AI Review Analysis & Competitor Benchmarking
Built AI pipeline to classify reviews and benchmark competitors achieving 99.17% classification accuracy
Role
Product Demo Lead & ML Reviewer
Timeframe
Spring 2025
Team Size
5 engineers and data scientists
Tools & Technologies
Key Impact
99.17% classification accuracy, 80% reduction in manual review workload
Context
Product teams were overwhelmed by unstructured customer reviews, with manual review and competitor benchmarking being time-intensive and error-prone.
Problem
Manual review and competitor benchmarking were time-intensive and error-prone, limiting strategic decision-making capabilities.
Approach
Fine-tuned BERT model for review classification and sentiment analysis
Built interactive Streamlit demo for real-time analysis
Reviewed ML code for bias detection and quality assurance
Implemented competitor benchmarking algorithms and visualizations
Created automated pipeline for continuous model improvement
Outcome
Achieved 99.17% classification accuracy on review categorization
Reduced manual review workload by 80% through automation
Delivered interactive demo for strategic product management decisions
Provided actionable benchmarking insights for competitive positioning
Next Steps
Scale pipeline to multiple industries and product categories
Automate dashboard integration for real-time insights
Expand competitor analysis to include social media and news sources