The world of business thrives on data-driven decisions, and the ability to harness the power of data is essential. In this project, we embark on an exciting journey to analyze sales data, uncover trends, and reveal valuable insights that will drive strategic decision-making.
Our primary goal is to extract actionable information from a substantial sales dataset, providing a roadmap for optimizing sales strategies and achieving sustainable growth
Within the realm of this project, we will delve deep into a vast repository of sales data, armed with analytical tools and techniques. Our mission is to unearth the hidden gems that lie within these numbers. We will scrutinize sales trends, identify star-performing products, and meticulously calculate essential revenue metrics, including total sales and Average revenue per Order.
But we won't stop at just numbers; we will bring these insights to life through compelling visualizations that resonate with stakeholders
The dataset has sales data from January 2019 to January 2020. It consists originally of 6 columns which was modified further for the purpose of the analysis;
order_id: Unique identifier for each order placed by a customer
Product: Electronics Products sold by the store
quantity: Quantity ordered for each product
order_date: Date the order was placed
Price Each: Price of each product in USD
Purchase Address: Address of each customer who placed an Order
Q1. What is the overall sales trend?
Q2. What are the products performance by revenue?
Q3. What are the Products performance by Quantity?
Q4. What is the Order performance per City?
Q5. When is the peak period of the day?
Q6. What is the revenue contribution between weekdays and weekends?
The analysis was done using Microsoft Power BI.
Data Import: I imported and converted the raw data which was in CSV file format into Microsoft Excel.
Data Cleaning: I cleaned the data to ensure accuracy and consistency this included standardizing the formats, and correcting errors.
Data Transformation: I transformed the data to make it suitable for analysis. This transformation includes calculations, aggregations, and filtering operations. I also created new columns required for the analysis using Microsoft Excel.
Data Visualization: Data Visualization: I visualized the analyzed data using the charts and graphs available on Microsoft Power BI. I also created a dashboard using the insights from the analyzed data.
Total Orders - 186K
Number of Products - 19
Total Quantities Sold - 209K
Total revenue - $34.4M
Average revenue/order - $185.5
Qtr 4 recorded the highest revenue with a total of $11.5M which is 33.49% of the total revenue, while Q1 with a total of $6.8M which is 19.81% of the total revenue recorded the lowest revenue generated.
The most productive month is December with a total revenue of $4.61M which is 13.38% of the total revenue generated. While January has the lowest performance with a total of $1.82M revenue generated which is 5.28% of the total revenue generated. Sales are low at the beginning of the year and highest at the end of the year.
Tuesdays accounted for the highest revenue generated with a total of $5M which is about 14.75% of the total revenue generated. While the day that recorded the least revenue is Thursday with a total of 4.8M which is 14% of the total revenue generated.
The top performing product by revenue is Macbook Pro Laptop, which generated total revenue of $8.04M, while the least performing product is AAA Batteries (4-pack) which generated a total revenue of $900k
The most bought product is the AAA Batteries (4-pack) which sold about 31k quantities, while the product that sold the least number of units is LG Dryer with a total of 646 quantities sold.
The top performing City is San Francisco with a total of 45k orders which is 24% of the total orders received, while the least performing City is Austin with a total of 10k orders which is 5.33% of the total orders received.
Revenue peak at 7pm everyday with a total of $2.4M revenue generated and is lowest at 3am everyday.
Sales over the weekend generated a total of revenue of $9.84M which is 28.52% of the total revenue, while sales during weekdays generated a total revenue of 24.6M which is 71.48% of the total revenue generated.
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The fourth quarter (Q4) stands out as the highest revenue-generating period, contributing 33.49% of the total revenue, while the first quarter (Q1) records the lowest revenue at 19.81%. This indicates a seasonality trend with higher sales towards the end of the year.
December is the most productive month, contributing 13.38% of the total revenue, while January performs the lowest at 5.28%. This confirms a yearly pattern of low sales at the beginning of the year, gradually increasing towards the year-end.
Tuesdays generate the highest revenue at 14.75% of the total, while Thursdays record the lowest at 14%. This indicates variations in daily sales performance throughout the week.
The Macbook Pro Laptop is the top-performing product, generating $8.04M in revenue, while AAA Batteries (4-pack) is the least performing with $900k in revenue.
The AAA Batteries (4-pack) is the most sold product, with approximately 31k units sold, while the LG Dryer is the least sold with only 646 units.
San Francisco is the top-performing city, accounting for 24% of total orders, while Austin performs the least with 5.33% of total orders.
Sales peak at 7 pm every day, generating $2.4M in revenue, and reach their lowest point at 3 am daily.
Sales over the weekend contribute 28.52% of the total revenue, while weekday sales account for 71.48% of the total revenue
Capitalize on the seasonality trend by implementing targeted marketing and promotions during Q4 to maximize revenue. Consider offering special deals during the holiday season. Monthly Strategies: Focus on boosting sales during the beginning of the year, particularly in January, through innovative marketing campaigns and product promotions to counter the post-holiday slump.
Develop strategies to optimize sales on Tuesdays, the highest-performing day, and consider introducing incentives or exclusive offers to drive sales on Thursdays, the lowest-performing day.
Given the high performance of the Macbook Pro Laptop, consider expanding product lines or creating bundles that include this popular item to further boost revenue.
Monitor and manage inventory for AAA Batteries (4-pack) and LG Dryer to ensure that stock levels match demand.
Explore opportunities to increase sales in cities like Austin through localized marketing efforts and tailored promotions.
Leverage the 7 pm peak period by running special promotions or flash sales during this time to maximize revenue.
Continue to focus on weekend sales, potentially with weekend-specific promotions, while maintaining a strong weekday presence.