Neha Agarwal

Data Scientist

TransOrg Analytics

Jun 2025 - Present

Customer Analytics & Churn Prediction for a Leading Coffee Chain: Performed RFM segmentation on 1.1M+ customers, categorizing them into 14 segments and applied K-Means Clustering to define customer personas, enabling personalized lifecycle offers. Evaluated lifecycle offer effectiveness through A/B testing on 4.2L+ customers, achieving a 3% conversion rate and generating 1Cr+ monthly incremental revenue. Conducted pre and post-campaign analysis across 10+ campaigns; the Mother’s Day campaign drove 40% revenue growth and a 38% increase in new registrations, while 13K+ offer redeemers generated 1.7Cr+ revenue. Developed a churn prediction model using Logistic Regression and Random Forest, achieving 85%+ recall and enabling retention of the top 7% highest churn-risk customers, resulting in an 18% reactivation rate. NLP-Driven Review Analytics for a Leading Hospitality Brand: Analyzed 1.8L+ guest survey responses across 219 hotels to identify dining preferences and benchmark brand recall against global competitors. Implemented a SpaCy NER pipeline with RapidFuzz fuzzy matching and 200+ noise filters, extracting 19K+ usable restaurant mentions and classifying them as client vs competitor brands. Found that 64% of respondents mentioned competitor brands; applied RoBERTa over VADER for sentiment classification, identifying 53% of brand-mentioning guests with low ratings as a key churn-risk segment. Delivered cuisine-level brand benchmarking and sentiment trend analysis, informing culinary positioning strategy across the hospitality portfolio.