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📊 𝗪𝗵𝗮𝘁 𝗜 𝗔𝗻𝗮𝗹𝘆𝘇𝗲𝗱

● Compared EV sales and adoption trends across states and vehicle categories.
● Analyzed manufacturer performance and market share to identify leading players.
● Studied yearly and quarterly trends to understand changes in EV demand.
● Examined EV penetration to identify markets with stronger adoption and future potential.

💡 𝗞𝗲𝘆 𝗙𝗶𝗻𝗱𝗶𝗻𝗴𝘀

The analysis highlighted Ola Electric as a leading 2-wheeler EV manufacturer, while Goa and Kerala stood out for EV adoption. Tata Motors also showed strong growth among leading 4-wheeler manufacturers.

🎯 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗩𝗮𝗹𝘂𝗲

The Power BI dashboard brings these findings together to help identify high-potential markets, understand competitive positioning, and support AtliQ Motors’ India expansion strategy.

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📊 𝗪𝗵𝗮𝘁 𝗜 𝗔𝗻𝗮𝗹𝘆𝘇𝗲𝗱

● Examined policy sales and revenue trends across different insurance products.
● Analyzed customer and policy data to understand customer behavior and preferences.
● Compared performance across sales channels, cities, and policy categories.
● Tracked key business metrics to identify growth areas and performance gaps.

💡 𝗞𝗲𝘆 𝗙𝗶𝗻𝗱𝗶𝗻𝗴𝘀

The analysis helped identify high-performing policies, customer segments, sales channels, and locations, while highlighting areas where performance could be improved.

🎯 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗩𝗮𝗹𝘂𝗲

The Power BI dashboard brings these insights together in an interactive format, making it easier to monitor business performance, understand customer trends, and identify opportunities for improving insurance sales and growth.

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📊 𝗪𝗵𝗮𝘁 𝗜 𝗔𝗻𝗮𝗹𝘆𝘇𝗲𝗱

● Analyzed revenue, bookings, occupancy, and average daily rates over time.
● Compared performance across hotel types and booking channels.
● Examined cancellations, customer segments, and booking patterns to understand their impact on revenue.
● Identified periods and areas with strong and weak hotel performance.

💡 𝗞𝗲𝘆 𝗙𝗶𝗻𝗱𝗶𝗻𝗴𝘀

The analysis highlighted revenue trends, occupancy patterns, high-performing booking channels, and the impact of cancellations, helping identify areas that influence overall hotel performance.

🎯 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗩𝗮𝗹𝘂𝗲

The dashboard brings key revenue and booking metrics together, making it easier to track hotel performance, understand booking behavior, identify revenue opportunities, and support better pricing and business decisions.

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🌐𝐀𝐛𝐨𝐮𝐭 𝐭𝐡𝐞 𝐂𝐨𝐦𝐩𝐚𝐧𝐲: AtliQ Hardware excels in the computer hardware sector, serving both domestic and international markets.

🔍𝐓𝐡𝐞 𝐂𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞: Responding to the need for data-driven decisions, AtliQ Hardware's management initiated a SQL challenge to strengthen their analytics team. My task was to interpret 10 ad-hoc SQL requests and transform them into actionable insights.

🎯𝐊𝐞𝐲 𝐈𝐧𝐬𝐢𝐠𝐡𝐭𝐬 𝐚𝐧𝐝 𝐓𝐚𝐤𝐞𝐚𝐰𝐚𝐲𝐬:

📌𝐌𝐚𝐬𝐭𝐞𝐫𝐞𝐝 𝐃𝐚𝐭𝐚 𝐈𝐧𝐭𝐞𝐫𝐩𝐫𝐞𝐭𝐚𝐭𝐢𝐨𝐧: Emphasized thorough understanding of data before delving into SQL queries.

📌𝐄𝐧𝐡𝐚𝐧𝐜𝐞𝐝 𝐒𝐐𝐋 𝐒𝐤𝐢𝐥𝐥𝐬: Explored advanced concepts like Common Table Expressions (CTE), JOIN operations, Stored Procedures, etc.

📌𝐋𝐢𝐧𝐤𝐞𝐝 𝐒𝐐𝐋 𝐭𝐨 𝐑𝐞𝐚𝐥-𝐖𝐨𝐫𝐥𝐝 𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬: Demonstrated practical applications of SQL in business contexts, beyond mere skill.

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🚨 𝗞𝗲𝘆 𝗙𝗶𝗻𝗱𝗶𝗻𝗴𝘀:

📉 𝗟𝗼𝘄 𝗙𝘂𝗹𝗳𝗶𝗹𝗹𝗺𝗲𝗻𝘁 𝗥𝗮𝘁𝗲𝘀:

● OTIF (On-Time In-Full) performance is WAY below target: 29.02% vs. a target of 66%

● OT% stands at 59% — far from the target of 86%

● IF% is at 52.78%, underperforming the target of 77%

🌆 𝗖𝗶𝘁𝘆-𝘄𝗶𝘀𝗲 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲:

● Surat performed slightly better in OT%, but all three cities (Ahmedabad, Surat, Vadodara) missed the OTIF target.

