What Glassdoor ratings do companies need to improve?

Posted on May 30, 2021

Introduction

Looking for the right job is tough, as well as for companies to find the right candidate. Companies that can offer different things to job seekers, but building trust between companies and job seekers is difficult. As job seekers dig deeper and deeper into companies, they might find some unpleasant things. More specifically, they might find negative reviews. 

Glassdoor is one of the world's largest job and recruiting sites with over 57 million unique monthly visits and nearly 50 million reviews from more than 1 million companies. Job seekers have come across Glassdoor at some point to look more into a company. In fact, about 1 in 3 job seekers say that they focus on reviews when going over job listings. (Based on a survey conducted by The Harris Poll on behalf of Glassdoor in May 2018).

Since reviews can have influence on job seekers, companies are potentially losing good candidates. My project is to focus on what Glassdoor ratings do companies need to improve, and furthermore, if they are able to be improved. For my project, I used a dataset with thousands of reviews from Glassdoor (Kaggle dataset). My goal is to see what issues companies need to focus on to improve hiring, and even employee retention. 

Data

The dataset contains reviews from 2008-2018, but I chose between 2013-2018, as previous years did not have "Culture/Values" ratings. Also, the ratings that we will focus on are:

  • Work/Life Balance
  • Culture/Values
  • Career Opportunities
  • Compensation/Benefits
  • Senior Management

Note: There are also "Overall Ratings", but we will talk about that more in the analysis.

 

To understand the meaning behind the scores for each rating, we will use a scale that Glassdoor provides (Glassdoor scale).

Finally, the companies that will be reviewed from the dataset are:

  • Amazon
  • Apple
  • Facebook
  • Google
  • Microsoft
  • Netflix
 

Quick things to note:

  • Only "Overall Reviews" are required when writing a review, so multiple scores were left blank in some reviews. I used the average score of each rating to fill in the black scores of that rating.
  • There is a question of authenticity with some reviews (WSJ article), so I have decided to select reviews that were marked as "Helpful" by at least 5 people.

Analysis

For my project, I wanted to focus on the 5 unique ratings mentioned earlier, but why not just the Overall Ratings? 

The "Overall Ratings" are important as they are the first rating that job seekers see when looking at a company, but job seekers tend to dig deeper and look at the individual reviews. Individual reviews contain scores for all ratings.

From the chart, the Overall Ratings indicate that employees are overall "OK" with their experiences in these companies, but this does not tell the whole story.

In this case, employees are "Dissatisfied" with the "Work/Life Balance" and "Senior Management" of these companies, while employees are nearly "Satisfied" with "Comp/Benefits". This tells us that these companies need to improve on "Work/Life Balance" and "Senior Management". 

But is this enough to confirm that these companies would need to improve on the same ratings? 

 

This chart shows that each company vary in average scores for each rating. As I mentioned earlier, "Work/Life Balance" and "Senior Management" are the lowest scores between all companies, but in Microsoft the lowest scores are "Senior Management" and "Culture/Values". Also, overall scores for all ratings are higher for some companies, like Facebook, while lower for companies, like Amazon.

As companies find out what ratings they need to improve on, is there even a possibility that they can improve over time?

While it maybe difficult to know what actions these companies took to influence ratings, but in this graph, ratings do change over time. Companies seem to improve on "Work/Life Balance" over time. From 2016-2018, "Senior Management" decreased significantly, and even fell below "Work/Life Balance".

Conclusion and Future Work

Overall, most of these companies need to improve on "Work/Life Balance" and "Senior Management", but it does vary. There is not just one rating that every company needs to improve on, but finding the right rating can be a great start. 

Companies can't change past reviews, but trying to find issues and improving on them, can result to better reviews, and more candidates/employee retention.

Being my first project, it was fun to look, clean, and analyze lots of data. I would love to get the most up to date reviews, especially from the past year to see how COVID-19 has affected reviews. Also, one of the issues with this dataset is that the companies involved were mainly Big Tech. I would love to see the differences or similarities in ratings between the small, mid, and large-sized companies.

Hope you enjoy this read, and can't wait to post more! 

About Author

Jesse Egoavil

Data Analyst with a passion to yield insights for business needs, create impactful, data-driven storytelling, and continually refine my technical skills.
View all posts by Jesse Egoavil >

Related Articles

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