Glassdoor Reviews Scraper

Glassdoor Reviews Scraper

Glassdoor Reviews Scraper is an Apify actor that extracts structured employee reviews from Glassdoor company profile pages — including ratings, pros/cons, job titles, locations, and timestamps — so you can analyze sentiment and build a Glassdoor reviews dataset at scale. It solves the pain of manual copy-paste by providing a reliable glassdoor reviews scraping service and a Glassdoor reviews API alternative that’s ready for marketers, developers, data analysts, and researchers. With batch processing and robust anti-blocking, you can scrape glassdoor reviews across multiple companies for HR analytics, competitive research, and more. 🚀

What data / output can you get?

The actor saves individual review records to the Dataset and also groups results by company in the Key-Value Store. Below are key fields captured (real field names as in the output):

Data field	Description	Example value
review_id	Unique review identifier	4852131
summary	Review summary/title	"Advisory Engineer in STG, IBM"
pros	Pros section of the review	"Great colleagues, flexible policies..."
cons	Cons section of the review	"Rating system issues..."
advice	Advice to management	"Management must keep in mind..."
rating_overall	Overall rating (1–5)	4
review_date_time	Review date (ISO)	"2014-08-26T09:02:30.030"
is_current_job	Whether reviewer is a current employee	true
length_of_employment	Years of employment	9
job_title	Reviewer’s job title	"Advisory Engineer"
location	Reviewer’s location	"Hopewell Junction, NY"
employer_short_name	Company short name	"IBM"
employer_logo_url	Company logo URL	"https://media.glassdoor.com/..."
count_helpful	Helpful votes count	12
has_employer_response	Whether the company responded	true
rating_recommend_to_friend	Recommend-to-friend sentiment	"RECOMMEND"
rating_ceo	CEO approval sentiment	"APPROVE"
rating_business_outlook	Business outlook sentiment	"POSITIVE"
rating_career_opportunities	Career opportunities rating (0–5)	5.0
rating_compensation_and_benefits	Compensation rating (0–5)	3.0
rating_culture_and_values	Culture rating (0–5)	4
rating_diversity_and_inclusion	Diversity rating (0–5)	4
rating_senior_leadership	Senior leadership rating (0–5)	3.5
rating_work_life_balance	Work–life balance rating (0–5)	4.0
company_id	Glassdoor company ID (from URL)	354
company_url	Original profile URL	"https://www.glassdoor.com/Overview/Working-at-IBM-EI_IE354.11,14.htm"
reviews_count	Running total collected for the company	120

Notes:

• Bonus metadata includes employer_id, employer_short_name, employer_logo_url, and featured flag.
• Export your Glassdoor reviews dataset as JSON, CSV, or Excel directly from the Apify Dataset.

Key features

• 🔁 Smart proxy fallback & rotation — Automatically escalates from direct → datacenter → residential proxies with detailed logging and sticky residential when needed, reducing blocks for reliable Glassdoor reviews data extraction.
• 🧩 Comprehensive review fields — Captures ratings (overall and category-specific), text (pros/cons/advice), reviewer metadata (job title, location, tenure), helpful counts, and employer response flags for deep analysis.
• 📦 Multi-company runs — Add multiple Glassdoor company profile URLs to extract glassdoor company reviews in a single workflow for research at scale.
• 💾 Real-time dataset saves — Each page of reviews is pushed instantly to the Dataset so partial results persist even if a run is interrupted.
• 🧭 Structured outputs — Get individual review rows in the Dataset and grouped results by company in the Key-Value Store (including an aggregated OUTPUT key).
• 🛡️ Anti-blocking measures — Randomized delays and realistic headers (including CSRF tokens) help avoid rate limiting while scraping glassdoor reviews without API access.
• 🛠️ Developer-friendly — Access results programmatically via the Apify API, or connect this glassdoor review scraper tool to your Python pipelines and BI workflows.
• 📈 Production-ready — Includes retry logic, error handling, and clear logs. Ideal for building a glassdoor reviews crawler into automated pipelines.

How to use Glassdoor Reviews Scraper - step by step

1. Sign in to Apify Console.
2. Navigate to Actors and search for “glassdoor-reviews-scraper”.
3. Open the actor and configure inputs:
   • startItems: Add one or more Glassdoor company profile URLs (e.g., https://www.glassdoor.com/Overview/Working-at-IBM-EI_IE354.11,14.htm).
   • maxResults: Set how many reviews to collect per company (1–1000; default 50).
   • proxyConfiguration: Optionally enable Apify Proxy; if left as default, the actor will auto-fallback to datacenter then residential as needed.
4. Click Start to run. The actor will process each company URL and fetch reviews page by page.
5. Monitor logs to see proxy mode usage, per-page collection counts, and overall progress.
6. Access results:
   • Dataset tab for individual review records (one row per review).
   • Key-Value Store for grouped results (per-company entries and an aggregated OUTPUT).
7. Export your data as JSON, CSV, or Excel from the Dataset UI.
8. Optional: Use the Apify API to automate runs and fetch datasets programmatically.

