Phaneendra Reddy

Solved a variety of interesting problems in Retail domain and developed analytics solutions with the help of Python and AWS Sage maker.

Achievements/Tasks

• Product Recommendation - Developed a cutting-edge product recommendation system utilizing collaborative filtering and clustering methods. Leveraged AWS Sage Maker for model development and deployment, ensuring scalability and efficiency. Through the recommendation system, the client achieved an additional 2% of total revenue resulting in a significant increase in dealer revenue.
• Beat Optimization - Developed a route plan optimization solution for TGMs (Territory General Managers) using Pyomo within AWS Sage Maker. Designed to satisfy all constraints, the solution effectively optimized route and prioritizing visits, the solution maximized TGM productivity.
• Churn Analysis - Developed a comprehensive churn analysis model for dealers by analyzing their past buying behavior. Utilized historical data to predict and classify dealers likely to churn, enabling proactive intervention strategies, which significantly contributed to the organization's ability to recover churned dealers.
• DP Segmentation - Employed a variety of techniques and features to effectively segment distributors into distinct categories. Leveraged advanced data analysis and machine learning methods to develop a segmentation strategy tailored to the organization's specific needs, enabling targeted marketing and operational initiatives.
• Development of Rural Attractive Index - Led the development of Economic Potential Index (EPI) at the village level using annual data. Leveraged advanced statistical techniques and machine learning algorithms to analyze extensive datasets and derive meaningful insights.
• Sales Forecasting - Developed and fine-tuned a robust demand forecasting model using various ML algorithms and Time series algorithms.