Customer Satisfaction Summary and Transforming Data Using Bold Data Hub

In this article, we will demonstrate how to import tables from a CSV file, generate a customer satisfaction summary through transformations, and move the cleaned data into the destination database using Bold Data Hub. Follow the step-by-step process below.

Sample Data Source:

Sample CSC Data

Step-by-Step Process in Bold Data Hub

Step 1: Open Bold Data Hub

• Click on the Bold Data Hub.

Step 2: Create a New Pipeline

• Click Add Pipeline in the left-side panel.
• Enter the pipeline name and click the tick icon.

Step 3: Choose the Connector

• Select the newly created pipeline and opt for the CSV connector. You can either double-click or click on the Add Template option to include a template.

Step 4: Upload Your CSV File

• Click the “Upload File” button to select and upload your CSV file.

Step 5: Set the Properties

• Copy the file path and paste it into the filePath property field.

Step 6: Save and Choose the Destination

• Click Save, choose the destination, and confirm by clicking the Yes button.

Note: On-Demand Refresh will be triggered when the pipeline is saved. If needed, the pipeline can be scheduled in the Schedules tab.

Step 7: View Logs and Outputs

• Click the pipeline name in the left-side panel and switch to the Logs tab to view logs.

Step 8: Apply Transformations

• Go to the Transform tab and click Add Table.

• Enter the table name to create a transform table for customer satisfaction summary.

Note: The data will initially be transferred to the DuckDB database within the designated {pipeline_name} schema before undergoing transformation for integration into the target databases. As an illustration, in the case of a pipeline named “customer_service_data”, the data will be relocated to the customer_service_data table schema.

Learn more about transformation here

Customer Satisfaction Summary

Overview

Measuring customer satisfaction helps evaluate service quality, agent performance, and regional trends. This query calculates the average satisfaction score (or NPS) by region, agent, and ticket category.

Approach

We aggregate Customer Satisfaction Scores for resolved tickets using:

• By Region → Understand satisfaction trends across locations
• By Agent → Assess individual agent performance
• By Ticket Category → Identify service types needing improvement

SQL Query for Customer Satisfaction by Region

SELECT 
    Region, 
    AVG(Customer_Satisfaction) AS Avg_Satisfaction_Score 
FROM {pipeline_name}.sample_csc_data 
WHERE Ticket_Status = 'Resolved' 
GROUP BY Region 
ORDER BY Region;

SQL Query for Customer Satisfaction by Agent_ID

SELECT 
    Agent_ID, 
    AVG(Customer_Satisfaction) AS Avg_Satisfaction_Score 
FROM {pipeline_name}.sample_csc_data 
WHERE Ticket_Status = 'Resolved' 
GROUP BY Agent_ID 
ORDER BY Agent_ID;

SQL Query for Customer Satisfaction by Ticket_Category

SELECT 
    Ticket_Category, 
    AVG(Customer_Satisfaction) AS Avg_Satisfaction_Score 
FROM {pipeline_name}.sample_csc_data 
WHERE Ticket_Status = 'Resolved' 
GROUP BY Ticket_Category 
ORDER BY Ticket_Category;