Optimizing Online Sales: Analyzing Marketplace Data
In the rapidly evolving world of e-commerce, understanding marketplace data is the key to staying ahead of the competition. With consumers increasingly relying on online platforms for their shopping needs, businesses must leverage data-driven insights to optimize their sales strategies. This post delves into the art and science of marketplace data analysis, exploring how businesses can harness the power of data to boost sales, enhance customer experiences, and drive growth. Whether you're a seasoned online retailer or just starting out, this guide will provide valuable insights into making informed decisions that can transform your online sales strategy.
In this demonstration, we'll delve into a comprehensive dataset of online sales transactions spanning various product categories. Each row in the dataset represents a single transaction, providing detailed information such as the order ID, date, category, product name, quantity sold, unit price, total price, region, and payment method. When working with sales data in SAS Visual Analytics, it's essential to understand the significance of each column for effective analysis. The Order ID is a unique identifier for every sales order, ensuring that each transaction is distinct and easily traceable. The Date column records the exact date of the transaction, enabling trend analysis over time. The Category column classifies products into broad groups—such as Electronics, Home Appliances, Clothing, Books, Beauty Products, and Sports—allowing for an overview of sales performance across different product types. The Quantity column indicates the number of units sold in each transaction. When combined with the Unit Price, which reflects the cost of a single unit, it helps calculate the Total Price, representing the revenue generated from each sale. The Region column specifies the geographic location of the transaction, whether it occurred in North America, Europe, Asia, or another region, providing valuable insights into regional sales trends.
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SAS Viya is a powerful cloud-based analytics platform that offers a unified environment for data management, advanced analytics, and AI-powered insights. It interacts smoothly with a variety of data sources, enabling users to access, study, and analyze their data in real time. In this demonstration, we'll use SAS Viya's Explore and Visualize capabilities to gain insights from our sales dataset. This allows for dynamic visuals and easy data exploration. This technique not only improves our knowledge of the data, but it also enables us to make data-driven decisions more efficiently.
Data Preparation
First, we need to make a new report and load our data into memory to start making so visualizations to gain some insight. We start by clicking on "New Report" and load our data from our local device into the dashboard. The data we will be using for this demonstration can be found on the Kaggle website. This website provides a vast amount of datasets with different genres such as, computer vision, education, marketing, and stock market trend data.
Now that we have our data loaded in-memory, let's start by bring our data to the dashboard by using the "Objects" tab to visualize the data by using the "List table" function. We can drag and drop the "List table" function or you can double-click to receive the same result.
In the data, we are able to see 11 columns listing the details of the dataset. The goal is to provide a dashboard that shows transactional payment methods that consist of Credit Card, Debit Card, and PayPal transactions. The region for the dataset are listed as Asia, Europe, and North America, we will build some visualization to provide insight into the Total revenue by payment method, region and product category and payment method for units sold.
Visualization
In the illustration, we used a butterfly chart to display the total revenue by payment method and unit price. We can observe that majority of payments to purchase product were made through credit cards with a total of $ 51,170.86 with a unit price 38,761.14. The unit price is number of units of the product sold in the transaction, this helps in showing potential profit margin of product sold. We see that next highest payment method used was PayPal with a total revenue of $21,268.06 with a unit price of $15,272.3.
For this demonstration, we used a dual-axis bar-line chart to display the total revenue by region. We can observe that majority of revenue by region was North America with a total of $ 36,844.34 with a unit price 28,309.78. The unit price is number of units of the product sold in the transaction, this helps in showing potential profit margin of product sold per region. We see that next highest revenue used was Asia with a total revenue of $22,455.45 with a unit price of $13,152.82.
Here, we used a pie chart to display the payment method for unit's sold with a total number of transaction being 518. We can observe that majority of payment for units sold was through credit with 286 customer using this method to purchase products. The next form of payment for unit's sold was debit cards with 145 customer's using this form of payment to purchase products. Lastly, the final payment method used to purchase products was through PayPal with 105 unit's being purchased through this payment method.
For this illustration, we used a pie chart to display the total revenue by product category and unit price. We notice from the pie chart that electronic products bring in the most revenue with $ 34,982.41 and Home appliances with a total revenue of $18,646.16. We notice that for all regions for product sold the lowest amount of products sold consisted of beauty products and clothing with both combine bring in around $11,000.
Conclusion
We explored a detailed online sales dataset using SAS Viya, focusing on key aspects like product categories, transaction dates, and regional sales trends. By leveraging SAS Viya's powerful analytics and visualization tools, we gained real-time insights that enhance our ability to make data-driven decisions. This process underscores the importance of understanding data at a granular level and highlights how platforms like SAS Viya can be crucial for effective sales analysis and business strategy.
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