E‑Commerce Dashboard (Power BI and R)

Aleksei_Pr — Thu, 26 Mar 2026 17:32:36 GMT

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

Power BI model follows a star schema for performance and clarity.

Visualizations

Mostly standard Power BI visuals.
Some custom charts built with Deneb using Vega‑Lite.

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

Current dataset ends on 2025‑10‑21.
Dashboard locked to 2025; needs a relative date slicer for real‑world use.

Missing Data Sources

No web analytics or marketing data, limiting behavioral and campaign analysis.

Causal Modeling Limitations

Synthetic dataset too random for meaningful causal inference.
Real data required for deeper insights.

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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Re: E‑Commerce Dashboard (Power BI and R)

Aleksei_Pr — Thu, 26 Mar 2026 17:26:55 GMT

Here are my other dashboards combining visualization, forecasting, and causal analysis:

Credit Risk Simulation Dashboard (Power BI + R)

Consumer Financial Complaints Dashboard

topic Re: E‑Commerce Dashboard (Power BI and R) in Data Stories Gallery