Gartner.com Reviews Scraper ## How it works This actor scrapes product reviews from Gartner Peer Insights (gartner.com/reviews). It intelligently parses Gartner review listing URLs with complex filters and constructs optimized API calls to retrieve structured review data. The actor supports advanced filter parsing including company size mappings, multi-value filters, and various sorting options. For each review, it optionally opens the review details page to enrich the dataset with additional fields. ## Features Multiple Inputs Supported: - Start from a Gartner reviews page URL with optional filters such as: - Basic URL: https://www.gartner.com/reviews/market/document-management/vendor/opentext/product/opentext-documentum-content-management/reviews - With filters: https://www.gartner.com/reviews/market/it-service-management-platforms/vendor/servicenow/product/servicenow-service-management/reviews?sort=-helpfulness&companySize=2|3|4|5|1&reviewRating=4|5 - Or specify product and category (market) via input parameters and the actor will construct the API URL. Enhanced URL Parser & API Builder: - Intelligently parses complex filter URLs with support for: - Company Size Filters: Automatically maps company size values (1-5) to include additional internal IDs - Multi-value Filters: Supports pipe (|) and comma (,) separated values for filters like companySize=2|3|4|5|1 - Review Rating Filters: Handles rating filters like reviewRating=4|5 - Partner Review Filters: Supports boolean partner review filtering - Industry & Function Filters: Parses job roles, industries, and deployment regions - Sort Parameters: Preserves sort preferences (-helpfulness, -review_date) - Constructs optimized API calls with proper parameter encoding and filter mapping Pagination and enrichment: - Fetches the initial page to get total counts, then requests all reviews. - Optionally visits each review page to extract extra details. ## How to Use 1. Set Up: Ensure you have an Apify account and access to the Apify platform. 2. Configure Input: Provide either a Gartner reviews page URL in startUrls or set product and category SEO names. 3. (Optional) Set maxItems to limit number of reviews. 4. Configure Proxy: Strongly recommended to improve reliability and avoid IP blocks. 5. Run the actor and export data (JSON/CSV) from the run dataset. ## Input Configuration Here are example inputs for scraping reviews: Basic Product Scraping: json { "startUrls": [ { "url": "https://www.gartner.com/reviews/market/observability-platforms/vendor/datadog/product/datadog/reviews" }, { "url": "https://www.gartner.com/reviews/market/it-service-management-platforms/vendor/servicenow/product/servicenow-service-management/reviews?sort=-helpfulness&companySize=2|3|4|5|1&reviewRating=4|5&partnerReview=false" } ], "maxItems": 100, "maxConcurrency": 10, "minConcurrency": 1, "maxRequestRetries": 3, "proxy": { "useApifyProxy": true, "apifyProxyGroups": ["RESIDENTIAL"], "apifyProxyCountry": "US" } } ### Supported Filter Parameters The actor automatically parses and handles these URL filter parameters: - companySize: Company size categories (1-5), automatically mapped to include additional internal IDs - reviewRating: Star ratings (1-5), supports multiple values like reviewRating=4|5 - partnerReview: Boolean filter for partner reviews (true/false) - industry: Industry category filters - jobRole: Job function filters (mapped to function parameter) - deploymentRegion: Geographic deployment filters - sort: Sort order (-helpfulness, -review_date) - months: Time range filters Filters support multiple values separated by pipe (|) or comma (,): companySize=2|3|4 or reviewRating=4,5 ## Output Structure The output dataset contains one item per review. Each item merges base review fields from the Gartner reviews API with additional fields parsed from the review detail page under reviewDetails. json { "reviewId": 6075242, "formattedReviewDate": "Mar 11, 2025", "reviewSourceCode": 4, "reviewIncentiveCode": 1, "productNames": "Grafana Cloud", "reviewRating": 5, "industryCd": 258, "industryName": "Construction", "companySizeCd": 9902, "companySize": "10B - 30B USD", "jobTitle": "Enterprise Architect", "reviewSummary": "giving insight into software and customer usage takes the confusion out of developer expectation and customer service expectation", "reviewHeadline": "The user experience isnt what you think it is. Trust the telemetry emissions", "upVotes": 0, "functionCd": 233, "function": "IT", "partnerReview": false, "productSeoNames": [ "grafana-cloud" ], "vendorSeoName": "grafana-labs", "sortValue": { "long": false, "double": true, "boolean": false, "any": false, "string": false, "null": false }, "reviewDetails": { "headline": "The user experience isnt what you think it is. Trust the telemetry emissions", "summary": "giving insight into software and customer usage takes the confusion out of developer expectation and customer service expectation", "submitDate": "Mar 11, 2025", "rating": 5, "source": "Invited by vendor (direct, user community)", "upvote": 0, "downvote": 0, "deploymentArchitecture": "Hybrid Cloud and On-premises", "partnerReview": false, "user": { "id": 