/Power_BI_Customer_Insights_and_Target_Market_Analysis_Project

This project showcases Power BI skills by analyzing customer behavior and preferences in the automotive market. Using interactive dashboards, it provides insights into demographics, car brands, models, and purchasing trends, highlighting the use of Power BI for data-driven decisions.

🔍 Customer Insights and Market Analysis with Power BI


📋 Overview

This project leverages Power BI to analyze customer behavior and preferences in the automotive market. By exploring key metrics such as car brand and model popularity, average viewed car prices, and customer demographics, the analysis provides actionable insights to support strategic decision-making and targeted marketing efforts.

Table of Contents

  1. 🎯 Objectives
  2. 🔧 Technologies Used
  3. 📊 Data Overview
  4. 🔍 Key Insights
  5. 🧠 Key Concepts and Methodology
  6. 📈 Analysis and Findings
  7. 💡 Recommendations
  8. ⚠️ Limitations and Proposals for Improvement
  9. 🎉 Conclusion

🎯 Objectives

  • 🔍 Identify key customer segments based on demographics, viewing preferences, and purchasing behaviors.
  • 📈 Provide actionable insights to guide marketing strategies and enhance customer engagement.
  • 🛠️ Utilize advanced Power BI features for data visualization, including DAX calculations and interactive dashboards.

🔧 Technologies Used

Power BI

  • Microsoft Power BI: A business analytics tool for interactive visualizations and business intelligence, enabling integration of multiple data sources and creation of detailed reports and dashboards.

📊 Data Overview

The dataset includes customer data from appointments for car test drives, with insights into demographics, car viewing preferences, and transaction status.

Feature Description
gender Customer's gender: Male or Female.
age Age range of the customer.
car_brand_most_viewed Most frequently viewed car brand by the customer.
car_model_most_viewed Most frequently viewed car model by the customer.
car_body_type_most_viewed Most frequently viewed car body type (e.g., sedan, SUV).
user_state State derived from the customer's address.
user_city City derived from the customer's address, anonymized with placeholders.
average_viewed_car_price Average price of cars viewed by the customer.
transacted Indicates if the customer completed a car purchase.

🔍 Key Insights

  • Transaction Count: 699 customers completed purchases.
  • Average Viewed Car Price: Approximately 70.27K across all viewed cars.

🧠 Key Concepts and Methodology

Fundamentals

  • Data Integration: Merging data from multiple sources for a unified analysis.
  • Segmentation: Using Power BI’s slicers and filters to analyze data by demographics and transaction status.
  • Basic Visualizations: Implementing bar charts, line graphs, and KPI cards to highlight key metrics.

Advanced Concepts

  • DAX Calculations: Creating custom metrics such as averages and cumulative counts to enhance analysis depth.
  • Interactive Dashboards: Designing dynamic dashboards for user-driven data exploration.
  • Data Storytelling: Structuring visualizations to guide stakeholders through findings and recommendations.

📈 Analysis and Findings

Customer Insights Dashboard

  1. Transaction Overview:

    • Total Transactions: 699 completed transactions.
    • Average Price Viewed: 70.27K, indicating a preference for mid-range car prices.

  2. Demographic Analysis:

    • Age and Gender:
      • Highest interest in expensive cars among males aged 36-45 (81K average).
      • Females aged 56+ show interest in more affordable options compared to males.

  3. Brand and Model Preferences:

    • Top Brands: Brand R popular among older demographics (56+), Brand U favored by younger customers (18-25).
    • Top Models: Model 78 popular among 46-55 age group.

  4. Geographical Insights:

    • State Variations: Higher engagement and preference for premium brands in Selangor and Kuala Lumpur.

Target Market Dashboard

  1. Metrics Target:

    • Body Type Preference: SUVs lead in popularity, especially for Brand B.
    • Brand Pricing: Higher average prices for Brand B’s SUVs indicate a premium market segment.

  2. Demographics Target:

    • City-Level Preferences: Model 131 is a top choice in City I, suggesting potential for localized marketing.
    • Age-Based Insights: Customers aged 26-35 prefer mid-range models like Model 53.

💡 Recommendations

  1. Age-Specific Campaigns:

    • Focus on males aged 36-45 and females aged 56+ with targeted marketing for popular models within these demographics.

  2. Promote SUVs:

    • Highlight SUV models, especially from Brand B, to capitalize on the segment’s higher interest and pricing.

  3. Regional Customization:

    • Tailor marketing strategies for states with higher interest in premium brands, like Selangor and Kuala Lumpur.

⚠️ Limitations and Proposals for Improvement

Limitations:

  • Incomplete Data: The dataset may not capture all customer interactions, especially those without completed transactions, which may skew the analysis.
  • Lack of Real-Time Data: The analysis doesn’t consider dynamic factors like real-time pricing changes or promotional impacts.

Proposals for Improvement:

  • Broaden Data Sources: Integrate data from web analytics and social media to build a more comprehensive view of customer behavior.
  • Incorporate Predictive Analytics: Develop models to predict customer preferences and potential purchases using historical data.

🎉 Conclusion

This project highlights the use of Power BI to analyze customer behavior in the automotive sector, providing insights that drive data-informed marketing strategies. By leveraging interactive dashboards and advanced analytics, the project supports strategic decision-making, leading to enhanced customer targeting and engagement.