Relational Data Modeling
Orders, customers, payments, reviews, products, and order items were connected into a relational reporting model designed to support KPI analysis across multiple business areas.
Overview
Project Summary
E-commerce performance depends on more than revenue alone. Order activity, delivery timing, customer reviews, and purchasing behavior all influence how a marketplace performs over time.
This reporting workflow brings those operational and customer metrics into a single dashboard focused on KPI visibility, trend analysis, and day-to-day business reporting.
The reporting structure separates high-level KPI monitoring from deeper operational analysis, making it easier to track performance while still exploring the trends driving those results.
Business Problem
Reporting Questions Addressed
The reporting focuses on operational and customer metrics commonly used to evaluate marketplace performance and fulfillment reliability.
- How is revenue trending over time?
- What is happening with order volume and average order value?
- How reliable is delivery performance across the platform?
- Do late deliveries impact customer review scores?
- Are customers primarily one-time buyers or repeat purchasers?
- How concentrated is installment usage across customer purchases?
- Which operational metrics appear most connected to customer satisfaction?
Approach
How The Reporting Was Structured
By identifying business questions and focusing on key metrics, the reporting workflow was organized around operational visibility, customer behavior, and revenue performance.
KPI Reporting
Revenue growth, order activity, delivery timing, customer reviews, and installment behavior were structured into reporting metrics focused on operational and customer visibility.
Dashboard Architecture
Executive KPI reporting was separated from more detailed operational analysis to keep the reporting focused, readable, and easier to navigate.
Validation & Preparation
Power Query workflows and lightweight Python audit scripts were used to validate relationships, identify inconsistent records, and prepare the dataset for reporting.
Main Dashboard
Executive KPI Overview
The main reporting view brings revenue performance, fulfillment reliability, customer satisfaction, and purchasing behavior into a single operational snapshot.
Trend Analysis
Focused Reporting Views
Supporting dashboard views expand on customer behavior, order trends, review activity, and purchasing patterns across the marketplace.
Key Insights
Operational & Customer Findings
- Revenue trends upward over time, reflecting sustained marketplace growth.
- Approximately 89% of orders are delivered on time, indicating generally strong fulfillment performance.
- Late deliveries correlate with noticeably lower customer review scores, directly linking operations to customer satisfaction outcomes.
- The customer base is heavily concentrated around one-time purchasers, highlighting opportunities for stronger retention strategy.
- Installment usage remains concentrated within lower installment counts, while a smaller segment of customers relies on extended financing behavior.
- Revenue growth and average order value trend upward together, indicating increasing platform usage and higher-value purchasing activity.
Tools Used
Technology Stack
Power BI
KPI reporting, relational modeling, dashboard structure, interaction flow, and business-focused analytics.
Power Query
Data preparation, cleanup workflows, transformation logic, and reporting preparation.
Python
Dataset auditing, null analysis, duplicate validation, and preprocessing support.
Outcome
Reporting Scope
The reporting workflow covers the process from data intake and validation through relational modeling, KPI reporting, dashboard architecture, and operational analysis.
Revenue visibility, delivery performance, customer satisfaction, and purchasing behavior were brought into a single reporting layer designed around day-to-day business visibility.
The final dashboard balances executive KPI reporting with deeper operational and customer trend analysis across multiple areas of the business.