Customer Support Ticket Data Analysis
Project: Analyze Customer Support Ticket Data (SQL)
Project Overview
In this SQL project from InterviewMaster.ai, I practiced creating a database and a table using pgAdmin 4 and imported a csv file. I then viewed the data using SQL and did not have to do much data cleaning for this analysis. However, I did have to make one adjustment to one field using excel before I could successfully import the csv file. After the data was ready to work with, I then began writing SQL queries to analyze the customer support tickets data to understand common issues and how they are handled. I worked with real-world support data to surface patterns and trends that inform customer service operations.
Data Source: Kaggle Dataset, found here
Learning Objectives
- Write SQL queries to explore marketing campaign datasets
- Clean and standardize data fields
- Aggregate data to find trends in campaign performance metrics
- Practice filtering and summarizing marketing data
Practice Questions
- How many support tickets are in the dataset?
- What are the most common issue types reported?
- How many tickets were submitted through each support channel?
- What is the average resolution time across all tickets?
- How many tickets were resolved on the same day they were submitted?
- How many tickets were submitted each month?
- What is the total number of unresolved tickets?
- Which ticket types have the highest number of unresolved tickets?
- From the unresolved tickets for cancellation requests are they because we have not gotten to them or are we waiting on a customer’s response?
- How many unresolved tickets do we have per ticket priority level and are not waiting for a customer response?
- How many tickets were submitted for each ticket channel?
- How many of the tickets submitted for each ticket channel are unresolved?
- Which ticket channel that has a better average ticket resolution time?
- What is the average customer satisfaction level by ticket_type?
Please see full project on my GitHub.