A well-designed ecommerce Power BI dashboard can transform the e-shop data into insightful visualizations. Power BI integrates with various data sources like Facebook Ads, Shopify, enabling comprehensive data consolidation. Users can also track sales, monitor inventory, and analyze customer behavior in real-time.
In this article we will go through a real-world case study for how ECommerce Power BI Dashboard were used for driving informed decisions.
Power BI’s advanced analytics features, such as DAX (Data Analysis Expressions) offered deep insights into sales trends, customer preferences, and operational efficiency. Additionally, its intuitive interface allowed for easy customization and sharing of reports, facilitating data-driven decision-making and enhancing overall business performance.
There are lots of reasons why ecommerce brands may want to build Power BI dashboards.
The specific dashboard we are discussing here was built for a hemp oil brand that needed a single source of truth for the management team to make decisions. Having their data disjointed in Facebook, Google, Bing, Google Analytics, Klaviyo and Shopify was making it way too difficult to produce in-depth analysis of the business.
On top of that, this specific ecommerce brand in our case study a Power BI dashboard to answer the following business questions:
Power BI has native integrations with data sources like Google Analytics which are widely used in ecommerce analytics. However, if you want to build a highly functional Ecommerce Power BI dashboard, you also need to connect to sources like Shopify, Facebook Ads and many others. These sources don’t have a native integration but not to worry! There are lots of third-party integrations between these sources and Power BI.
We used the Vidi Corp Power BI Connectors for this case study to extract the ecommerce data. These connectors enabled us to automatically extract the data into an Azure SQL Server database and make it readily available for the analysis. We recommend these connectors for multiple reasons:
This section of the Power BI dashboard analyses the ecommerce sales data which primarily uses Shopify data. Let’s walk through it together to explore the KPIs and uncover insights from the data.
This view highlights various KPIs, including Orders, Unique Customers, Sales, Discounts Offered, Average Order Value (AOV), New Customers, Leads, and Conversions
Crucially this dashboard compares these important metrics for new and returning customers. It is obvious from the dashboard that returning customers have a 20% higher AOV and account for 42% of revenue. This highlights the importance of marketing to the returning customers through the newsletter and PPC remarketing campaigns.
We have a customer segment that outlines various KPIs related to customers, including New and Returning users.
Additionally, we have KPIs that classify these users based on revenue and the number of orders.
We also create a loyalty and retention Power BI dashboards for some of our ecommerce clients using the data from Shopify. This dashboard is designed to help the users find specific customers by loyalty level and their stage of life cycle.
The loyalty metric is a score from 0 to 1 which is assigned to every customer from Shopify. It is calculated as a factor of average order value, shopping frequency and how many different products the customer bought.
The Retention metric is calculated as the latest time when the purchase was made by a particular customer. A customer is then assigned a label based on multiple conditions
The dashboard allows the users to filter by loyalty and retention group and find the exact emails of customers to target in emails. This way the users can extract the list of dormant customers and work to reactivate them through email
The next 2 pages of the dashboard analyse which products new customers buy and which products the most loyal customers buy. These products can be used to reactivate dormant customers through remarketing campaigns.
This view presents various KPIs such as revenue, orders, and Average Order Value (AOV) by geographical location. In this case study It displays data by province according to business requirements, allowing the business to identify which provinces generate more sales than others.
A map visualization helps in identifying sales in each province and table visuals helps in detailing the performance of each province in terms of Price, Orders, and Average Order Value (AOV).
This view represents the email marketing metrics from Klaviyo. The website visitors can sign up for the newsletter which is a massive source of revenue from returning customers. Every email marketing campaign is analysed for performance metrics
This view provides insights into ad performance and Customer Acquisition Cost/Lifetime Value from various sources.CAC (Customer Acquisition Cost) is the cost of acquiring a new customer through marketing and sales efforts where LTV (Customer Lifetime Value is the total revenue a business expects to earn from a customer over their entire relationship.
MRR represents regular monthly income and it helps track short-term revenue trends.
ARR reflects steady annual income and it offers an annualized view of recurring revenue for future financial forecasting.
Overall we can conclude that, Analyzing sales data with Power BI enhances decision-making by providing clear insights through interactive dashboards and reports.
It helps identify trends, track performance, and optimize strategies, leading to improved sales, better customer understanding, and more informed business decisions, ultimately driving growth and efficiency.
