolio Projects
Customer Churn & Prediction Analysis
This project focuses on reducing customer churn in the telecom industry by combining data engineering, business intelligence, and machine learning to deliver a full-scale, data-driven solution. It integrates a robust ETL pipeline in SQL Server, insightful Power BI dashboards, and a predictive Random Forest model to identify at-risk customers and uncover churn patterns. By transforming complex data into clear, actionable insights, the project empowers stakeholders to make smarter decisions and implement targeted retention strategies. Its innovative approach not only enhances customer loyalty but also holds the potential to significantly boost revenue and operational efficiency.
Supplier Quality & Performance Analysis
Innova Manufacturing Ltd, a manufacturing company, faced significant challenges in managing supplier quality and performance due to a lack of a centralized procurement system. Without a structured approach to assessing supplier reliability, the company struggled with inconsistent vendor performance, defective materials, and production downtime. Recognizing the need for a data-driven solution, the production management team consolidated supplier and defect-related data across multiple plants. The objective of this project was to analyze supplier quality, identify the key contributors to material defects and downtime, and provide actionable insights to optimize supply chain decisions and improve overall manufacturing efficiency.
Product Sales and Performance Analysis
The Product Sales and Performance Analysis project aimed to provide actionable insights into sales trends, revenue performance, and sales effectiveness within the product marketing and sales industry. By leveraging Power BI dashboards, the analysis evaluated total revenue, budget alignment, product group performance, sales channels, and salesperson rankings, offering data-driven recommendations to optimize business strategies. This project underscores the power of data-driven insights in shaping marketing and sales strategies, helping businesses maximize revenue potential and enhance overall performance.
Hospitality Domain Revenue Insights
In today’s competitive hospitality industry, data-driven decision-making is essential for maintaining market leadership and maximizing revenue. This project focuses on a Revenue Insights Dashboard developed in Power BI for Atliq Grands, a luxury hotel chain facing market share and revenue decline. By analyzing historical data, the dashboard provides key performance indicators (KPIs) such as occupancy rates, average daily rates (ADR), revenue per available room (RevPAR), and competitor benchmarking. Showcasing how business intelligence and data analytics can play a pivotal role in strategic decision-making, enabling hospitality brands to regain market share and sustain revenue growth.
E-commerce Platform Performance Analysis
Current fast paced digital disruptions has brought signficant transformations to the retail and commerce landscape, due to the rise of e-commerce platforms. With the convenience of these online platforms, consumers now have unparalleled access to a vast array of products and services. As e-commerce continues to reshape the retail landscape, understanding consumer behavior and market trends becomes essential for businesses to stay competitive and profitable. The objective of this analysis is to provide a comprehensive overview of the client's e-commerce platform performance by leveraging insights from Key Performance Indicators (KPIs), that will empower the client to make data-driven decisions to enhance customer acquisition, retention, and overall sales performance.
From Data to Strategy: Customer Segmentation in Online Retail
Customer segmentation is a crucial strategy in the e-commerce and retail industry, enabling businesses to identify and group customers based on purchasing behavior, engagement and other key metrics. This project utilized Python data libraries—Pandas, NumPy, and Plotly—along with the K-Means Clustering algorithm to analyze over 500,000 customer transaction data. By segmenting customers based on RFM (Recency, Frequency, Monetary) metrics into identify distinct groups, the aim is to enable the client, owners of an online retail store implement effective data-driven customer centric marketing strategies to optimize customer engagement and retention, and improve revenue generation.