Phonepe Data Analysis Dashboard

Project Overview:

In this blog, I’ll walk you through a PhonePe Data Analysis Dashboard. This project covers all the essential data analysis aspects such as transaction tracking, failed payment monitoring, and visualizing multiple services like Insurance, Loans, Money Transfers, and Recharge & Bills.

Project Structure:

PhonePe Data Analysis Project revolves around creating a 5-page Power BI dashboard that displays various transaction and service metrics. The primary goal is to track the following key services:

• Insurance Payments
• Loans
• Money Transfers
• Recharge and Bills

Each of these services have dedicated data visualizations, and the homepage of the dashboard provide an overview of all the metrics for a quick summary.

Dataset Overview:

• User Details: User ID, Name, Age, Join Date
• Transaction Information: Transaction ID, Amount, Service Type, Payment Status, Date, and more
• Service Breakdown: Details about each service, such as UPI payments, insurance premiums, loan types, and recharge types.

Download the dataset from here

Project Explanation:

Creating the Homepage

The homepage summarize the entire project. Key metrics to be displayed include:

• Total Transaction Amount: The total amount transacted via PhonePe.
• Failed Payments: A visualization that tracks failed transactions and identifies the reasons (e.g., server errors, insufficient balance).
• Date Range: A slicer that allows users to select different time periods to analyze transactions.

For each service, I show the total transaction amounts and the number of successful payments.

Service-Specific Dashboards

Next I create individual tabs for each service. The details of each service, such as insurance, loans, and money transfers, displayed in separate pages.

For insurance, I break down transactions into categories like term life insurance, car insurance, health insurance, etc. Similarly, for loans, I analyze loan types, such as auto loans and personal loans.

Each page contain the following visualizations:

• Amount vs Month: A graph showing the total amount transacted each month.
• Service vs Amount: A bar chart displaying the contribution of each service to the total transaction amount.
• Payment Status: A pie chart illustrating the payment status (successful vs. failed payments).

Failed Payment Analysis

A crucial part of the project is monitoring failed payments. In this section, I analyze why certain transactions failed. This displayed in a pie chart showing the reasons for failure (e.g., server error, wrong payment details, insufficient balance). Tracking failed payments is essential for businesses to identify issues and take corrective actions.

Creating Interactivity

One of the highlights of this project is the interactivity. By using Power BI’s slicers, I make the dashboard interactive. For example, users can select different date ranges, and the data displayed will automatically update based on the selection. This feature makes the dashboard more dynamic and useful for decision-makers.

Realtime Dashboard

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