Irene Widyastuti/

Optimizing Online Sports Retail Revenue


1. Counting missing values

Sports clothing and athleisure attire is a huge industry, worth approximately $193 billion in 2021 with a strong growth forecast over the next decade!

In this notebook, we play the role of a product analyst for an online sports clothing company. The company is specifically interested in how it can improve revenue. We will dive into product data such as pricing, reviews, descriptions, and ratings, as well as revenue and website traffic, to produce recommendations for its marketing and sales teams.

The database provided to us, sports, contains five tables, with product_id being the primary key for all of them:


columndata typedescription
product_namevarcharName of the product
product_idvarcharUnique ID for product
descriptionvarcharDescription of the product


columndata typedescription
product_idvarcharUnique ID for product
listing_pricefloatListing price for product
sale_pricefloatPrice of the product when on sale
discountfloatDiscount, as a decimal, applied to the sale price
revenuefloatAmount of revenue generated by each product, in US dollars


columndata typedescription
product_namevarcharName of the product
product_idvarcharUnique ID for product
ratingfloatProduct rating, scored from 1.0 to 5.0
reviewsfloatNumber of reviews for the product


columndata typedescription
product_idvarcharUnique ID for product
last_visitedtimestampDate and time the product was last viewed on the website


columndata typedescription
product_idvarcharUnique ID for product
brandvarcharBrand of the product

We will be dealing with missing data as well as numeric, string, and timestamp data types to draw insights about the products in the online store. Let's start by finding out how complete the data is.


COUNT(*) AS total_rows,
COUNT(description) AS count_description,
COUNT(listing_price) AS count_listing_price,
COUNT(last_visited) AS count_last_visited
FROM info
INNER JOIN finance
ON info.product_id=finance.product_id
INNER JOIN traffic
ON info.product_id=traffic.product_id;

2. Nike vs Adidas pricing

We can see the database contains 3,179 products in total. Of the columns we previewed, only one — last_visited — is missing more than five percent of its values. Now let's turn our attention to pricing.

How do the price points of Nike and Adidas products differ? Answering this question can help us build a picture of the company's stock range and customer market. We will run a query to produce a distribution of the listing_price and the count for each price, grouped by brand.


FROM finance
ON finance.product_id=brands.product_id
WHERE finance.listing_price > 0
GROUP BY brand, listing_price
ORDER BY listing_price DESC;

3. Labeling price ranges

It turns out there are 77 unique prices for the products in our database, which makes the output of our last query quite difficult to analyze.

Let's build on our previous query by assigning labels to different price ranges, grouping by brand and label. We will also include the total revenue for each price range and brand.


SUM(revenue) AS total_revenue,
WHEN listing_price < 42 THEN 'Budget'
WHEN listing_price >= 42 AND listing_price < 74 THEN 'Average'
WHEN listing_price >= 74 AND listing_price < 129 THEN 'Expensive'
WHEN listing_price > 129 THEN 'Elite'
END price_category
FROM finance
ON finance.product_id=brands.product_id
GROUP BY brand, price_category
ORDER BY total_revenue DESC;

4. Average discount by brand

Interestingly, grouping products by brand and price range allows us to see that Adidas items generate more total revenue regardless of price category! Specifically, "Elite" Adidas products priced \$129 or more typically generate the highest revenue, so the company can potentially increase revenue by shifting their stock to have a larger proportion of these products!

Note we have been looking at listing_price so far. The listing_price may not be the price that the product is ultimately sold for. To understand revenue better, let's take a look at the discount, which is the percent reduction in the listing_price when the product is actually sold. We would like to know whether there is a difference in the amount of discount offered between brands, as this could be influencing revenue.


AVG(discount)*100::decimal AS average_discount
FROM brands
FULL JOIN finance
ON brands.product_id=finance.product_id
WHERE brands.brand IS NOT NULL
GROUP BY brands.brand;

5. Correlation between revenue and reviews

Strangely, no discount is offered on Nike products! In comparison, not only do Adidas products generate the most revenue, but these products are also heavily discounted!

To improve revenue further, the company could try to reduce the amount of discount offered on Adidas products, and monitor sales volume to see if it remains stable. Alternatively, it could try offering a small discount on Nike products. This would reduce average revenue for these products, but may increase revenue overall if there is an increase in the volume of Nike products sold.

Now explore whether relationships exist between the columns in our database. We will check the strength and direction of a correlation between revenue and reviews.


corr(reviews, revenue) AS review_revenue_corr
FROM finance
INNER JOIN reviews

6. Ratings and reviews by product description length

Interestingly, there is a strong positive correlation between revenue and reviews. This means, potentially, if we can get more reviews on the company's website, it may increase sales of those items with a larger number of reviews.

Perhaps the length of a product's description might influence a product's rating and reviews — if so, the company can produce content guidelines for listing products on their website and test if this influences revenue. Let's check this out!


SELECT TRUNC(LENGTH(i.description), -2) AS description_length,
    ROUND(AVG(r.rating::numeric), 2) AS average_rating
FROM info AS i
INNER JOIN reviews AS r 
    ON i.product_id = r.product_id
WHERE i.description IS NOT NULL
GROUP BY description_length
ORDER BY description_length;

7. Reviews by month and brand

Unfortunately, there doesn't appear to be a clear pattern between the length of a product's description and its rating.

As we know a correlation exists between reviews and revenue, one approach the company could take is to run experiments with different sales processes encouraging more reviews from customers about their purchases, such as by offering a small discount on future purchases.

Let's take a look at the volume of reviews by month to see if there are any trends or gaps we can look to exploit.


EXTRACT(MONTH FROM last_visited) AS month,
count(*) AS num_reviews
FROM reviews AS r
INNER JOIN brands AS b
INNER JOIN traffic AS t
GROUP BY brand, month
ORDER BY brand, month

8. Footwear product performance

Looks like product reviews are highest in the first quarter of the calendar year, so there is scope to run experiments aiming to increase the volume of reviews in the other nine months!

So far, we have been primarily analyzing Adidas vs Nike products. Now, let's switch our attention to the type of products being sold. As there are no labels for product type, we will create a Common Table Expression (CTE) that filters description for keywords, then use the results to find out how much of the company's stock consists of footwear products and the median revenue generated by these items.

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