Ambitionbox Reviews Scraper

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The Ambitionbox Reviews Scraper is a powerful Apify Actor designed to extract detailed employee reviews from Ambitionbox.com, providing valuable insights into company cultures, salaries, and work experiences. It automates the collection of authentic user-generated content, enabling businesses and researchers to analyze feedback at scale without manual effort. This tool is ideal for gaining competitive intelligence and understanding employee sentiments across various industries.

Features

• Comprehensive Data Extraction: Scrapes full review details including ratings, pros/cons, job profiles, and company responses for in-depth analysis.
• High Reliability: Built on Apify's robust CheerioCrawler, ensuring consistent performance and handling of dynamic web content.
• Customizable Crawling: Allows specification of start URLs and maximum requests to tailor scrapes to your needs.
• Structured JSON Output: Delivers clean, machine-readable data ready for integration into dashboards, databases, or analytics tools.
• Scalable and Efficient: Processes multiple pages quickly, supporting large-scale data collection with minimal resource overhead.
• Error-Resilient: Includes built-in retry mechanisms to handle network issues or site changes gracefully.
• Privacy-Compliant: Extracts publicly available data without violating terms of service or privacy laws.

Input Parameters

Example Usage

To run the scraper, provide input like this:

{
"startUrls":[
{
"url":"https://www.ambitionbox.com/reviews/amazon-seller-service-reviews"
}
],
"maxRequestsPerCrawl":100
}

Example output:

[
{
"id":75917298,
"companyName":"Amazon Sellers Services",
"companyId":316337,
"urlName":"amazon-seller-service",
"overallCompanyRating":2,
"userName":"Anonymous",
"jobProfile":"Area Manager",
"division":"eCommerce Operations",
"jobLocation":"Chennai",
"employmentType":"fulltime",
"currentJob":null,
"startDate":null,
"endDate":null,
"likesText":"Nothing",
"disLikesText":"Low scope of growth. High dirty politics, worse than Indian national politics.\nNo vision from managers and sr managers.\nNo hike for anyone from past 2 years, only shares which will be maturing in next 2 years.",
"workDescriptionText":null,
"benefitsText":null,
"ratingDistribution":[
{
"name":"Salary",
"rating":2
},
{
"name":"Skill development",
"rating":1
},
{
"name":"Job security",
"rating":1
},
{
"name":"Promotions",
"rating":1
},
{
"name":"Work satisfaction",
"rating":1
},
{
"name":"Company culture",
"rating":1
},
{
"name":"Work-life balance",
"rating":2
}
],
"workPolicy":"WFO",
"helpfulCount":0,
"notHelpfulCount":0,
"views":0,
"verified":false,
"continued":7,
"isFeaturedReview":false,
"isEmployerResponded":false,
"created":"2025-11-19 09:13:20",
"modified":"2025-11-19 09:16:43",
"modifiedHumanReadable":"19 Nov 2025",
"url":"/reviews/amazon-seller-service-reviews?rid=75917298",
"reviewerExperience":null
}
]

Use Cases

• Market Research: Analyze employee feedback to benchmark company reputations and identify industry trends.
• Competitive Intelligence: Gather insights on competitors' strengths and weaknesses from real employee experiences.
• HR Analytics: Assess job satisfaction, salary trends, and work-life balance to inform recruitment and retention strategies.
• Content Aggregation: Build databases of reviews for sentiment analysis or reporting in business intelligence tools.
• Academic Research: Collect data for studies on workplace culture, diversity, or economic factors in employment.
• Business Automation: Automate monitoring of reviews for alerts on emerging issues or positive mentions.

Installation and Usage

1. Search for "Ambitionbox Reviews Scraper" in the Apify Store.
2. Click "Try for free" or "Run".
3. Configure input parameters (e.g., start URLs and max requests).
4. Click "Start" to begin extraction.
5. Monitor progress in the log.
6. Export results in your preferred format (JSON, CSV, Excel).

Output Format

The output is an array of JSON objects, each representing a single review. Key fields include id (unique review identifier), companyName and overallCompanyRating (company details), userName and jobProfile (reviewer info), likesText and disLikesText (pros and cons), ratingDistribution (array of category ratings like Salary or Work-life balance), and metadata like created date and helpfulCount. This structure ensures easy parsing and integration.

Support

For custom/simplified outputs or bug reports, please contact:

• Email: support@getdataforme.com
• Subject line: "custom support"
• Contact form: https://getdataforme.com/contact/

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