Superstore Sales Performance Dashboard

TL;DR: A Power BI dashboard project built with .pbip structure for version control and collaboration. It showcases quarterly sales, regional performance, and customer-segment insights. Watch the demo video below to see an executive walkthrough.

Demo: an executive walkthrough of the Superstore Sales Performance Dashboard showing quarterly sales, regional breakdowns, and customer-segment insights.

Project Overview

This project delivers executive-level insights into quarterly sales performance, designed to support data-driven decision-making across key business dimensions, including regional performance, product categories, customer segmentation, and financial strategy.

The underlying dataset is the well-known Superstore dataset, a fictional retail company's transactional sales data. It covers revenue, quantity, profit, and shipping across multiple regions, categories, and customer segments.

You can find the dataset on platforms like Kaggle or Tableau's Sample Data.

Dashboard Snapshot

Field	Value
Report Name	`superstore_dashboard_project`
Current Version	`v1.0.0`
Status	`Published`

Preview

Executive Overview — Quarterly Sales Performance

Regional Sales — West Region Breakdown

#	Page	Purpose
1	Executive Overview	Top-level KPIs: total sales, profit, and a Sales × Profitability matrix across segments and categories
2	Regional Breakdown	Drill-down by region — sales, profit, order count, AOV, and top cities

Other pages (not included in v1.0.0) will cover: - Product category performance - Customer segmentation analysis - Strategic & financial insights (margin analysis, growth trends, etc.)

Global Filters / Slicers

Quarter — Filter all pages by quarter (e.g. 2026-Q3)
Country/Market — Filter by geography (e.g. US or CA)
Region — Available on the Regional Sales page (e.g. West, East, Central, South)

Data Sources

The project uses the Superstore dataset. A flat, single-table CSV source containing transactional sales records for a fictional US-based retail company.

Field Group	Examples
Order info	`Order ID`, `Order Date`, `Ship Date`, `Ship Mode`
Geography	`Region`, `State`, `City`
Product	`Category`, `Sub-Category`, `Product Name`
Financials	`Sales`, `Quantity`, `Discount`, `Profit`
Customer	`Customer ID`, `Customer Name`, `Segment`

Semantic Model

As the source is a single flat table, no complex relational model was designed. Data is imported directly into Power BI with minimal transformation — the primary addition being a dedicated Date dimension for time-based analysis.