Pro tip: Use this Glassdoor reviews API alternative in your data pipelines — schedule runs, pull fresh datasets via API, and feed them into sentiment analysis or HR analytics dashboards.

Use cases

Use case	Description
Market research & competitor benchmarking	Compare glassdoor ratings and reviews across competitors to quantify culture, compensation, and outlook trends.
HR analytics & employer branding	Analyze pros/cons, work–life balance, and leadership ratings to inform talent strategy and improve employer brand.
Investment due diligence	Incorporate employee sentiment signals into risk and reputation assessments across target portfolios.
Recruitment intelligence	Extract glassdoor company reviews to learn what candidates value and where friction points exist.
Academic research	Build reproducible datasets for studies on workplace satisfaction, diversity & inclusion, and organizational culture.
Brand monitoring	Track changes in sentiment over time and identify recurring pain points to prioritize internal initiatives.
Data engineering pipeline	Use as a glassdoor reviews API alternative within ETL/ELT jobs to download glassdoor reviews into your data warehouse.

Why choose Glassdoor Reviews Scraper?

This solution focuses on reliable, structured Glassdoor reviews data extraction — built for accuracy, resilience, and automation.

• 🎯 Precise extraction: Structured review fields map cleanly to analytics (ratings, metadata, timestamps, and more).
• 🧠 Anti-blocking built-in: Smart proxy fallback with sticky residential minimizes disruptions during crawling.
• 📚 Multi-company workflows: Process multiple company URLs in one run for faster insights.
• 💻 Developer access: Fetch datasets via the Apify API and integrate with your glassdoor reviews scraper Python workflows.
• 🔒 Public data focus: Targets publicly available Glassdoor company review pages only.
• 💸 Efficient operations: Automatic pagination, real-time saves, and stability reduce re-runs and manual effort.
• 🔌 Integration-ready: Export JSON/CSV/Excel or connect the glassdoor reviews crawler to automation tools and BI.

Bottom line: It’s a dependable Glassdoor employer review scraper built to outperform brittle browser extensions and one-off scripts.

Is it legal / ethical to use Glassdoor Reviews Scraper?

Yes, when used responsibly. This actor collects data from publicly available Glassdoor company review pages and does not access private or password-protected content.

Guidelines for compliant use:

• Scrape only public pages and respect Glassdoor’s Terms of Service.
• Follow data protection laws and policies (e.g., GDPR, CCPA).
• Use the data responsibly (e.g., analytics, research), not for spam.
• Implement reasonable request rates and avoid excessive load on Glassdoor.
• Consult your legal team for specific edge cases or jurisdiction-specific requirements.

Input parameters & output format

JSON input example

{
  "startItems": [
    "https://www.glassdoor.com/Overview/Working-at-IBM-EI_IE354.11,14.htm",
    "https://www.glassdoor.com/Overview/Working-at-Google-EI_IE9079.11,17.htm"
  ],
  "proxyConfiguration": {
    "useApifyProxy": false
  },
  "maxResults": 50
}

Input fields

• startItems (array, required): List of Glassdoor company URLs (e.g., https://www.glassdoor.com/Overview/Working-at-IBM-EI_IE354.11,14.htm), company names, or keywords. Default: none.
• maxResults (integer, optional): Maximum number of reviews to collect per company (1–1000). Default: 50.
• proxyConfiguration (object, optional): Choose proxies. Actor starts with no proxy, then datacenter, then residential if blocked. Sticks with residential after fallback. Default: { "useApifyProxy": false }.

Output format

The actor saves:

• Dataset: individual review records (one row per review) with company metadata.
• Key-Value Store:
  • Per-company keys: company-<company_id> with grouped data.
  • OUTPUT key: aggregated object keyed by company_id (as strings), each containing company_url, reviews_count, and reviews[].

