Global Super Store (Power BI)
The Situation:
Global Super Store is an online retail store with customers across the globe offering a variety of products categorized into three main sections which includes Technology, Office Supplies, and Furniture. Our goal is to transform their raw data into meaningful insights and recommendations for management. More specifically, we need to:
- Track KPIs (sales, revenue, profit, returns)
- Compare regional performance
- Analyze category and product-level trends
- Identify high-valued customers
- Identify top performing sales persons
The Data:
We’ve been given a collection of raw data (CSV files), which contain information about transactions, products, customers, sales person, and sales territories, spanning from the years 2018 to 2020.
The Task: We are tasked with using solely Microsoft Power BI to:
- Connect and transform/shape the data in Power BI’s back-end using Power Query
- Build a relational data model, linking the 8 fact and dimension tables
- Create calculated columns and measures with DAX
- Design a multi-page interactive dashboard to visualize the data in Power BI’s front-end
The Process:
1. Connecting and Shaping the Data
Firstly, we imported the data into the Power Query editor to transform and clean it. The next process involved:
Removing Duplicates: Duplicate entries were removed from the dataset to ensure accurate analysis.
Handling Null or Missing Values: For some columns, missing values were replaced with defaults or averages. Null values in “key” columns were removed using filters.
Data Type Conversion: Columns were converted to appropriate data types to ensure consistency. Dates were converted to Date type, numerical columns to Decimal or Whole Numbers, and text columns to Text.
Column Splitting and Merging: Several columns were split to separate concatenated information, or merged to create a unified name (such as Customer Full Name).
Standardising Date Formats: All date columns were formatted consistently to facilitate time-based analysis. This step was important for ensuring accurate time-series analysis in Power BI.
Removing Unnecessary Columns: Irrelevant columns were removed to streamline the dataset. This helped focus the analysis on relevant information, reducing memory usage and improving performance.
2. Building a Relational Data Model
The data is combined into a single table and has all the required information. So, there’s no need for modelling but we created a Calendar table to easily analyze data.
3. Creating Measures
Next, we used Power BI’s front-end formula language, DAX, to analyze our relational data model and create several calculated columns (for filtering) and measures (for aggregation), that we could later reference and use when analyzing and visualizing the data.
The list of calculated measures is available below and includes key information on revenue, profit, orders, returns, and more.
4. Visualising the Data
The final step of the project was creating a multi-page interactive dashboard, including a range of visuals and KPIs that could serve management and lead to informed decision-making. We used several visuals and tools to demonstrate and visualize the data across the 12 report pages, including KPI cards, line and bar charts, waterfall charts, matrices, gauge charts, maps, donut charts, and slicers. We made sure the report was fully interactive and simple to navigate, with icons used to enable filters, cancel filters, and guide users to each report page with ease. Features such as drill-through, bookmarks, parameters, and tooltips were also used throughout the dashboard, further enhancing its usefulness and impact on management.
Executive Dashboard: The first report page provides a high-level view of the overall performance. We used card visuals to present Key Performance Indicators such as overall revenue, profit, total orders, and average quantity per order. To track an overall Revenue performance, we used a line chart that shows that each year from January to July there is a decline in revenue and then from September to December the store had fetched nice revenue. Another useful information that the chart has is that the covid-19 had caused a massive decline in sales causing a drastic decline in revenue.
We also included donut charts to visualize sales by segment and also quantity shipped by shipping mode. A combination of line and column chart nicely depict the profit margin throughout the period. We used a matrix to display the top 25 customers based on key indicators (revenue, profit, total orders and quantity sold).
Key Insights: The second report page displayed a more in-depth view of the store’s performance with more Key Performance Indicators such as total transactions, total quantity sold, percentage of profitable transactions, percentage of profitable products, percentage of discounted loss transactions (products in loss with discount as well) and percentage of profitable customers. A donut chart that shows contribution of each category in profit, a bar chart for profit by market, a matrix for showing sales performance, a bar chart for count of transactions by duration and a tree map for total transactions by order priority.
Market: The third report page focuses on detailed market-level analysis. It displayed detailed product information for countries in the selected market (drill through from Key Insights report page). It also included a column chart for visualizing profit by category in the current market, a donut chart for visualizing shipments within SLA, KPI visuals for Revenue and Orders comparisons (current vs previous years), and an interactive line chart showing, revenue, profit, quantity sold, total orders, and total transactions (using parameters that can be switched to display the selected parameter).
We also included further report pages for a deeper analysis of the market like Country, State and City.
Country:
State:
City:
Category: The next reportpage throws a light on how a category has performed during the period. We have used donut charts to visualize shipping cost by ship mode and total orders by order priority, a bar chart that visualize how a category has performed in each market, a matrix that lists top 100 profitable products and a waterfall chart that shows a breakdown of profit by subcategory in the selected category.
We also included further report pages for a deeper analysis of the category like subcategory, and product.
Subcategory:
Product:
Customer Detail: The next report page provided a deeper insight into customer behavior and value. It used donut charts to break down order shipments, a matrix to show products purchased by the selected customer with further variables like profit, shipping cost, order priority etc. Some further visuals are also being added to track customers purchase behavior.
Sales Person: In order to visualize the sales person’s performance, we have included a separate report page, using a drill through feature, that shows the overall performance of a sales person using different visuals. A matrix that displays products sold by a sales person with orders and quantity, a funnel chart that shows profit by subcategory, a KPI card for current vs previous orders, and a line chart that shows profit, revenue, orders and transactions.
Segment: The next report page displays sale for a selected segment. A matrix table to display market wise performance for a segment, KPI cards for revenue and orders comparison, and an area chart for showing revenue, profit, orders and transactions over time.
