Sephora Data Analysis For Select ELC Brands

Posted on Nov 5, 2018
The skills we demoed here can be learned through taking Data Science with Machine Learning bootcamp with NYC Data Science Academy.

Sephora Data Analysis For Select ELC Brands

Background and Introduction

Estee Lauder Companies (ELC) is a high-end beauty company that was founded in 1946 and became a publicly traded corporation in 1995. Currently, data shows ELC has 30 beauty brands in its portfolio.

Since going public, ELC has heavily focused on growing their brand portfolio through several acquisitions. However, ELC is still focused on maintaining and growing legacy brands (i.e., brands that were part of the portfolio prior to going public), as they are vital to the corporation.

For the purposes of this project, I made the assumption that ELC leadership would like to gain insight into candid customer feedback for ELC’s legacy brands in order to track and maintain brand integrity. The brands in focus for this analysis include:

  • Estee Lauder
  • Origins
  • Clinique
  • La Mer
  • Bobbi Brown

To gather candid customer feedback, I performed web scraping of using Selenium and Python.

Sephora Data Analysis For Select ELC Brands

Data Gathering and Analysis

From, I gathered the following information for each of the products aligned to the five legacy brands listed in the last section:

  • Β Brand
  • Name
  • Price
  • Loves
  • Type
  • Reviews Complete
  • Rating

In total, information aligned to 517 products across the five legacy brands was gathered.

Sephora Data Analysis For Select ELC Brands

The main analysis was focused on the distribution of product rating vs. product price for each brand. Overall, it was clear that each brand had a distinct price range and generally consistent review ratings for their products. La Mer had the highest average product rating and smallest distribution range for product rating. However, La Mer also had a significantly higher average price point per product. In contrast, Origins had the lowest average product rating and lowest average price point per product. This shows that higher prices are loosely correlated to higher product ratings.


To see additional information regarding my analysis and web scraping code, please visit my Github project.

About Author

Leave a Comment

No comments found.

View Posts by Categories

Our Recent Popular Posts

View Posts by Tags

#python #trainwithnycdsa 2019 2020 Revenue 3-points agriculture air quality airbnb airline alcohol Alex Baransky algorithm alumni Alumni Interview Alumni Reviews Alumni Spotlight alumni story Alumnus ames dataset ames housing dataset apartment rent API Application artist aws bank loans 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 boston safety Bundles cake recipe California Cancer Research capstone car price Career Career Day citibike classic cars classpass clustering Coding Course Demo Course Report covid 19 credit credit card crime frequency crops D3.js data data analysis Data Analyst data analytics data for tripadvisor reviews data science Data Science Academy Data Science Bootcamp Data science jobs Data Science Reviews Data Scientist Data Scientist Jobs data visualization database Deep Learning Demo Day Discount disney dplyr drug data e-commerce economy employee employee burnout employer networking environment feature engineering Finance Financial Data Science fitness studio Flask flight delay gbm Get Hired ggplot2 googleVis H20 Hadoop hallmark holiday movie happiness healthcare frauds higgs boson Hiring hiring partner events Hiring Partners hotels housing housing data housing predictions housing price hy-vee Income Industry Experts Injuries Instructor Blog Instructor Interview insurance italki Job Job Placement Jobs Jon Krohn JP Morgan Chase Kaggle Kickstarter las vegas airport lasso regression Lead Data Scienctist Lead Data Scientist leaflet league linear regression Logistic Regression machine learning Maps market matplotlib Medical Research Meet the team meetup methal health miami beach movie music Napoli NBA netflix Networking neural network Neural networks New Courses NHL nlp NYC NYC Data Science nyc data science academy NYC Open Data nyc property NYCDSA NYCDSA Alumni Online Online Bootcamp Online Training Open Data painter pandas Part-time performance phoenix pollutants Portfolio Development precision measurement prediction Prework Programming public safety PwC python Python Data Analysis python machine learning python scrapy python web scraping python webscraping Python Workshop R R Data Analysis 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 seafood type Selenium sentiment analysis sentiment classification Shiny Shiny Dashboard Spark Special Special Summer Sports statistics streaming Student Interview Student Showcase SVM Switchup Tableau teachers team team performance TensorFlow Testimonial tf-idf Top Data Science Bootcamp Top manufacturing companies Transfers tweets twitter videos visualization wallstreet wallstreetbets web scraping Weekend Course What to expect whiskey whiskeyadvocate wildfire word cloud word2vec XGBoost yelp youtube trending ZORI