This blog will follow my exploration of a dataset found on Kaggle that could help Sephora execute more sales. I have chosen to work with the Sephora Website Dataset and it contains 9 variables shown below:
In the dataset, each row is information about a single product that is listed on their website. Each product has information listed such as, category, size of the product, rating, number of reviews the product had, how many loves (favorites) it has, price point, value price (what the product is actually worth for discounted products), if there are marketing flags, and which marketing flags are used. There are a total of 9,169 observations recorded.
Throughout this semester I will be using this dataset to practice the skills I will be learning in class. This data will help me discover if Sephora’s prices strongly correlate with their corresponding ratings. I will also be analyzing this dataset to see if any of the other variables such as marketing flags and ratings have a significant impact on the number of loves (how many people have favorited the product). By looking at these variables it can potentially help Sephora price products better or completely take the product off their website. This discovery has the potential to guide and possibly influence customers shopping online at Sephora. I am looking to answer the question of what factors determine if a customer will “love” the product?
I chose this dataset because I like to shop for makeup, even though I will not necessarily buy it, I might add it to my cart or favorite something. It would be interesting to see if the reason I favorite a product is for the same reasons as someone else.
Thank you for joining me!
NicoleMISY262 