Building an E‑Commerce Dashboard with Power BI and R
Purpose of the Project
Demonstrates how to build an analytical e‑commerce dashboard using Power BI with R for advanced analytics.
Created for the November 2025 DataDNA Challenge, winning both overall and accessibility categories.
Core Analytical Features
Revenue Forecasting
Uses auto.arima from R’s forecast package.
Accounts for seasonality and trends.
Forecasts revenue across country, category, and channel.
Integrated into Power BI with smooth cross-filtering.
ABC/XYZ Product Segmentation
ABC: ranks products by revenue contribution.
XYZ: classifies products by demand variability.
Combined ABC/XYZ matrix supports inventory and marketing decisions.
RFM Customer Segmentation
Based on Recency, Frequency, Monetary.
Defines 9 intuitive customer segments (e.g., Champions, At Risk, Hibernating).
Used to guide retention and engagement strategies.
Implementation Details
Data Cleaning & Transformation
Majority done via R scripts in Power Query.
Includes EDA, preprocessing, forecasting, and an attempted refund prediction model.
Data Model
Visualizations
Dashboard Structure
Summary – Key KPIs at a glance.
Loyalty – Repeat buyers, LTV, purchase frequency.
Products – ABC/XYZ, revenue by category/vendor, top products.
Pricing – Discount metrics, revenue lift, discount time series.
Customers – RFM segments and revenue contribution.
Areas for Improvement
Dynamic Time Periods
Missing Data Sources
Causal Modeling Limitations
Conclusion
The project outlines a full end‑to‑end workflow — from data cleaning to modeling to dashboard design — showcasing how R and Power BI can be combined to build a sophisticated, award‑winning e‑commerce analytics tool.
Read this article if you're interested in the details of building this dashboard: Building an E-Commerce Dashboard with Power BI and R – Frequentist.org
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