Customer Feedback Analysis System

Project Overview

The Customer Feedback Analysis System is designed to store and analyze customer feedback for a product or service. The system incorporates SQL and PL/SQL functionalities to enable efficient data management and insightful analysis. Key features include feedback aggregation, common complaint identification, and activity logging to ensure comprehensive tracking of feedback updates.

Features

1. Feedback Aggregation:

   • Analyze feedback by rating, product, and region.
   • Gain insights into customer satisfaction levels and trends.

2. Complaint Identification:

   • Use PL/SQL functions to identify the most common complaints across products or regions.

3. Feedback Update Logging:

   • Implement triggers to log feedback updates in an audit trail for accountability.

Database Schema

The database consists of the following tables:

1. Region
2. Customer
3. Product
4. Feedback
5. Audit_Log

• These table structure is provided in the project file named as 'tables.sql' with the data queries file named as 'data.sql'

Description: Maintains an audit trail of feedback updates with details about changes and responsible personnel.

Key Tasks

1. SQL for Feedback Analysis:

   • Write SQL queries to aggregate feedback by product, region, and rating.

2. PL/SQL for Complaint Identification:

   • Develop PL/SQL functions to identify and prioritize common complaints from customer feedback.

3. Triggers for Feedback Updates:

   • Implement triggers to log feedback modifications into the Audit_Log table.

Usage

1. Set up the database schema by running the provided SQL scripts to create the tables.
2. Populate the tables with provided sample data for testing.
3. Use SQL and PL/SQL scripts to perform analysis and identify complaints. (PL/SQL script is written in operations.sql file)
4. Ensure feedback updates are logged automatically by triggers.

Future Enhancements

• Develop a web-based interface for customers to provide feedback.
• Add visualization tools to represent feedback trends.
• Integrate machine learning models to predict customer satisfaction.

Authors

• Developed by [Sahane Krushna].
• College Name : Amrutvahini College Of Engineering, Sangamner.
• Department : (TE) Computer Engineering
• Email : krushnasahane57@gmail.com