Create Customer Journey Map and Transforming Data Using Bold Data Hub

In this article, we will demonstrate how to import tables from a CSV file, create customer journey map using transformations, and move the transformed 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

Creating a Customer Journey Map

Overview

A customer journey map helps to visualize and understand each customer’s experience by analyzing their interaction history. By aggregating data from various touchpoints, such as support tickets, we can track a customer’s path from their first interaction to the most recent one. This analysis helps identify patterns and improve customer experience.

Approach

We aggregate the support tickets based on Customer_ID, ordered by Ticket_Creation_Date to analyze the sequence of interactions. This allows us to track the customer’s journey over time and identify recurring issues or improvements.

SQL Query for Creating a Customer Journey Map

SELECT 
    Customer_ID, 
    Customer_Name, 
    MIN(Ticket_Creation_Date) AS First_Interaction, 
    MAX(Ticket_Creation_Date) AS Last_Interaction, 
    COUNT(Ticket_ID) AS Total_Tickets, 
    AVG(Resolution_Time) AS Avg_Resolution_Time, 
    AVG(Customer_Satisfaction) AS Avg_Satisfaction 
FROM {pipeline_name}.sample_csc_data 
GROUP BY Customer_ID, Customer_Name 
ORDER BY Last_Interaction DESC;