Kantshri Baronia

Within CG&S domain, key projects worked upon:

1. Sustenance team

Managing a team of 3 to refresh dashboards leveraging ML & Analytics, and to improve code logics to meet key business needs, across multiple geographies

2. eRGM (ecommerce Revenue Growth Management)

• Developed ML models to create widgets that could evaluate brand's promotional performance across various eCommerce platforms
• Implemented RFM Analysis and Customer Segmentation techniques to categorize customers and tailor marketing strategies

3. Intelligent Sales Coach

• Worked towards developing Generative AI powered mobile app to enable on demand coaching experience for Sales reps through Cross-Functional Collaboration
• Coordinated with diverse teams including AI developers, sales experts, and UX designers to align project objectives

4. New Product Ideation

• Built Gen AI assisted, innovative idea generation tool, leveraging consumer & marketplace intelligence leveraging effective prompt engineering & contextualization

5. Benchmarking Tool

• Optimized e-commerce widget to assess brand’s performance against its competitors on industry agnostic indices, using 3P datasets through AI powered benchmarking engine

Skills used: ML, RFM Analysis, Trade Promotion Optimization, Market Basket Analysis, Customer Segmentation, Gen AI, Hypothesis Testing, Tshirt Sizing, Traditional AI, Python, SQL, NLP, Azure Databricks, Snowflake

Key projects worked upon:

1. CLTV

• Developed frameworks to predict Customer Lifetime Value (CLTV)- both Predictive and Historic, to identify potential High Value customers and drivers of CLTV which helped Customer Analytics team devise cross sale strategies

2. Media Mix Modelling

• Engaged in designing and executing media mix optimization engagement and pricing studies based on historic sales data and develop analytical frameworks

3. Price & Promotion Analysis

• Implemented solutions for FMCG and CPG clients for price optimization and discount budgeting

Skills used: GLM & BG-NBD algorithms, Bayesian/OLS Regression, Multivariate analysis, Insights Generation, Azure DataBricks, Python, SQL

• Implemented end-to-end problem solutions involving data procurement, generation & testing hypotheses, Insights generation, predictive modeling and data visualization
• Successfully built models on Churn Prediction, High Value Player Identification, Bonus Efficient Campaigns and Content Recommendation

Skills used: Hypothesis Testing, Gradient Boosting, Random Forest, Decision Trees, Multivariate Analysis