Project Details

● I took on this project from the hotel industry, diving into AtliQ Grands' challenges. By digging into booking and capacity data, I built a user-friendly Power BI dashboard, turning complex data into clear visuals. This project showcases my strategic thinking and data skills, revealing how analytics can reshape a business's path for the better.
● In one of the insights, I found that revenue lost due to cancellations is higher in Mumbai region and to cater to that they can implement strategies such as flexible cancellation policies or targeted marketing to mitigate cancellations.

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Project Details

● In this pivotal Data Analysis endeavor, I navigated the intricate landscape of AtliQ Hardware, a global consumer electronics leader. A comprehensive exploration of Sales, Finance, and Supply Chain data fueled the creation of a robust Power BI dashboard for heightened data visualization.
● Assigned to the project team, I embarked on crafting five distinct Power BI dashboards catering to Finance, Sales, Marketing, Supply Chain, and Executive perspectives. The journey began with a project kick-off session, aligning stakeholder requirements and setting the stage for a comprehensive analytics initiative.
● In one of the insights, I found that for some of their customers, the Gross Margin % variance was 10% or higher, which means they fell short of achieving the targets. Also, I understood that AtliQ gained a significant market share of 5.9% during the same year based on robust net sales growth.

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Project Details

● Armed with a sample dataset featuring 4000 customers across five cities, my role as a data analyst unfolded. The primary objective is to analyze this dataset to unveil key insights that would guide the product strategy team in tailoring the credit cards to customer needs and market trends.
● The heart of the project involved designing a Power BI dashboard—an intuitive visual tool aimed at top-level management and the product strategy team. The success of this venture pivoted on the dashboard's self-explanatory nature and its ability to convey complex insights with simplicity.
● In one of the insights, I found that the overall average monthly credit card spending is highest in Bills, followed by Electronics, and Health and wellness categories.
● Also, the overall average monthly credit card spending is highest in the 25-34 age group with 15.3 Million ₹, followed by the 35-45 age group with 11.9 Million ₹.

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Project Details

I used data tools in Excel to create insightful reports. For sales, I made a customer performance report and compared market performance to sales targets. In finance, I generated Profit and Loss reports for different periods and markets. I learned ETL, dynamic pivot tables, and DAX for advanced calculations. The reports helped in quick decision-making, adapting to changing data, and designing user-friendly reports for everyone involved.

Project Details

AtliQ Mart, a growing FMCG manufacturer in India, faces service issues affecting key customer contracts. To resolve this, our data-driven project focuses on optimizing 'On-Time' and 'In Full' delivery metrics. Insights reveal city-specific performance variations, high-order volumes from key customers, and product-category dynamics. Key decisions include customer-centric strategies, process optimization, and realistic target setting. The goal is to enhance overall service levels, customer satisfaction, and readiness for expansion into new cities within the next two years.

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Project Details

• Objective: Analyze pre/post-5G launch metrics for AtliQo's telecom services.
• Key Metrics: Total Revenue, Average Revenue Per User (ARPU), Total Active Users (TAU), Total Unsubscribed Users (TUU), Market Share, and plan effectiveness.
• Findings:
- 0.5% decrease in overall revenue after 5G.
- 11% increase in ARPU, driven by plans p11 and p12.
- 8.3% decline in TAU, with Pune showing the highest percentage change.
- Fluctuations in market share, requiring targeted strategies.
• Recommendations:
- Optimize revenue plans, focusing on successful post-5G plans.
- Implement city-specific strategies to improve market share.
- Enhance user retention initiatives to address TAU decline.
- Evaluate and possibly phase out less effective plans.
• Emphasis: Adaptability, continuous monitoring, and data-driven decisions for sustained competitiveness.

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