🚀 SQL Project Series #29

Customer Support & Helpdesk Analytics 🎧
Analyze customers, support tickets, agents, resolutions, and customer satisfaction using SQL to improve support operations, reduce resolution time, and enhance customer experience.

🎯 Business Objectives
✅ Analyze support ticket volume
✅ Monitor agent performance
✅ Measure response and resolution time
✅ Identify recurring customer issues
✅ Analyze customer satisfaction
✅ Track SLA compliance
✅ Improve first-contact resolution
✅ Build executive support dashboards

📂 Step 1: Create Database
CREATE DATABASE helpdesk_db;
USE helpdesk_db;

📂 Step 2: Create Customers Table
CREATE TABLE customers (
customer_id INT PRIMARY KEY,
customer_name VARCHAR(100),
city VARCHAR(50),
signup_date DATE
);

📂 Step 3: Create Support Agents Table
CREATE TABLE support_agents (
agent_id INT PRIMARY KEY,
agent_name VARCHAR(100),
team_name VARCHAR(50),
joining_date DATE
);

📂 Step 4: Create Tickets Table
CREATE TABLE tickets (
ticket_id INT PRIMARY KEY,
customer_id INT,
agent_id INT,
issue_category VARCHAR(50),
priority VARCHAR(20),
created_at DATETIME,
resolved_at DATETIME,
ticket_status VARCHAR(20),
satisfaction_rating DECIMAL(2,1),
FOREIGN KEY (customer_id) REFERENCES customers(customer_id),
FOREIGN KEY (agent_id) REFERENCES support_agents(agent_id)
);

📂 Step 5: Insert Sample Customers
INSERT INTO customers VALUES
(1,'Rahul Sharma','Mumbai','2025-01-05'),
(2,'Priya Verma','Delhi','2025-01-08'),
(3,'Amit Patel','Pune','2025-01-10'),
(4,'Sneha Joshi','Bangalore','2025-01-12'),
(5,'Rohan Gupta','Hyderabad','2025-01-15');

📂 Step 6: Insert Sample Support Agents
INSERT INTO support_agents VALUES
(101,'Ankit Mehta','Technical Support','2024-01-10'),
(102,'Neha Singh','Billing Support','2024-03-15'),
(103,'Vikas Sharma','Customer Success','2024-05-20'),
(104,'Pooja Verma','Technical Support','2024-07-01');

📂 Step 7: Insert Sample Tickets
INSERT INTO tickets VALUES
(1001,1,101,'Login Issue','High','2025-02-01 10:00:00','2025-02-01 11:30:00','Resolved',4.8),
(1002,2,102,'Billing Query','Medium','2025-02-02 09:15:00','2025-02-02 10:00:00','Resolved',4.5),
(1003,3,101,'Password Reset','Low','2025-02-02 14:30:00','2025-02-02 14:45:00','Resolved',5.0),
(1004,4,103,'Feature Request','Low','2025-02-03 12:00:00',NULL,'Open',NULL),
(1005,5,104,'Application Crash','Critical','2025-02-03 16:45:00',NULL,'In Progress',NULL);

🧠 SQL Concepts You'll Practice
✔ DDL & DML
✔ INNER JOIN
✔ LEFT JOIN
✔ Aggregate Functions
✔ GROUP BY
✔ HAVING
✔ CASE WHEN
✔ Date & Time Functions
✔ Common Table Expressions (CTEs)
✔ Window Functions
✔ Ranking Functions

📊 Business KPIs You Can Build
📈 Total Support Tickets
📈 Open Tickets
📈 Resolved Tickets
📈 Pending Tickets
📈 Average Resolution Time
📈 First Response Time
📈 First Contact Resolution Rate
📈 SLA Compliance Rate
📈 Customer Satisfaction Score (CSAT)
📈 Tickets by Priority
📈 Tickets by Category
📈 Tickets by City
📈 Agent Productivity
📈 Tickets Resolved per Agent
📈 Average Rating per Agent
📈 Daily Ticket Volume
📈 Monthly Ticket Trend
📈 Repeat Customer Issues
📈 Escalation Rate
📈 Executive Helpdesk Dashboard

🎯 This project reflects real-world SQL analysis performed by Customer Support Analysts, Operations Analysts, Service Delivery teams, Customer Success teams, and Business Intelligence professionals to improve service quality, optimize support operations, and enhance customer satisfaction.

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