💡 𝗖𝘂𝘀𝘁𝗼𝗺𝗲𝗿 𝗜𝗻𝘀𝗶𝗴𝗵𝘁𝘀:

● Lotus Mart is lagging with a poor OTIF of 16.3%, compared to Propel Mart at 40.9%.

● Despite delays, VOFR remains high (~97%), showing that while quantity is fulfilled, it's not on time.

🛍️ 𝗣𝗿𝗼𝗱𝘂𝗰𝘁 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲:

● Major products like AM Biscuits and AM Butter exhibit a consistent LIFR of ~66% and VOFR of ~96%, pointing to inventory or delivery planning issues.

⏰ 𝗗𝗲𝗹𝗶𝘃𝗲𝗿𝘆 𝗗𝗲𝗹𝗮𝘆𝘀:

● 13,000+ orders were delivered late!

● The average delay per order: 1.69 days, highlighting scheduling ineffi

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📸 𝗛𝗲𝗿𝗲’𝘀 𝗮 𝘀𝗻𝗮𝗽𝘀𝗵𝗼𝘁 𝗼𝗳 𝘄𝗵𝗮𝘁 𝗜 𝘂𝗻𝗰𝗼𝘃𝗲𝗿𝗲𝗱:

🔍 𝗞𝗲𝘆 𝗜𝗻𝘀𝗶𝗴𝗵𝘁𝘀 𝗳𝗿𝗼𝗺 𝘁𝗵𝗲 𝗗𝗮𝘁𝗮:
✅ 𝟭𝟭.𝟭 𝗕𝗶𝗹𝗹𝗶𝗼𝗻 𝗩𝗶𝗲𝘄𝘀 𝗔𝗻𝗮𝗹𝘆𝘇𝗲𝗱:

● 𝟴𝟴.𝟱% 𝗼𝗳 𝘃𝗶𝗲𝘄𝘀 came from videos with specific tag counts.
● 𝗦𝗵𝗼𝗿𝘁 𝘃𝗶𝗱𝗲𝗼𝘀 dominated with 𝟵.𝟴 𝗯𝗶𝗹𝗹𝗶𝗼𝗻 𝘃𝗶𝗲𝘄𝘀, followed by medium and long videos.

🎥 𝗧𝗼𝗽-𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗶𝗻𝗴 𝗖𝗼𝗻𝘁𝗲𝗻𝘁:

● 🥇 "𝗠𝗶𝗴𝗵𝘁𝘆 𝗟𝗶𝘁𝘁𝗹𝗲 𝗕𝗵𝗲𝗲𝗺" topped the charts with 𝟴𝟴𝟴 𝗺𝗶𝗹𝗹𝗶𝗼𝗻 𝘃𝗶𝗲𝘄𝘀.
● Other hits included "𝗕𝗲𝗹𝗹𝗮 𝗖𝗶𝗮𝗼" 🎶 and "𝗕𝗵𝗲𝗲𝗺 𝗖𝗼𝗼𝗸𝘀 𝗧𝗵𝗲 𝗕𝗲𝘀𝘁 𝗣𝗼𝗻𝗴𝗮𝗹"🍲.
● The 𝘁𝗼𝗽 𝟱 𝗺𝗼𝘀𝘁-𝗹𝗶𝗸𝗲𝗱 𝘃𝗶𝗱𝗲𝗼𝘀 collectively garnered 𝟭𝟬.𝟭𝟴 𝗺𝗶𝗹𝗹𝗶𝗼𝗻 𝗹𝗶𝗸𝗲𝘀!

📅 𝗧𝗿𝗲𝗻𝗱𝘀 𝗢𝘃𝗲𝗿 𝗧𝗶𝗺𝗲:

● 𝗪𝗲𝗱𝗻𝗲𝘀𝗱𝗮𝘆𝘀 were the most engaging day, with 𝟮.𝟯𝟰 𝗯𝗶𝗹𝗹𝗶𝗼𝗻 𝘃𝗶𝗲𝘄𝘀.
● 𝗠𝗮𝗿𝗰𝗵 saw the highest video uploads (𝟱𝟬𝟭 𝘃𝗶𝗱𝗲𝗼𝘀), while 𝗔𝗽𝗿𝗶𝗹 had the lowest (𝟯𝟰𝟴 𝘃𝗶𝗱𝗲𝗼𝘀).

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🎯𝗧𝗮𝗿𝗴𝗲𝘁 𝗔𝗰𝗾𝘂𝗶𝘀𝗶𝘁𝗶𝗼𝗻𝘀: Our objectives were crystal clear:
- Unleash the power of data insights to refine the credit card development process.
- Steer strategies that align seamlessly with customer preferences and dynamic market trends.

📊𝗜𝗻𝘀𝗶𝗴𝗵𝘁𝘀: Our dive into demographics, spending behaviour, and customer segments was an eye-opener. The insights gathered from 4000 customers spanning cities, professions, and age brackets were pivotal in shaping our strategies.

