https://github.com/gab-182/market-analysis-report-for-national-clothing-chain

Using custom M and DAX codes in Power BI, I conducte a thorough market analysis for a national clothing chain. The insights gathered from customer data and US Census Bureau statistics led to the formulation of a targeted marketing strategy, contributing to enhanced sales and customer satisfaction.
https://github.com/gab-182/market-analysis-report-for-national-clothing-chain

data-analysis power-bi

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Using custom M and DAX codes in Power BI, I conducte a thorough market analysis for a national clothing chain. The insights gathered from customer data and US Census Bureau statistics led to the formulation of a targeted marketing strategy, contributing to enhanced sales and customer satisfaction.

• Host: GitHub
• URL: https://github.com/gab-182/market-analysis-report-for-national-clothing-chain
• Owner: Gab-182
• Created: 2024-01-20T11:44:28.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2024-01-20T12:09:45.000Z (about 1 year ago)
• Last Synced: 2024-11-17T07:16:42.864Z (3 months ago)
• Topics: data-analysis, power-bi
• Homepage:
• Size: 1.37 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# 
Market Analysis for National Clothing Chain


In this repository, you'll find a detailed market analysis report for a national clothing chain using Power BI. The analysis leverages custom M and DAX codes to extract insights from customer data and US Census Bureau statistics.




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General Analysis




The analysis kicks off by predicting customer income based on their last 6 months' purchases, utilizing a linear regression formula: ```Y = mX + b``` In this context, 

  - (Y) represents the average sale per state, dependent on 

  - (X) the average income per state.

  - The correlation between average sales and average income is calculated as **0.78**, demonstrating a strong relationship.



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Customers Analysis


Predicting customer incomes enables a deeper understanding, guiding product recommendations. 

The formula for predicted income is expressed as ```Predicted Income = -722.14 - Y / -0.01``` 

The analysis highlights:





  
Highest income customer: Jon Little (Income: $556.79k)

  
Correlation between income and sale: R^2 = 0.78

  
Customers categorized into income buckets for targeted product recommendations.





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Products Analysis




Building on customer analysis, customers are categorized into three income ranges: Low, Medium, and High. Recommendations by category:




  
Low-Income: Recommend (Shirt) to 44% of customers.

  
Medium-Income: Recommend (Shirt and Sweater) to 39.70% of customers.

  
High-Income: Recommend (Shirt, Sweater, and Leather Bag) to 16.30% of customers.







After studying and analyzing the relation between the customers rating and the return rate of the products, we find that the correlation between the return rate and the customers rating is R^2 = (0.50).


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Other Analysis and Recommendations






Here we show the 10 highest and lowest states by income, which the advertising and product recommendation should consider with the earlier findings such as the customers buys and incomes.




  
Top and bottom 10 states by income are identified for targeted advertising and product recommendations.

  
Recommendations:

    


      
Market the Sweater strategically in states with colder climates to maximize sales.

      
Improve the quality of the Leather Bag for high-income customers, considering its low rating (3.2).

      
Promote Shirts in the lowest 10 states, given their high rating and preference among low-income customers.