Project: Analyzing Online Sports Revenue

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, you will undertake the role of a product analyst for an online sports clothing company. The company is specifically interested in how it can improve revenue. You 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.

You've been provided with four datasets to investigate:

brands.csv

Columns	Description
`product_id`	Unique product identifier
`brand`	Brand of the product

finance.csv

Columns	Description
`product_id`	Unique product identifier
`listing_price`	Original price of the product
`sale_price`	Discounted price of the product
`discount`	Discount off the listing price, as a decimal
`revenue`	Revenue generated by the product

info.csv

Columns	Description
`product_name`	Name of the product
`product_id`	Unique product identifier
`description`	Description of the product

reviews.csv

Columns	Description
`product_id`	Unique product identifier
`rating`	Average product rating
`reviews`	Number of reviews for the product

1. Reading in and formatting the data.

import pandas as pd

brands = pd.read_csv("brands.csv") 
finance = pd.read_csv("finance.csv")
info = pd.read_csv("info.csv")
reviews = pd.read_csv("reviews.csv")
#traffic = pd.read_csv("traffic.csv")

1.1 Formatting the datasets for analysis.

bran_finance = brands.merge(finance, on ='product_id')
fin_bran_info = bran_finance.merge(info,on ='product_id')
fin_bran_info_rev = fin_bran_info.merge(reviews , on ='product_id')

fin_bran_info_rev.dropna(inplace = True)

2. Sales performance of Adidas and Nike products

What is the volume of products and average revenue for Adidas and Nike products based on listing price quartiles?

Find the volume of products and associated revenue for Adidas and Nike products, split based on "listing_price" quartiles.

# (a) Creating a column in the DataFrame called price_label

fin_bran_info_rev['price_label'] = pd.qcut(fin_bran_info_rev['listing_price'], q=4, labels=['Budget', 'Average', 'Expensive', 'Elite'])

# Group the data by brand and price_label, and calculate the number of products and mean revenue
adidas_vs_nike = adidas_nike.groupby(['brand', 'price_label'], as_index=False).agg(num_products=('price_label', 'count'), mean_revenue=('revenue', 'mean')).round(2).reset_index(drop=True)

3. Finding the relationship between product description lengths, ratings, and reviews

Split product description length into bins and assign labels, before calculating the average rating and number of reviews for each range of description length.

Do any differences exist between the word count of a product's description and its mean rating?

3.1 Finding the length of each product description

# Find the largest description_length
max(fin_bran_info_rev["description"].str.len())

# Store the length of each description
fin_bran_info_rev["description_length"] = fin_bran_info_rev["description"].str.len()

# Upper description length limits
lengthes = [0, 100, 200, 300, 400, 500, 600, 700]

# Description length labels
labels = ["100", "200", "300", "400", "500", "600", "700"]

# Cut into bins
fin_bran_info_rev["description_length"] = pd.cut(fin_bran_info_rev["description_length"], 
bins=lengthes, labels=labels)

# Group by the bins
description_lengths = fin_bran_info_rev.groupby("description_length", as_index=False).agg(
    mean_rating=("rating", "mean"), 
    num_reviews=("reviews", "count")
).round(2)

4. Comparing footwear and clothing products.

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