𝐊𝐞𝐲 𝐅𝐢𝐧𝐝𝐢𝐧𝐠𝐬💡
1. Credit cards are the top choice for customer payments.💳
2. Major spending categories include bills, groceries, electronics, and health-wellness products.🛒📱💊
3. Male and married customers dominate the spending trends.👨‍👩‍👦‍👦💰
4. The 25-35 and 35-45 age groups exhibit the highest spending patterns.👨‍💼👩‍💼💵
5. Mumbai emerges as the top spending city among all.🏙️💸
6. Salaried IT Employees and Other Salaried Employees drive the majority of transactions.💼💻💰

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🌟𝐏𝐫𝐨𝐣𝐞𝐜𝐭 𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬🌟

🏪𝐒𝐭𝐨𝐫𝐞 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬: Conducting a comprehensive analysis of store performance to identify key metrics and areas for improvement.

📢𝐏𝐫𝐨𝐦𝐨𝐭𝐢𝐨𝐧 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬: Analyzing the effectiveness of promotions to optimize future marketing strategies.

🔠𝐂𝐚𝐭𝐞𝐠𝐨𝐫𝐲 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬: Analyzing category trends and performance to enhance inventory management and assortment strategies.

📦𝐏𝐫𝐨𝐝𝐮𝐜𝐭 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬: Conducting a detailed analysis of product performance for informed decision-making and strategic planning.

💡𝐊𝐞𝐲 𝐅𝐢𝐧𝐝𝐢𝐧𝐠𝐬💡

📌Both campaigns experienced remarkable boosts in 𝐬𝐚𝐥𝐞𝐬 𝐚𝐧𝐝 𝐫𝐞𝐯𝐞𝐧𝐮𝐞, with 𝐒𝐚𝐧𝐤𝐫𝐚𝐧𝐭𝐢 showing particularly impressive percentage gains.

📌Standout performers included 𝐌𝐲𝐮𝐬𝐮𝐫𝐮 and 𝐁𝐞𝐧𝐠𝐚𝐥𝐮𝐫𝐮, while Vijayawada and Coimbatore stores highlighted areas for enhancement.

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🌟𝐏𝐫𝐨𝐣𝐞𝐜𝐭 𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬🌟

🏠𝐇𝐨𝐦𝐞 𝐏𝐚𝐠𝐞: The central hub is designed for effortless navigation, offering swift entry to crucial information.

💰𝐅𝐢𝐧𝐚𝐧𝐜𝐞 𝐕𝐢𝐞𝐰: Vital financial key performance indicators (KPIs), interactive charts, and adaptable parameters for a comprehensive financial analysis.

📈𝐒𝐚𝐥𝐞𝐬 𝐕𝐢𝐞𝐰: Gain insights into sales dynamics through scatter charts, product/customer performance data, and donut charts.

📢𝐌𝐚𝐫𝐤𝐞𝐭𝐢𝐧𝐠 𝐕𝐢𝐞𝐰: Explore in-depth marketing insights, including segment performance, waterfall charts, and versatile tables.

🚚𝐒𝐮𝐩𝐩𝐥𝐲 𝐂𝐡𝐚𝐢𝐧 𝐕𝐢𝐞𝐰: Streamline the supply chain by utilizing key metrics, trend charts, and detailed breakdowns.

👨🏻‍💼𝐄𝐱𝐞𝐜𝐮𝐭𝐢𝐯𝐞 𝐕𝐢𝐞𝐰: An essential section tailored for executives, delivering crucial metrics, revenue contributions, trend charts, and detailed tables.

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In this impactful project, I've leveraged Excel pivot tables to create dynamic sales reports that empower businesses to optimize customer discounts, streamline negotiations, and identify lucrative market opportunities. These reports aren't just data summaries; they're strategic assets delivering actionable insights to elevate sales strategies, bolster customer satisfaction, and drive revenue growth.

My journey through this project honed my expertise across pivotal areas:
1. Mastering pivot tables for comprehensive data analysis.
2. Harnessing the power of Power Query for seamless data integration.
3. Commanding the fundamentals of DAX for advanced data modeling.
4. Elevating data presentation through strategic conditional formatting.
5. Extracting invaluable insights from sales metrics to fuel strategic decisions.

This project is more than just number-crunching—it's about arming businesses with the tools and knowledge to supercharge their sales initiatives.

Project Details

This project involved a comprehensive analysis of Amazon’s sales data from three key locations: Mandalay, Yangon, and Naypyitaw. Using SQL, I explored 1,000 transaction records to identify patterns in customer behavior, revenue generation, and product performance.

Key objectives included:

● Determining monthly revenue trends

● Identifying top-performing product lines and customer types

● Classifying sales performance as "Good" or "Bad"

● Analyzing gender distribution, VAT impacts, and branch-wise performance

The analysis provided actionable insights to help Amazon refine its sales strategy, improve inventory decisions, and deliver more personalized customer experiences.