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Analyzed Adidas' product sales performance, top retailers, monthly trends, yearly growth, regional distribution, and pricing insights. Performed ETL from Python (Pandas) to SQL Server, extracted data with SQL, and visualized key insights in Excel.

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Adidas Sales Analysis

Adidas Sales

Table of Contents

Project Background

This project aims to explore and assess the performance of Adidas' sales through different retailers across regions in the US. We'll investigate factors like product performance, regional differences, sales methods (in-store vs. online), and profitability to uncover trends and opportunities for improving sales strategy.

Insights and recommendations are provided on the following key areas :

  • Product Sales Performance: Analyzing sales distribution across product categories to identify high-performing and underperforming items, guiding portfolio adjustments.

  • Top Retailer Performance: Assessing retailer sales and profit figures to determine the highest and lowest performers, informing retailer-specific strategies.

  • Seasonal Sales Trends: Examining monthly sales and profit data to identify peak and low-performing months, providing insights for seasonal inventory and promotions.

  • Regional and Channel Contributions: Analyzing sales distribution across regions and sales channels to understand market share and the impact of digital versus in-store sales.

  • Pricing and Demand Patterns: Evaluating sales by price range and seasonal unit sales trends to identify optimal pricing strategies and seasonal demand variations.

Detailed Resources:

  • The Pre-Processing process utilized can be found here.
  • An interactive Excel dashboard can be downloaded here.

Data Structure

Adidas Sale's database structure as seen below consists of one table: Sales with 9648 records.

Executive Summary

Overview of Findings

The retailer’s product portfolio shows men’s street footwear as the top seller, while online channels and the West region lead in sales distribution. July sees peak monthly sales and profit, with pricing in the $30–$60 range driving strong unit volumes. August has the highest average units sold, aligning with seasonal demand. New York is the top-performing location, showing notable regional sales variation.

Below is the overview page from the Excel dashboard and more examples are included throughout the report. The interactive dashboard can be viewed here.

Adidas Sales Dashboard

  • Product Sales Performance: The retailer's product portfolio comprises six categories, with men’s street footwear leading in total sales at $27,680,769, significantly surpassing women’s street footwear at $17,201,563. Conversely, women’s athletic footwear has the lowest sales, amounting to $14,315,521, followed closely by men’s athletic footwear at $20,577,180.

  • Top Retailers by Sales and Profit: West Gear emerges as the leading retailer, recording the highest sales at $32,409,558 and profit at $121,196,890. In contrast, Amazon shows the lowest performance, with sales and profit figures at $10,096,987 and $3,984,432, respectively.

  • Monthly Sales Trends: July marks the peak month with sales of $12,550,419 and profits of $4,780,283. Conversely, March experiences the lowest performance, with sales at $7,694,984 and profit at $2,946,398, suggesting seasonal fluctuations in consumer demand.

  • Yearly Growth and Regional Distribution: 2021 experienced a significant 324% growth compared to the previous year, with the West region contributing the largest share of sales at 30%, followed by the Northeast at 21%. The Midwest region trails with a 14% share. Notably, online sales accounted for 41% of the overall total, reflecting a sharp increase in digital commerce, with outlet stores following at 32%.

  • Pricing Insights: The highest-priced item, at $60, sold 1,275 units, while the lowest-priced item, at $7, saw a modest sale of 83 units, possibly reflecting lower popularity or smaller-sized items. The $30–$60 price range appears to be the optimal pricing zone, with robust sales volume observed. As prices increase beyond this range, a decline in unit sales is evident.

  • Average Monthly Units Sold: August recorded the highest average units sold at 302, with July and September close behind at 283 and 277 units, respectively. November, however, saw the lowest unit sales at 219, potentially reflecting seasonal demand for sports and streetwear during warmer months as customers prepare for outdoor activities.

  • Sales by Location: New York generates the highest sales at $60,000, followed by Florida at $41,000, while Maryland reports the lowest at $1,500. This geographical spread highlights regional demand variations across the retailer's markets.

Adidas Sales Dashboard

Recommendations

Based on the uncovered insights, the following recommendations have been provided :

  • Expanding Product Offerings: Given the strong performance of women’s apparel compared to athletic and street footwear, consider expanding the range of non-sport-specific items to further capture demand. In contrast, for men’s categories, focusing on athletic and street footwear could enhance sales and align with customer preferences.
  • Seasonal Product Alignment: Sales trends indicate higher demand during warmer months (June, July, and August) as compared to colder months (January, February, and March). To leverage these seasonal patterns, it would be beneficial to adjust product offerings accordingly, prioritizing warm-weather apparel during summer months and winter-specific items during colder months.
  • Incentives for In-Store Purchases: Offering tailored discounts and promotions for in-store purchases can drive foot traffic and increase sales within physical locations. For instance, implementing a program where online customers receive a voucher or discount on their next in-store purchase could encourage store visits. Additionally, offering seasonal items as complimentary incentives for meeting a purchase threshold may further stimulate in-store shopping.

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Analyzed Adidas' product sales performance, top retailers, monthly trends, yearly growth, regional distribution, and pricing insights. Performed ETL from Python (Pandas) to SQL Server, extracted data with SQL, and visualized key insights in Excel.

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