Data Analyst | GreyOrange
At GreyOrange, I played a key role in enhancing lead generation, sales analytics, and marketing reporting by leveraging tools like Salesforce, SalesLoft, HubSpot, Excel, and Looker Studio. I developed an interactive lead management dashboard for NRF 2025, which increased lead conversion by 15% through data visualization and actionable insights. I also conducted data validation and optimization in Salesforce, improving outreach campaign efficiency. Additionally, I automated marketing email and InMail analysis in HubSpot, boosting engagement rates with workflow-driven drip campaigns. Through these efforts, I provided stakeholders with data-driven strategies that improved sales performance and marketing outcomes.

Data Analyst Intern | Ultratech Cement Ltd.
During my internship at Ultratech Cement, I applied advanced analytics in Excel and Python to analyze the primary-secondary sales correlation, uncovering key business insights that streamlined sales operations. Through data-driven analysis, I helped reduce operational impediments by 30% and increase market penetration by 25%. I introduced data-driven solutions that lowered sales barriers by 35% and expanded market reach by 30%. Collaborating closely with cross-functional teams, I delivered actionable insights, optimizing decision-making and boosting overall sales efficiency.

This case study details my experience in revolutionizing inventory management for used car dealerships.  I spearheaded a data-driven project focused on improving data accuracy and optimizing inventory processes.  My role involved data analysis, database management, and visualization to help dealerships make more informed decisions.

Many used car dealerships struggled with data inconsistency in their inventory management systems.  This led to inefficient processes, inaccurate sales forecasting, and ultimately, lost revenue. The challenge was to improve data accuracy, streamline inventory tracking, and provide dealerships with actionable insights to optimize their stock and sales.

The primary objective was to improve data accuracy by at least 25% and enhance analytical efficiency by 30%.  This would translate to more accurate sales forecasting and better inventory management, allowing dealerships to maximize profits and minimize losses from unsold stock. My goal was to develop a user-friendly system that would provide clear, concise data visualizations for efficient decision-making.

I began by collecting data from various sources, carefully cleaning and preparing it for analysis. SQL played a crucial role in this stage; I used its powerful features to transform the raw data into a structured format suitable for analysis and reporting. Data validation was a crucial step; ensuring data quality before analysis was paramount for achieving reliable insights. I then designed an intuitive data model within Power BI, ensuring ease of navigation and access for the inventory managers.

The development involved several key steps: Data Cleaning and Transformation using SQL; Trend Analysis and Data Filtering for enhanced insights; and the creation of Interactive Visualizations within Power BI.  I worked closely with the dealership's inventory manager to ensure the dashboards accurately reflected their needs and provided actionable insights. The entire process involved rigorous testing and refinement to deliver a robust and user-friendly solution.

The biggest challenge was dealing with the inconsistent and incomplete nature of the initial data.  To address this, I employed advanced SQL techniques for data cleaning and transformation. I developed custom SQL queries to identify and handle missing values, outliers, and inconsistencies.  This process required careful attention to detail and a robust understanding of SQL's data manipulation capabilities.  The experience reinforced the importance of data validation and preprocessing as a foundation for reliable data analysis.

Throughout the development process, rigorous testing was conducted to ensure data accuracy and dashboard functionality.  This included unit testing of individual components within the Power BI dashboards, as well as comprehensive testing of the entire system.  The inventory manager provided valuable feedback throughout this process, ensuring the final product met their specific requirements and was intuitive to use.  This iterative feedback loop proved essential in ensuring the success of the project.

The project successfully achieved its goals. Data accuracy improved by 25%, and analytical efficiency increased by 30%. The dealership is now able to make more informed decisions regarding inventory management, leading to improved sales forecasting and reduced carrying costs. The inventory manager reported significant improvements in their workflow efficiency and decision-making. The interactive dashboards provided by Power BI are now integral to their daily operations, providing them with real-time visibility into their inventory and sales trends.

This project reinforced the importance of data quality and the power of data visualization in driving business decisions.  I gained valuable experience in data cleaning, transformation, and visualization, strengthening my SQL and Power BI skills.  The collaborative nature of the project highlighted the importance of clear communication and regular feedback loops in ensuring project success.  Working closely with the end-user ensured the final product was tailored to their specific needs and improved usability.

