Nripendra Vikram Singh

Marketing Mixed Modelling

• Developed an optimization tool for campaign planning, leading to more efficient and effective marketing strategies.
• Delivered actionable insights on saturation curves, identifying diminishing returns.
• Assisted clients in campaign planning to meet their target KPIs.
• Techniques used: MLR, SLSQP
  

Supply Chain Optimization

• Optimized supply chain networks, focusing on cost reduction and profitability.
• Employed a two-pronged approach to identify profitability factors and minimize network costs.
• Quantifiable Impact: Reduced supply chain costs by 18% and increased overall profitability.
  Techniques used: Xgboost, SLSQP
  

Video Creative Analytics

• Used generative AI to extract and analyze data from video creatives.
• Identified features impacting engagement rates, aiding the media team in creating higher-engagement content.
• Quantifiable Impact: Enhanced video engagement rates by 30%.
  

Price Elasticity and Incentive Steering

• Devised strategies for optimizing discount allocations based on consumer utilization patterns.
• Employed multilevel modeling and the Pyomo framework for precise optimization.
  

New Car Demand Forecasting

• Aided clients in accurate demand forecasting using advanced methodologies (XGBoost, SARIMAX, UCM, fbprophet).
• Implemented model stacking to reduce prediction errors.
• Deployed models on AWS for scalable solutions.
• Quantifiable Impact: Increased forecasting accuracy by 25%, leading to better inventory management.

CI/CD Pipeline Development

• Built and optimized the CI/CD pipeline for data science model deployment.
  

• Collaborated with hundreds of students to boost homework completion and address weaknesses, achieving 60% improvement
• Instructed 30 students per week remotely via video chat, maintaining 90% attendance and on-time delivery
• Utilized technology for efficient information access, reducing learning time by 40%.

• Executed FMCG marketing analytics initiatives, crafting potent statistical models via market mix modeling. Leveraged SAS for precise multiple linear regression, yielding optimal ROI and valuable insights, encompassing elasticity and competitor-driven marketing recommendations.
• Employed Bayesian model averaging in R to discern the impact of diverse marketing promotions on brand volume
• Used ARIMA models for category forecasting in R
• Contributed to business development by employing a Kalman filter approach in R to analyze advertisement effectiveness, copy and repetitive wear-out, as well as the half-life of advertisements