Overview This repository contains the analysis and findings from a customer satisfaction survey. The analysis is aimed at understanding customer feedback across different aspects such as Delivery, Product, Service, and Support. The results are classified into positive, neutral, and negative sentiments to provide actionable insights.
Table of Contents
Project Description
This project analyzes customer satisfaction data to evaluate feedback on Delivery, Product, Service, and Support. By classifying sentiments into positive, neutral, and negative, it aims to identify strengths and areas for improvement, with actionable recommendations for enhancing customer experience.
Data Sources
The analysis was conducted on data generated in phyton using faker library.
Analysis Steps
- Data Cleaning: Initial data preprocessing to handle missing values and outliers.
- Exploratory Data Analysis (EDA: Visualizing and summarizing key data points.
- Sentiment Analysis: Classification of feedback into positive, neutral, and negative sentiments.
- Visualization: Graphical representation of feedback distribution and sentiment trends.
- Conclusion and Recommendations: Summary of key insights and suggested actions for improvement.
Key Findings
- Support excels, service/delivery needs improvement: Support consistently receives high satisfaction ratings, while service/delivery is an area that requires attention and improvement.
- Location influences satisfaction: Certain locations demonstrate higher average satisfaction scores, suggesting that location-specific factors impact customer experience.
- Support feedback is polarized: Sentiment analysis reveals positive feedback for support, indicating potential inconsistencies in other categories leading to bad customer support experiences.
- Gender may play a role: Female customers tend to report slightly higher satisfaction levels compared to male customers, suggesting potential gender-specific preferences or experiences.
- Age is less influential: Satisfaction scores are relatively stable across age groups, implying that age might not be a major determinant of customer satisfaction in this context.
Technologies used
- Python: For data analysis and visualization.
- Pandas: For data manipulation.
- Matplotlib/Seaborn: For creating visualizations.
- Jupyter Notebook: For documenting and running the analysis.
Contributing
Contributions are welcome! Please open an issue or submit a pull request if you have suggestions or improvements.