Project Details

Analyzed AtliQ Hardware’s financial and sales performance from 2019 to 2021 to support strategic decision-making.

Key Learnings:

ETL Process to streamline data handling
Data Cleaning: Efficiently streamlined data using Power Query Editor
Data Modeling: Established relationships using Power Pivot
DAX Measures: Created measures for detailed financial and sales analysis
Pivot Tables: Generated comprehensive P&L statements and sales performance reports

Financial Insights
Net Sales increased from $87.48M in 2019 to $598.88M in 2021
Gross Margin grew from $36.24M to $218.16M
GM% declined from 41.43% to 36.43%

Sales Insights
Top Product: 'AQ Electron 4 3600 Desktop Processor' with $19.35M in sales
16 new products in 2021 generated $176.16M in sales
Highest sales markets: India, USA, and South Korea

The comprehensive report provided valuable insights enabling data-driven decisions for growth & efficiency.

Project Details

Customer Behavior
State-wise Distribution:
Top Buyers' State: The majority of the buyers are from Maharashtra, indicating a significant customer base in this state.
Product Trends
Product Popularity:
High Demand Product: T-shirts are in high demand, showing a strong preference among customers.
Preferred Size: M-Size T-shirts are the most popular choice among buyers.
Sales Patterns
Order Fulfillment:
Fulfillment Method: Most orders are fulfilled through Amazon, suggesting reliance on Amazon's logistics and fulfillment network.
Customer Type:
Retailers as Main Customers: The business primarily serves retailers, indicating a B2B sales model.

Project Details

This project, which combines Power BI, SQL, and Excel, centers around addressing operational issues at AtliQ Hardware, a prominent global consumer electronics company. The company's heavy reliance on Excel for data analysis has led to significant losses in some regions. To rectify this, senior management aims to initiate a data analytics project, leveraging data from the previous year to enhance performance and drive improvements.
💼Finance View: shows profit and loss statement like GM,NP,NS etc. and performance over time chart w.r.t to key metrices.
📣Sales View: Shows customers and product performance , unit economics and performance matrix by NS and GM%
📣 Marketing View: Shows region and product performance , unit economics and performance matrix by NS and GM%
🚚 Supply Chain View: shows forecast accuracy, Net Error, trends and key metrices by customers and product.
👨‍🎓 Executive View: shows market share of competitors, trends ,revenue by division and channel, top 5 customer

Live Interactive Dashboard

Project Details

Insights and Recommendations
Market Expansion: Focus on growing markets where "Atliq Exclusive" operates in the APAC region.
Product Development: Increase production for high-demand segments like notebooks, accessories, and peripherals.
Cost Management: Monitor manufacturing costs to optimize production expenses.
Customer Strategy: Tailor discount strategies to key customers to maximize sales.
Sales Optimization: Leverage high-performing sales channels and plan for seasonality in sales.

Project Details

🔍 Problem Statement:
Building a winning cricket team isn’t just about picking star players; it’s about assembling a well-rounded squad where each player excels in their specific role. The challenge was to identify the best 11 players globally, based on data-driven criteria, for roles ranging from Openers to Fast Bowlers.

💡 The Approach:
◾ Data Collection: Using Python, I gathered extensive player stats from various sources through web scraping and API integration.
◾ Criteria Definition: Set specific performance benchmarks for each role, like batting average, strike rate, and bowling economy.
◾ Data Analysis: Filtered and analyzed the data in Python to select players who met these criteria.
◾ Visualization: Used Power BI to create interactive dashboards, making the data insights easy to interpret.

💼 Skills & Tools Used:
Python: Data collection, cleaning, and filtering.
Power BI: Visualization and reporting.
Data Analysis: Evaluating and ranking player performance.

Live Interactive Dashboard

Project Details

🛠️ Key Focus Areas:
1️⃣ 𝐁𝐨𝐨𝐤𝐢𝐧𝐠 𝐓𝐫𝐞𝐧𝐝𝐬 & 𝐏𝐚𝐭𝐭𝐞𝐫𝐧𝐬: Analyzed seasonal and platform-specific booking behaviours.
2️⃣ 𝐑𝐞𝐯𝐞𝐧𝐮𝐞 𝐈𝐧𝐬𝐢𝐠𝐡𝐭𝐬: Identified cities and properties facing revenue decline.
3️⃣ 𝐎𝐜𝐜𝐮𝐩𝐚𝐧𝐜𝐲 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬: Evaluated occupancy rates to uncover optimization opportunities.
4️⃣ 𝐂𝐚𝐧𝐜𝐞𝐥𝐥𝐚𝐭𝐢𝐨𝐧𝐬: Examined the impact of cancellations on overall performance.

🐍 𝐓𝐞𝐜𝐡 𝐒𝐭𝐚𝐜𝐤 𝐔𝐬𝐞𝐝:
• Python Programming for data processing and analysis.
• NumPy & Pandas for efficient data cleaning and manipulation.
• Seaborn & Matplotlib for creating insightful visualizations and graphs.
I transformed raw booking data into meaningful insights using ETL pipelines, data cleaning, feature engineering, and insightful visualizations.