Gender in the Activewear Market

Posted on Mar 30, 2021

Github Repository


Most clothes and shoes aren't made unisex. In the activewear market, most companies produce a line of men's products and a separate line of women's products. These separate lines of clothes tend to be very similar looking. A potential activewear company, or a current, struggling activewear company may have questions like:

  • Should gender be a factor when it comes to product pricing?
  • Should gender be a factor when it comes to variety of products?

For guidance, a smart thing to do would be to look at highly successful activewear brands and analyze their strategies. Two of the top activewear brands, or arguably THE top two activewear brand, are Nike and Adidas.

Web Scraping the Products is a majorly successful American online shoe and clothing retailer. It is a major retailer for Adidas and Nike, and sells thousands of their products. Therefore, I scraped to gather data on products by Nike and Adidas.

I decided to analyze three activewear categories: sneakers, shorts, and short sleeve shirts. I specifically chose these three categories because I wanted gender to be the only independent variable in my study. In these three categories, the types of styles available seem to be similar for both genders, lending to the opportunity for a fair analysis of the influence of the specific factor of gender on activewear prices and variety.

When I scraped the price of each product, I made sure to scrape only the list price of each item and never the sale price. This way the analysis of prices would be fair.

I made sure not to include any fashion sneakers in the analysis, as Adidas does have a sub-division of sneakers that are more fashion focused than sports or exercise focused, whereas Nike does not. I didn't think my analysis comparing Nike and Adidas would be fair if I included products in this sub-division, as only one brand makes this type of shoe. Anyway, this sub-division was only a small percentage of Adidas sneakers being sold.

Observation and Analysis of Pricing for Each Brand

I decided to observe the price range and mean price for each gender in these three categories for the two brands. When looking at a specific brand, important questions are:

  • Does gender have an effect on mean price for all of the categories, or for any of the categories?
  • On average, is one gender's products more expensive than the other's for all of the categories?
  • Does gender have an effect on price range for all or any of the categories?
  • Is the price range for one gender larger than the others for all of the categories?

Below are box plots to visualize the data:

Below are bar plots to visualize the ratio of mean price, Men to Women: (The black line indicates where a 1:1 ratio would fall, indicative of no difference in average price between genders)

Taking a look at Nike, for sneakers, there is not a significant difference in price range between the gender subcategories. For short sleeve shirts and shorts, when excluding the outliers, the men subcategory has a significantly larger price range.

There is not a significant difference in average product price between the men and women subcategories for the sneakers category. However, the average product price for the shorts category is significantly higher for the men subcategory, and for the short sleeve shirts category, slightly significantly higher for the women subcategory.

Taking a look at Adidas, for sneakers, there is not a significant difference in price range between gender subcategories. For short sleeve shirts and shorts, the men subcategory has a significantly larger price range.

For short sleeve shirts, the average product price is significantly higher for the women subcategory. For sneakers and shorts, the average product price is significantly higher for the men subcategory.

Observation and Analysis of Variety of Options

It appears that both brands consistently provide a greater variety of options for men.

Which Analyses are Consistent Between Brands

After observing and analyzing both brands, important questions are:

  • Are the analyses the same for both brands?
  • What seems to be consistent between brands and what does not?

The analyses are not exactly the same for both brands. But there are multiple points that are consistent between both brands:

  1. There is a greater variety of options for men in every category.
  2. There isn't a significant different in price range for the sneakers category between the gender subcategories.
  3. There is a significant difference in price range for shorts and short sleeve shirts between the gender subcategories, that being that the men subcategory has a larger price range.
  4. For the shorts category, the mean price for the men subcategory is significantly higher than for the women subcategory.
  5. For the short sleeve shirts category, the mean price for the women subcategory is significantly higher than for the men subcategory (although for Nike the difference is quite small). 

Conclusion: What Another Company Can Take from This

It would only make sense for a potential or struggling activewear company to consider significant for itself points which are consistent between both brands. Points which are not consistent between brands shouldn't mean much to a potential or struggling company, because both Nike and Adidas are successful, and these points are not true for both of them.

A major point of analysis which is consistent both within a brand and between brands is that men always have more options than women do. A company can choose to look at this point in one of two ways: A company can use this point as a model to follow for their own brand, or it can go in the other direction and make itself attractive by advertising its uniqueness.

If a company chooses to follow the approach of Nike and Adidas, they can be confident that it is a safe approach, as Nike and Adidas are majorly successful. However, sometimes being different is what makes a company successful and attractive. So, if a company says we are different than the major brands because we produce an equal variety of options for both men and women, people, especially women, can be intrigued. People may also be more inclined to support a brand that advertises themselves as treating genders equally.

The other points that are consistent between brands can also provide insight for a potential or struggling company:

  • Sneakers should have a similar price range for both genders.
  • Men should be provided with a larger range of pricing options when it comes to shorts and short sleeve shirts.
  • On average men's shorts should be more expensive.
  • On average women's short sleeve shirts should be more expensive.

There are two ways to approach the last three points. These two ways were mentioned earlier. A company can choose to either follow these points of analysis when producing and pricing their products, or to go in the direction of providing and pricing equally for both genders and advertise itself as a brand that does this. Either approach is not more correct than the other. It's up to a company to decide which approach they believe would attract more customers and increase revenue.

Potential Future Work

I think it would be interesting to analyze categories of clothes other than activewear in which the men's products being sold are similar to the women's products being sold. It would be interesting to see if trends were consistent among brands within the categories.

About Author

Leave a Comment

No comments found.

View Posts by Categories

Our Recent Popular Posts

View Posts by Tags

#python #trainwithnycdsa 2019 airbnb Alex Baransky alumni Alumni Interview Alumni Reviews Alumni Spotlight alumni story Alumnus API Application artist aws beautiful soup Best Bootcamp Best Data Science 2019 Best Data Science Bootcamp Best Data Science Bootcamp 2020 Best Ranked Big Data Book Launch Book-Signing bootcamp Bootcamp Alumni Bootcamp Prep Bundles California Cancer Research capstone Career Career Day citibike clustering Coding Course Demo Course Report D3.js data Data Analyst data science Data Science Academy Data Science Bootcamp Data science jobs Data Science Reviews Data Scientist Data Scientist Jobs data visualization Deep Learning Demo Day Discount dplyr employer networking feature engineering Finance Financial Data Science Flask gbm Get Hired ggplot2 googleVis Hadoop higgs boson Hiring hiring partner events Hiring Partners Industry Experts Instructor Blog Instructor Interview Job Job Placement Jobs Jon Krohn JP Morgan Chase Kaggle Kickstarter lasso regression Lead Data Scienctist Lead Data Scientist leaflet linear regression Logistic Regression machine learning Maps matplotlib Medical Research Meet the team meetup Networking neural network Neural networks New Courses nlp NYC NYC Data Science nyc data science academy NYC Open Data NYCDSA NYCDSA Alumni Online Online Bootcamp Online Training Open Data painter pandas Part-time Portfolio Development prediction Prework Programming PwC python Python Data Analysis python machine learning python scrapy python web scraping python webscraping Python Workshop R R language R Programming R Shiny r studio R Visualization R Workshop R-bloggers random forest Ranking recommendation recommendation system regression Remote remote data science bootcamp Scrapy scrapy visualization seaborn Selenium sentiment analysis Shiny Shiny Dashboard Spark Special Special Summer Sports statistics streaming Student Interview Student Showcase SVM Switchup Tableau team TensorFlow Testimonial tf-idf Top Data Science Bootcamp twitter visualization web scraping Weekend Course What to expect word cloud word2vec XGBoost yelp