Sentiment-Based Sales Optimization

Executive Summary

Sentiment-Based Sales Optimization is a focused retail analytics initiative designed to convert customer feedback into measurable commercial impact. Over seven days, I built a sentiment-driven intelligence system that analyzes reviews across multiple product divisions, identifies behavioural patterns among age groups, and highlights category-level performance signals.

Techniques & Tools

• Sentiment Analysis, TF-IDF, Logistic Regression, Clustering, Word Clouds
• Python, scikit-learn, NLTK, Power BI

Key Outcomes

• Achieved 93.03% accuracy in classifying customer recommendation behaviour.
• Mapped sentiment polarity across age groups and product categories.
• Delivered dynamic dashboards highlighting sentiment drivers and category performance.

Strategic Insights

• Customers aged 30–39 demonstrated the strongest satisfaction and loyalty signals.
• Intimates and Dresses consistently received the highest positive sentiment.
• Younger age groups showed more variability, indicating opportunities for targeted engagement.
• Sentiment polarity aligned closely with product class, enabling precise marketing segmentation.

Business Impact

This project provides a scalable framework for retail sentiment intelligence. By connecting customer emotion with product performance, the system supports more effective category-level marketing, better assortment planning, improved customer experience strategies, and data-driven promotion targeting.