PowerBI Sales Dashboard: Unveiling Answers in Data

Summary

The dataset comprises detailed sales information, capturing various aspects of the sales process, customer demographics, and product specifications. This comprehensive data allows for in-depth analysis and insightful discoveries.

Introduction

I possess an insightful depth in analyzing sales data to answer key business questions. Using Power BI Desktop, I have built interactive dashboards with graphs to address the following questions:

Questions to Answer

1. Total Sales by Product Category and Sub-Category in 2017.
2. Relationships Between Products Sold and Sales Generated
3. Average Shipment Time by Shipment Type and Commonly Shipped Product Categories

Scope of the Project

This project focuses on visualizing data through interactive dashboards created in Power BI.

The dataset consists of the following columns:

– Order Date: Date the order was made
– Ship Date: Date the shipment was sent
– Ship Mode: Type of shipment class (First Class, Second Class, Same Day, Standard Class)
– Segment: Type of customer (Home Office, Consumer, Corporate)
– City: City where sales were made
– State: State where sales were made
– Postal Code: Postal code where sales were made
– Region: Region where sales were made
– Product ID: ID of the product
– Category: Category of the product
– Sub-Category: Sub-category of the product
– Product Name: Name of the product
– Sales: Sales made from the products sold
– Quantity: Quantity of the product sold
– Discount: Discount percentage if the product was sold with a discount
– Profit: Profit made from the product

Power BI Implementation

The data was imported into Power BI without requiring pre-processing. However, creating new columns and measures was necessary to answer the questions effectively.

Total Sales by Product Category and Sub-Category Over the Past 4 Years

To address this question, the key columns used were Sales, Category, Sub-Category, and Order Date.

1. Initial Setup: A table displaying Category vs. Sum of Sales and Sub-Category vs. Sum of Sales was added to the dashboard. These tables were sorted in descending order to highlight the best-selling products.
2. Date Slider: A date slider was introduced to adjust the date range and allow stakeholders to focus on specific periods.
3. Visualization: The tables were transformed into bar charts for a more visually appealing representation.
4. Measures: An additional measure for the average sales amount was created and displayed according to the selected date range for each category and sub-category.

By following these steps, the dashboards provide a clear and interactive view of sales data, enabling stakeholders to make informed decisions.

To answer the question about sales distribution in 2017:

– The average sales across all product categories were $243,220.
– The average sales across all sub-categories were $42,920.

The data indicates that the Technology category generated the most sales, followed by Office Supplies and Furniture. Within the sub-categories, Phones led in sales, followed by Chairs and Binders. Categories like Art, Labels, Envelopes, and Fasteners require further analysis to identify opportunities for increasing their sales averages.

The dashboard also visually represents the sales distribution across the remaining sub-categories.

Relationships Between Products Sold and Sales Generated

To explore the relationships between products sold and the sales they generated, I used a similar approach with tables. Instead of focusing on sales amounts, I analyzed the quantity of products sold. One of the strengths of Power BI is the ability to reuse charts to help address different questions.

For this analysis:

– Tables were created to display the quantity of products sold across different categories and sub-categories.
– Interactive filters were applied so that selecting a specific category or sub-category dynamically updates the data displayed.

This interactive filtering capability allows for easy visualization of the relationships and comparisons between different product segments, providing valuable insights into sales performance.

In 2017, Binders were the most sold item in terms of quantity. However, they ranked third in total sales and belong to the Office Supplies category.

Conversely, Chairs had a lower quantity sold compared to other products but ranked second in total sales. Chairs are part of the Furniture category.

These insights highlight the difference between consumable products and one-time purchases:

– Consumable Products: Priced lower but purchased frequently. Examples include Binders.
– One-Time Purchases: Priced higher and purchased in lower quantities and less frequently. Examples include Chairs.

Understanding these dynamics provides valuable insights for inventory management, pricing strategies, and marketing efforts.

Average Shipment Time by Shipment Type and Commonly Shipped Product Categories

To address the question of average shipment time and commonly shipped product categories, I used the following approach in Power BI:

1. Data Preparation: Calculated the shipment time by subtracting the Order Date from the Ship Date.
2. Visualization: Created tables and charts to display average shipment time for each shipment type (First Class, Second Class, Same Day, Standard Class).
3. Category Analysis: Analyzed which product categories are most commonly shipped under each shipment type.

Key findings include:

– Shipment Time Analysis: Displayed the average shipment time for each shipment type.
– Commonly Shipped Categories: Identified the product categories most frequently shipped with each shipment type.

By visualizing these metrics, stakeholders can better understand the efficiency of different shipment methods and the shipping preferences for various product categories.