Reena Sree Nidadavolu

Results-oriented Business Analyst and Data Analyst with 3+ years of experience in data transformation and strategic insight generation. Skilled in business intelligence and proficient in Excel, SQL, Tableau, and Python. Completed a Master’s in Business Analytics, with practical experience in decision trees and forecasting. Dedicated to enhancing decision-making through data analysis and stakeholder engagement.

• Six Sigma Green Belt – LinkedIn Learning
• Banking and Banking Operations – Tata Institute
• Google Ads Measurement Certification – Google
• Corporate Finance – Great Learning Academy
• Fundamentals of Digital Marketing – Google
• Basics of Power BI – Great Learning
• Basics of Python – Infosys Springboard

Sales Performance Dashboard (Power BI)

• Tools Used: Power BI
  
• Description: Designed and developed a dynamic Power BI dashboard to visualize regional sales performance, KPIs, and trends across various product categories and sales teams. Integrated data from multiple sources (CSV, Excel, SQL) and utilized data modeling and DAX formulas to build custom metrics for YoY growth, profitability, and region-wise breakdown. Enabled business leaders to quickly identify underperforming regions and top-performing sales representatives, leading to a 15% improvement in quarterly sales planning efficiency.
  

Customer Churn Analysis (Python & Excel)

• Tools Used: Python (pandas, NumPy), Excel
  
• Description: Conducted a comprehensive churn analysis on telecom customer data to identify high-risk customers using logistic regression in Python and statistical analysis in Excel. Cleaned and prepared the dataset, performed exploratory data analysis to discover patterns and correlations related to churn behavior, and delivered recommendations including loyalty programs, contract renewal offers, and improved customer service strategies, potentially reducing churn by 12%.
  

Supply Chain Optimization (Excel & Tableau)

• Tools Used: Excel, Tableau
  
• Description: Analyzed inventory data, supplier lead times, and delivery schedules to uncover inefficiencies in the supply chain for a mid-sized retail company case study. Used Excel to simulate reorder levels, Economic Order Quantity (EOQ), and safety stock calculations, and visualized findings using Tableau dashboards. Proposed data-backed changes to vendor selection and order cycle strategies, which would reduce stockouts by 18% and holding costs by 10%.
  

Decision Trees and Random Forests: A Comparative Analysis (Python)

• Tools Used: Python (scikit-learn)
  
• Description: Built classification models using decision trees and random forests in Python on a retail customer segmentation dataset. Evaluated model accuracy, precision, and recall using cross-validation and confusion matrices to determine the most reliable model for business use cases. Recommended implementation of the random forest model for its higher accuracy and resistance to overfitting, enhancing decision-making for targeted marketing.
  

Statistical Data Analysis for Decision Making (Excel & PowerPoint)

• Tools Used: Excel, PowerPoint
  
• Description: Utilized hypothesis testing, regression analysis, and ANOVA to analyze customer feedback and sales data. Provided management with data-driven conclusions that influenced product bundling, pricing, and marketing strategies. Delivered final report presentations supported by charts and visualizations in Excel and PowerPoint.
  

Digital Marketing Analytics Project (Google Ads, Google Analytics, CRM Platforms)

• Tools Used: Google Ads, Google Analytics, CRM Platforms
  
• Description: Conducted a comprehensive digital marketing analysis for a small business using tools such as Google Ads, Google Analytics, and CRM platforms. Implemented keyword research, SEO audits, and campaign performance tracking. Created weekly reports analyzing user behavior, bounce rates, and conversion funnels, which helped optimize ad spend and improve ROI by 20%.