Shopify Users Transforming Data with Dashboards


Would you like to know what to do with your large data sheets having numerous columns and rows?

How can you get your large data into a more specific and attractive meaningful dashboard?

Would you like to have the solution to the above-mentioned questions?

In today’s blog, we will discuss a well-known e-commerce platform called the “Shopify.”

Here, you can start, grow, and manage your business, and it offers you to create a website. Also, you could use their shopping cart solution to sell, ship, and manage your products online. For this reason, we can say that it’s a great platform to begin your business startup.

Having all the data in the form of written or table information is difficult to maintain, analyze and get the information. The human brain process visual information with graphs and charts better. This blog will help you understand how you can get your large data imported from Shopify into meaningful information using visualization tools. Thus to explain it better, we are taking the help of the Microsoft Power BI tool.

Let’s begin with concise details of the Microsoft Power BI tool.

Power BI is a business intelligence tool for aggregating, analyzing, visualizing and sharing data. It is a paid tool, which has a trial period of 30 days. Power BI tool is user-friendly and gives you vast choices of visuals to map your data. Microsoft provides the learning tutorials for the Power BI tool, which can be useful for beginners to learn and start their very own data visualization project.

Microsoft Power BI Tutorial Link

Microsoft Power BI Download Link

Shopify provides inbuilt analytics reports which give you insights from recent store activity, site visitors, and store’s transactions. Shopify has three plans to buy, in fact, every Shopify plans have their own benefits and access to briefer analytics reports of the data. On the other hand, using the Power BI dashboard the data imported from Shopify loaded into the Power BI is used for making insights and can be filtered easily.

Users can go through the data of every year month or day and check trend, seasonality, furthermore, decisions can be made.

After creating a dummy Shopify 1 month data of products, orders, transactions, customers, and inventory with similar to the actual Shopify data format, it is loaded into the Power BI to create the dashboard. Later on, You can replace original data with dummy data in the dashboard structure. However, the column header of the data made is similar to the data imported from the Shopify.

First Dashboard: Overview of the User Shopify Data

  • “Orders in a month” the total sale profit of the month.
  • “Order in process” count of the total order purchase, however, payment on delivery orders are left out.
  • “Average order value” calculates the average of order values.
  • “Active customers” counts the number of total active customers.
  • “Total refund amount”. In summary, it is the sum of the amount that is to be refunded finally after the order is canceled or returned.
  • “Average discount value” is the average discount provided to the customers for the orders.
  • “Available product” is the total product available in the inventory.
  • “Average shipping hours”. In summary, is the average time taken to deliver the order?
  • “Number of items sold and the total sales amount” is the total amount profited by the seller and the number of quantities sold by the seller.

Second Dashboard: Order Detail of the User Shopify Data

  • “Orders in a month” count of monthly order purchased.
  • “Average cart value” average of the order amount paid.
  • “Fulfilled order”. In summary, the number of fulfilled orders.
  • “Average of fulfillment” percentage showing the total order fulfilled out of the total order made.
  • “Total profit”  total profit after selling items every day.
  • “Total revenue and the total quantity of the item sold” shows the total revenue made for the total quantity of items sold.

Third Dashboard: Product Detail of the User Shopify Data

  • “Available product” the total product available.
  • “Most used inventory” is the inventory containing a complete list of products and also the product in stock.
  • “Fulfilled order”. In summary, the number of fulfilled orders.
  • “Total inventory quantity” is the number of total goods in the stock available in the inventory.
  • “Most selling product” is the topmost trending product in demand.
  • However “The 10 most bought products on the daily report” is the trending 10 products and the total quantity sold.

Fourth Dashboard: Shipping Detail of the User Shopify Data

  • “Average shipping hours” average hour took to ship the orders.
  • “Average shipping cost” is the average shipping charge cost.
  • “Fulfilled order”. In summary, the number of fulfilled orders.
  • “Order canceled” the order canceled and thus returned.
  • However, “Average of shipping charges country-wise” the order purchased from the country and the average shipping charges applicable to that country.

  • “Count of order fulfillment by the financial status”
    • Use the drill-down function in the pie chart to show the percentage of order that is fulfilled and unfulfilled.
    • However, You can drill down again to see the percentage of the order payment paid and the percentage of order payment pending as shown in the pie chart.

Fifth Dashboard: Customer Detail of the User Shopify Data

  • Above all, “total customers” is the number of associated store customers associated.
  • “Average order value” is the customer’s daily average purchase.
  • “Fulfilled order”. In summary, the number of fulfilled orders.
  • “Order canceled” the total order canceled.
  • “Top 5 frequent buyers by order count” is the top 5 buyers purchasing the maximum products.
  • However, “top 5 frequent buyers by order value” is the top 5 buyers purchasing with the maximum order value.

This blog discusses the mostly asked basic insights into data acquisition. There are many more aspects that would need detailed coverage. Please feel free to leave feedback or suggestions in the comment section below. To know more about our services, please visit Loginworks Softwares Inc.

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