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

1. Summary – Key KPIs at a glance.

2. Loyalty – Repeat buyers, LTV, purchase frequency.

3. Products – ABC/XYZ, revenue by category/vendor, top products.

4. Pricing – Discount metrics, revenue lift, discount time series.

5. 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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