Introduction
In this project, I will be using SQL and Jupyter Notebook to analyze a sports database containing 5 tables that include product data such as pricing, reviews, descriptions, ratings, revenue, and website traffic. I am interested to see what recommendations can be provided to improve revenue.
Questions to consider:
How do the price points of Nike and Adidas products differ?
Is there a difference in the amount of discount offered by these brands?
Is there a correlation between revenue and reviews?
Is the majority of revenue coming from footwear products?
How does the revenue of clothing products compare to footwear products?
Key Insights:
Adidas items generate more total revenue regardless of price category than Nike!
No discount is offered on Nike products while Adidas products are discounted at an average rate of 33.4%.
There is a strong positive correlation of 0.651 between the revenue of a product and reviews of a product.
Product reviews are highest in the first quarter of the calender year!
Out of 3,117 total products, 2700 are in the footwear category generating a median revenue of over $3000 dollars.
Only 417 products are in the clothing category.
Database Schema
How complete is our data?
We are missing less than 5% of the values.
2. Nike vs Adidas Pricing!
It is very hard to analyze this query since the results include many unique prices. The solution is to group them in different price ranges to better analyze them.
As we can see, Adidas's revenue surpasses Nike's regardless of the price category.
The "Elite" category for Adidas generates the highest revenue for the company.
3. Average discount by brand
Nike offers no discounts while Adidas is discounted while having more revenue than Nike.
4. Is there a correlation between reviews of a product and revenue?
Surprisingly, there is a strong positive correlation between reviews of a product and revenue. This means more people are buying products that are generally reviewed more.
5. Reviews by month and brand!
Product reviews are highest at the beginning of the year but decrease as we get to the middle of the year.
6. Footwear Products vs Clothing Products!
As we know we have a total of 3,117 products. 2700 products are footwear products that generate a median revenue of over $3000.
417 products are clothing products that generate a median revenue of $503.
The majority of revenue comes from footwear products.
Recommendations to improve revenue:
The company can add more stock to its Adidas "Elite" price category since that is already the highest revenue-generating price point.
The company can try to offer a small discount on Nike products since they do not offer anything at the moment. This can increase overall product revenue.
The company can offer incentives to customers to leave a review for the product they purchased since there is a strong correlation between revenue and reviews.
Thank you for reading my analysis!
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