Dataset example (single review record):

{
  "company_id": 354,
  "company_url": "https://www.glassdoor.com/Overview/Working-at-IBM-EI_IE354.11,14.htm",
  "reviews_count": 120,
  "review_id": 4852131,
  "summary": "Advisory Engineer in STG, IBM",
  "pros": "Great colleagues, flexible policies...",
  "cons": "Rating system issues...",
  "advice": "Management must keep in mind...",
  "rating_overall": 4,
  "rating_recommend_to_friend": "RECOMMEND",
  "rating_ceo": "APPROVE",
  "rating_business_outlook": "POSITIVE",
  "rating_career_opportunities": 5.0,
  "rating_compensation_and_benefits": 3.0,
  "rating_culture_and_values": 4,
  "rating_diversity_and_inclusion": 4,
  "rating_senior_leadership": 3.5,
  "rating_work_life_balance": 4.0,
  "review_date_time": "2014-08-26T09:02:30.030",
  "is_current_job": true,
  "length_of_employment": 9,
  "job_title": "Advisory Engineer",
  "location": "Hopewell Junction, NY",
  "employer_id": 354,
  "employer_short_name": "IBM",
  "employer_logo_url": "https://media.glassdoor.com/...",
  "count_helpful": 12,
  "has_employer_response": true,
  "featured": false
}

Key-Value Store example (aggregated OUTPUT):

{
  "354": {
    "company_url": "https://www.glassdoor.com/Overview/Working-at-IBM-EI_IE354.11,14.htm",
    "reviews_count": 120,
    "reviews": [
      {
        "review_id": 4852131,
        "summary": "Advisory Engineer in STG, IBM",
        "pros": "Great colleagues, flexible policies...",
        "cons": "Rating system issues...",
        "advice": "Management must keep in mind...",
        "rating_overall": 4,
        "rating_recommend_to_friend": "RECOMMEND",
        "rating_ceo": "APPROVE",
        "rating_business_outlook": "POSITIVE",
        "rating_career_opportunities": 5.0,
        "rating_compensation_and_benefits": 3.0,
        "rating_culture_and_values": 4,
        "rating_diversity_and_inclusion": 4,
        "rating_senior_leadership": 3.5,
        "rating_work_life_balance": 4.0,
        "review_date_time": "2014-08-26T09:02:30.030",
        "is_current_job": true,
        "length_of_employment": 9,
        "job_title": "Advisory Engineer",
        "location": "Hopewell Junction, NY",
        "employer_id": 354,
        "employer_short_name": "IBM",
        "employer_logo_url": "https://media.glassdoor.com/...",
        "count_helpful": 12,
        "has_employer_response": true,
        "featured": false
      }
    ]
  }
}

Note: The actor also stores per-company grouped data under keys like company-354 in the Key-Value Store.

FAQ

How many reviews can I scrape per company?

You can scrape between 1 and 1,000 reviews per company using the maxResults parameter. The default is 50.

Can I scrape multiple companies in one run?

Yes. Add multiple Glassdoor company profile URLs to startItems and the actor will process each URL in the same run.

Do I need to log in to Glassdoor?

No. The Glassdoor Reviews Scraper targets publicly available company review pages and does not require login or private cookies.

What happens if requests are blocked?

The actor uses an intelligent proxy fallback: it starts direct, falls back to a datacenter proxy if blocked, and then to residential proxies with retries. When residential succeeds, it sticks with it to maximize success.

What formats can I download the results in?

You can export the Glassdoor reviews dataset from the Apify Dataset as JSON, CSV, or Excel. Grouped results are saved to the Key-Value Store (including the OUTPUT key).

Is this a Glassdoor reviews API?

It’s a Glassdoor reviews API alternative. You run the actor on Apify and fetch results via the Apify API, making it easy to integrate with Python scripts, ETL jobs, and dashboards.

Which data fields are included in each review?

Each record includes identifiers, text fields (summary/pros/cons/advice), category ratings, sentiments (recommend to friend, CEO approval, business outlook), reviewer metadata (job title, location, employment length, current/previous flag), timestamps, helpful counts, employer metadata, and a featured flag. See the Output section for examples.

How is the data organized in storage?

Individual reviews are pushed to the Dataset (one review per item). Grouped data is saved in the Key-Value Store per company (company-

How do I set up proxies?

Use proxyConfiguration. By default, it starts with no proxy and automatically falls back to datacenter, then residential if blocked. You can also enable Apify Proxy from the start.

What about pricing or free trials?

You can run the actor on Apify and access current pricing or trial information on the actor’s Apify Store listing. Availability may vary; check the listing for the latest details.

Closing CTA / Final thoughts

Glassdoor Reviews Scraper is built to extract glassdoor employee reviews and ratings at scale with structured, analysis-ready output. With smart proxy fallback, real-time dataset saves, and grouped outputs, it’s ideal for marketers, developers, analysts, and researchers who need a dependable glassdoor company reviews scraper.

Start with company profile URLs, set your maxResults, and export your Glassdoor reviews dataset as JSON/CSV/Excel. Developers can automate via the Apify API or connect this Glassdoor reviews crawler to Python workflows and BI pipelines. Start extracting smarter insights from employee feedback today.

Need a custom workflow or new features? Contact: dev.scraperengine@gmail.com