Mexico Toy Sales Analysis
Jan 1, 2026 00:00 · 556 words · 3 minute read #Power BI #Data Analysis Download

Retail Sales Analysis: An Interactive Power BI Dashboard

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Welcome to my Power BI portfolio! This project showcases my end-to-end skills in data analytics, from cleaning and transforming data to building a feature-rich, interactive dashboard that delivers actionable business insights.

Author: Ernando Taufiq Nur Hidayat

📈 Interactive Power BI Report

You can interact with the dashboard preview below. For a full-screen experience, open the PDF.

Download dashboard preview (PDF)

📝 Project Executive Summary

This project analyzes sales data from a fictional retail company to identify key trends, profit drivers, and areas for growth. The dashboard is designed to provide stakeholders with quick, data-driven answers to critical business questions.

  • Data Source: Maven Analytics Guided Project Dataset.
  • Tools Used: Microsoft Power BI (Power Query, DAX), Excel.

🎯 Project Objectives:

  1. Identify the best and worst-performing product categories and individual products.
  2. Analyze sales performance across different geographical locations (cities and store types).
  3. Uncover sales patterns and trends over time.

🔑 Key Findings & Insights:

  • Healthy Profitability Driven by High Volume: The business operates with a healthy 28.6% profit margin, but relies heavily on high-volume sales of low-cost items, resulting in a low Average Order Value (AOV) of ~$16.89.
  • “Downtown” Store Dominance: The “Downtown” store location is a clear outlier, contributing 56% of the total profit, indicating successful strategies that could be replicated elsewhere.
  • Strong Seasonal Sales Peak: A significant sales spike occurs around the year-end holiday season (Q4) and the beginning of the year (Q1), which is critical for inventory and marketing planning.

🛠️ Technical Process & Methodology

1. Data Extraction, Transformation, and Loading (ETL)

  • Used Power Query to connect to and import data from multiple CSV files.
  • Performed extensive data cleaning and transformation, including:
    • Correcting data types (e.g., text to date).
    • Handling null or missing values.
    • Creating new custom columns to support deeper analysis (e.g., extracting Year and Month from a date column).

2. Data Modeling

  • Designed an efficient Star Schema to optimize report performance and ensure accurate calculations.
  • Established correct relationships between fact and dimension tables.
  • Created a dedicated Date Table using DAX to enable complex time intelligence analysis.

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3. DAX Calculations

I wrote several dynamic Measures using DAX to create key business metrics. Some notable examples include:

  • Total Revenue & Profit: Total Revenue = SUM(Sales[Revenue])
  • Profit Margin: Profit Margin = DIVIDE([Total Profit], [Total Revenue])
  • Time Intelligence: Used functions like SAMEPERIODLASTYEAR and DATESYTD to compare performance over time.

4. Visualization & Dashboard Design

  • Focused on data storytelling, where each visual was chosen to answer a specific business question.
  • Maintained a consistent color scheme and layout to ensure a clean and intuitive User Experience (UX).
  • Added interactive features like slicers, informative tooltips, and drill-through capabilities to allow for self-service analysis by the end-user.

💡 Data-Driven Business Recommendations

Based on the insights uncovered, here are several actionable recommendations:

  1. Implement an Average Order Value (AOV) Growth Strategy: Given the low AOV, the company should introduce product bundling or frequently-bought-together recommendations at checkout.
  2. Replicate the “Downtown” Success Model: Conduct a deep-dive case study on the Downtown store’s operations, staffing, and local marketing strategies to create a blueprint for underperforming locations like “Airport”.
  3. Diversify the “Electronics” Category: Re-evaluate the product portfolio and create targeted promotions to boost sales volume.

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