Customer Satisfaction Intelligence – Predictive Insights for Service Excellence

Domain & Duration

Customer Experience / Support Analytics | 7 Days

Tools & Techniques

• Python, Pandas, Scikit‑learn, XGBoost, NLTK, Power BI
• Sentiment Analysis, Predictive Modeling, Clustering, Feature Engineering, Logistic Regression

Project Overview

This project delivers a full‑stack analytical framework to decode customer satisfaction across support channels. By integrating survey data, ticket logs, and behavioral metrics, it transforms raw service interactions into strategic insights that drive retention, resolution quality, and operational efficiency.

Key Techniques & Insights

• Sentiment Analysis — Classified feedback using NLP to detect emotional tone and service sentiment.
• Predictive Modeling — Forecasted dissatisfaction, escalation risk, and reopen probability (Accuracy ≈ 84%).
• Feature Engineering — Built Time to Resolution, Empathy Score, First Contact Success.
• Clustering — Segmented customers using K‑Means + PCA for targeted engagement.
• Dashboarding — Power BI visuals for CSAT, NPS, SLA compliance, agent performance.
• Anomaly Detection — Isolation Forest flagged outlier tickets and reputational risks.

Impact & Strategic Value

• Enabled closed‑loop interventions for dissatisfied customers.
• Delivered agent‑level coaching insights based on resolution patterns and sentiment scores.
• Supported resource optimization by forecasting ticket volumes and priority shifts.
• Built a scalable model for satisfaction prediction and customer segmentation.

Key Insight Summary

Customer satisfaction is most influenced by resolution time and ticket status, with neutral sentiment dominating across a multi‑generational user base.

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