Mickey Whittaker

Return Comment Analyzer

Overview
Developed a tool to automate analysis of 500+ weekly return comments and over 100,000 historical comments to extract actionable product and customer insights.

Purpose
To identify customer sentiment and thematic patterns in return comments, enabling integration with churn prediction models and product feedback loops.

Key Features

• Sentiment Scoring: Breaks down return comments into up to four themes per comment, each scored on a 1–5 scale (1 = very negative, 3 = neutral, 5 = very positive).
• Thematic Analysis: Extracts product-related issues and sentiment trends tied to individual customer IDs.
• Scalable Insights: Supports both near real-time feedback review and large-scale historical analysis for trend identification.
• Integration-Ready: Outputs sentiment and theme scores in structured format for use in churn prediction models and product performance dashboards.

Impact
Enabled systematic product feedback evaluation and customer experience analysis, replacing time-intensive manual review with consistent, scalable AI-driven insights.

Retail App with Social Media Elements

Overview
A mobile-first retail app that integrates social media features, allowing users to share, review, and discuss products in a community-driven environment.

Key Features

• Social Sharing: Users can post product reviews and share purchases with friends.
• Community Feedback: Comment and like features foster engagement and product discovery.
• Mobile Optimized: Designed for seamless use on smartphones and tablets.

Impact
Enhanced user engagement and product visibility through integrated social features, driving higher retention and sales.