This project demonstrated the significant impact of data-driven solutions in optimizing inventory management within the used car industry. The success of the project highlights the value of investing in data analysis and visualization tools. In the future, I would like to explore incorporating predictive modeling into the system to further enhance sales forecasting capabilities and explore machine learning techniques to identify optimal pricing strategies.

Improving data accuracy and optimizing inventory management in the used car industry using data analysis and Power BI.

Revitalizing Used Car Sales: Data-Driven Inventory Management

I was thrilled to lead a data analysis project focused on optimizing sales and inventory management at Amazon.  This project leveraged my skills in data analysis, visualization, and strategic recommendations to directly impact business outcomes. My primary goal was to enhance Amazon's inventory planning process and boost overall revenue by leveraging data-driven insights.

Amazon faced the common challenge of managing inventory effectively, balancing the need to meet customer demand with the costs associated with overstocking and stockouts.  Overstocking leads to storage fees, potential markdowns, and ultimately reduced profit margins.  Insufficient stock results in lost sales and customer dissatisfaction. The challenge was to accurately predict sales trends, customer preferences, and seasonal demand to optimize inventory levels and marketing efforts.

My key objectives were to: 1. Analyze sales data to identify trends and seasonal patterns. 2. Develop data-driven recommendations for inventory optimization and marketing strategies. 3. Provide clear and insightful visualizations of sales performance and forecast data.  My success was measured by a 20% reduction in overstock, a 15% projected increase in revenue, and a 25% improvement in the clarity and actionability of insights provided to the stakeholders.

I started by carefully planning the data analysis process. First, I defined the key performance indicators (KPIs) that would directly measure the impact of the project, such as the percentage reduction in overstock and the projected revenue increase.  Then, I planned to collect and clean the data and perform exploratory data analysis (EDA) to understand the data's structure and identify potential issues or outliers. I decided on the most suitable visualization techniques for clearly communicating the insights, considering the technical expertise and needs of the stakeholders.  My chosen architecture involved a Python-based data analysis workflow, using well-established libraries for data manipulation, analysis, and visualization.

The project followed an iterative development process.  I began with data extraction and preprocessing using Python's NumPy and Pandas libraries. This involved handling a significant dataset of over 100,000 sales records. Through careful data cleaning and transformation, I ensured data quality and accuracy.  Next came EDA to uncover key trends in sales, such as seasonal patterns and correlations between sales and marketing activities.  This was followed by developing visualizations using Matplotlib to showcase my findings clearly and effectively.  Finally, I prepared reports and recommendations for the stakeholders.  Through iterative feedback from stakeholders, I refined the visualizations and recommendations to maximize their impact.

The biggest challenge I encountered was handling the large dataset of over 100,000 records.  To address this, I used efficient data manipulation techniques with NumPy and Pandas, ensuring that memory usage remained manageable.  I employed optimized algorithms for data analysis and visualization, which made the processing time considerably more efficient.

The project successfully achieved its objectives.  We observed a 20% reduction in overstock, which directly translated into cost savings. Moreover, the data-driven recommendations led to a projected 15% increase in revenue. The visualizations significantly improved the understanding and clarity of sales trends, enabling the inventory planners and marketing teams to make better, more informed decisions. This successful project serves as a testament to the power of data analysis in driving business optimization.

Throughout this project, I honed my skills in data analysis, visualization, and communication. I learned the importance of efficient data management techniques when working with large datasets.  Furthermore, I gained valuable experience in collaborating with stakeholders, incorporating feedback, and ensuring that my analyses are aligned with the business objectives.  This project has strengthened my ability to transform raw data into actionable insights, providing demonstrable value to a business.

This project demonstrated the significant impact that data analysis can have on optimizing inventory management and boosting revenue.  By providing actionable insights and visualizations, we were able to improve Amazon's operational efficiency and enhance its overall financial performance.  Future steps could involve incorporating more advanced forecasting techniques or implementing machine learning models for even more accurate demand predictions. The project was a successful implementation of data-driven decision-making, setting a strong foundation for future data analysis initiatives.

Improving inventory planning and boosting revenue through data analysis and visualization.  Reduced overstock by 20% and projected 15% revenue increase.

Hey I'm Sankalp, a Data Analyst