Experience Reviews Spider

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Introduction

The Experience Reviews Spider is a powerful Apify Actor designed to scrape and extract detailed reviews from Experience.com profiles. It automates the collection of customer feedback, ratings, and associated metadata, enabling businesses and researchers to gain valuable insights into public perceptions. By leveraging robust scraping techniques, this Actor delivers high-quality, structured data efficiently, saving time and effort in manual data gathering.

Features

• Comprehensive Review Extraction: Captures full review text, ratings, reviewer details, dates, and source information from Experience.com.
• Flexible URL Input: Supports multiple profile URLs for batch processing, allowing scalable data collection.
• Proxy Integration: Utilizes residential proxies to bypass restrictions and ensure reliable, undetected scraping.
• Item Limiting: Configurable limits to control the volume of extracted data, preventing overload and optimizing costs.
• Structured JSON Output: Produces clean, machine-readable data with unique identifiers for easy integration into databases or analytics tools.
• High Reliability: Built with error-handling mechanisms to manage dynamic web changes and ensure consistent performance.
• Fast Processing: Optimized for speed, processing reviews quickly while maintaining data accuracy.

Input Parameters

Example Usage

To run the Actor, provide input parameters in JSON format. Here's an example input:

{
"Urls":["https://www.experience.com/reviews/MsOceanside"],
"proxyConfiguration":{
"useApifyProxy":true,
"apifyProxyGroups":["RESIDENTIAL"]
},
"itemLimit":10
}

The Actor will output a JSON array of review objects. Example output:

[
{
"uuid":"3e944f2f-fa83-4cb0-87e8-361e34d298ce",
"review_detail_id":10635711,
"review_text":"Melissa is who you want if you\u2019re looking in the San Diego area!! Greatful to be her referral partner",
"rating":5,
"reviewer_first_name":"Makayla M",
"reviewer_last_name":" ",
"review_date":"Wed Dec 31 2025 21:58:30 GMT+0000 (Coordinated Universal Time)",
"source_name":"google",
"source_label":"Google",
"is_pinned":false,
"pinned_at":"",
"replies":[
"Owner: Thank you so much, Makayla, for the kind words and partnership! I\u2019m truly grateful to have you as a referral partner and appreciate your trust. If you ever need anything for clients looking in the San Diego area, please don\u2019t hesitate to reach out anytime. I\u2019m always happy to help!\n (Thu Jan 01 2026 04:37:13 GMT+0000 (Coordinated Universal Time))"
],
"review_origin_link":"https://search.google.com/local/writereview?placeid=ChIJN4MIFu3524ARXPNi_MQ9r9U",
"belongs_to_slug":"MsOceanside",
"belongs_to_name":"Ms. Oceanside Real Estate"
},
{
"uuid":"713caf57-a93b-4ef9-9118-9c7435aaf764",
"review_detail_id":10406848,
"review_text":"Melissa helped me sell my father\u2019s condo in San Diego through a sometimes challenging process. What I appreciated most was her persistence and her commitment to getting the sale done, even with several obstacles along the way. She stayed on top of the details, communicated frequently, and ultimately succeeded in selling the property while I was out of town. I\u2019m grateful for her hard work and determination throughout the process.",
"rating":5,
"reviewer_first_name":"Fausto",
"reviewer_last_name":"I",
"review_date":"Sun Nov 30 2025 01:22:50 GMT+0000 (Coordinated Universal Time)",
"source_name":"write_review_unverified",
"source_label":"Experience",
"is_pinned":false,
"pinned_at":"",
"replies":[],
"review_origin_link":null,
"belongs_to_slug":"MsOceanside",
"belongs_to_name":"Ms. Oceanside Real Estate"
},
{
"uuid":"95927976-6fc8-4f60-ae8e-47f3fe467a58",
"review_detail_id":10407099,
"review_text":"Melissa helped me sell my father\u2019s condo in San Diego through a sometimes challenging process. What I appreciated most was her persistence and her commitment to getting the sale done, even with several obstacles along the way. She stayed on top of the details, communicated frequently, and ultimately succeeded in selling the property while I was out of town. I\u2019m grateful for her hard work and determination throughout the process.",
"rating":5,
"reviewer_first_name":"Fausto I",
"reviewer_last_name":" ",
"review_date":"Sun Nov 30 2025 01:22:16 GMT+0000 (Coordinated Universal Time)",
"source_name":"google",
"source_label":"Google",
"is_pinned":false,
"pinned_at":"",
"replies":[
"Owner: Hi Fausto, thank you for the review it was a process that we were able to conquer. Thanks for the trust in me. (Sun Nov 30 2025 01:40:41 GMT+0000 (Coordinated Universal Time))"
],
"review_origin_link":"https://search.google.com/local/writereview?placeid=ChIJN4MIFu3524ARXPNi_MQ9r9U",
"belongs_to_slug":"MsOceanside",
"belongs_to_name":"Ms. Oceanside Real Estate"
}
]

Use Cases

• Market Research and Analysis: Gather customer feedback to analyze trends and sentiment in specific industries.
• Competitive Intelligence: Monitor reviews of competitors to identify strengths, weaknesses, and market positioning.
• Reputation Management: Track and respond to reviews for businesses to maintain a positive online presence.
• Content Aggregation: Collect reviews for content creation, such as blog posts or reports on consumer experiences.
• Academic Research: Extract data for studies on customer behavior, satisfaction, and review authenticity.
• Business Automation: Automate data collection for dashboards or CRM systems to inform decision-making.

Installation and Usage

1. Search for "Experience Reviews Spider" in the Apify Store
2. Click "Try for free" or "Run"
3. Configure input parameters
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 a JSON array of objects, each representing a single review. Key fields include:

• uuid: Unique identifier for the review.
• review_detail_id: Internal ID from the source.
• review_text: Full text of the review.
• rating: Numerical rating (e.g., 1-5).
• reviewer_first_name and reviewer_last_name: Reviewer's name details.
• review_date: Timestamp of the review in GMT.
• source_name and source_label: Origin of the review (e.g., Google).
• is_pinned and pinned_at: Indicates if the review is pinned and when.
• replies: Array of owner responses, if any.
• review_origin_link: URL to the original review source.
• belongs_to_slug and belongs_to_name: Profile identifier and name.

This structured format 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/

We're here to help you get the most out of this Actor!