367080855, "title": "Enterprise Architect", "industry": "Construction", "companySize": "10B - 30B USD", "function": "IT" }, "market": { "id": 21, "name": "Observability Platforms", "seoName": "observability-platforms" }, "vendor": { "id": 64540, "name": "Grafana Labs", "seoName": "grafana-labs" }, "products": [ { "id": 135988, "name": "Grafana Cloud", "seoName": "grafana-cloud", "subscribed": false } ], "sections": [ { "id": 2, "title": "Lessons Learned", "questions": [ { "key": "lessonslearned-like-most", "title": "What do you like most about the product or service?", "value": "the ease of use and the amount of data and intelligence. remove technical boundaries and allows people to view the products through an agreed upon abstraction", "type": "text" }, { "key": "lessonslearned-dislike-most", "title": "What do you dislike most about the product or service?", "value": "out of box experience is still difficult for some users not familiar", "type": "text" } ] }, { "id": 3, "slug": "evaluation-contracting", "title": "Evaluation & Contracting", "ratingKey": "evaluation-contracting-overall", "ratingValue": "3", "questions": [ { "key": "why-purchase-s24", "title": "Why did you purchase this product or service?", "value": [ "Cost management", "Create internal/operational efficiencies" ], "type": "checkboxes" }, { "key": "factors-drove-decision-s24", "title": "What were the key factors that drove your decision?", "value": [ "Product functionality and performance", "Overall cost" ], "type": "checkboxes" }, { "key": "vendors-considered", "title": "Which other vendors did you consider in your evaluation?", "value": [ "Honeycomb", "Logz.io", "LogicMonitor", "Oracle", "Datadog", "Elastic", "New Relic" ], "type": "checkboxes" } ] }, { "id": 4, "slug": "integration-deployment", "title": "Integration & Deployment", "ratingKey": "integration-deployment-overall", "ratingValue": "4" }, { "id": 5, "slug": "service-support", "title": "Service & Support", "ratingKey": "service-support-overall", "ratingValue": "5" }, { "id": 6, "slug": "product-capabilities", "title": "Product Capabilities", "ratingKey": "capabilities-overall", "ratingValue": "5" }, { "id": 7, "slug": "additional-context", "title": "Additional Context", "questions": [ { "key": "deployment-architecture-s24", "title": "Deployment architecture", "value": "Hybrid Cloud and On-premises", "type": "text" }, { "key": "deployment-country-multi", "title": "In which country(s) did your deployment take place?", "value": "United States", "type": "text" } ] } ], "reviewSourceCode": 4, "reviewIncentiveCode": 1, "snippetAvailable": true } } ### Property Descriptions - reviewId: Unique identifier of the review - formattedReviewDate: Human-readable review date (as displayed by Gartner) - reviewSourceCode / reviewIncentiveCode: Vendor invitation and incentive codes (numerical enums) - productNames: Product name string for the reviewed product(s) - reviewRating: Overall star rating (number) - industryCd / industryName: Industry code and label - companySizeCd / companySize: Company size code and label - jobTitle: Reviewer’s job title - reviewSummary: Short review summary text - reviewHeadline: Review headline text - upVotes: Number of upvotes on the review - functionCd / function: Reviewer function code and label (e.g., IT) - partnerReview: Whether the review is partner-related (boolean) - productSeoNames: Array of product SEO names - vendorSeoName: Vendor SEO name - sortValue: Internal sort metadata object (type flags such as long, double, boolean, etc.) ### reviewDetails Property Descriptions - headline / summary: Headline and summary of the review - submitDate: Human-readable submission date - rating: Overall rating (number) - source: Review source text (e.g., vendor-invited) - upvote / downvote: Vote counts inside details - deploymentArchitecture: Deployment type (e.g., "Hybrid Cloud and On-premises") - partnerReview: Whether marked as partner review (boolean) - user: Object with reviewer metadata - id: Reviewer ID - title: Job title - industry: Industry name - companySize: Company size label - function: Function label - market: Object with market metadata - id, name, seoName - vendor: Object with vendor metadata - id, name, seoName - products: Array of product objects - id, name, seoName, subscribed - sections: Array of section objects capturing Q&A and ratings - id, slug (when present), title - Optional ratingKey, ratingValue - Optional questions: Array of question objects with: - key: Stable question key - title: Display text - value: Answer (string or array of strings depending on type) - type: e.g., text, checkboxes - Additional flags: - reviewSourceCode, reviewIncentiveCode, snippetAvailable Note: Fields can vary across markets/products and may change as Gartner updates their API and page schema. ## Explore More Scrapers If you found this Zillow Cheerio Scraper useful, be sure to check out our other powerful scrapers and actors at memo23's Apify profile. We offer a wide range of tools to enhance your web scraping and automation needs across various platforms and use cases. ## Support - For issues or feature requests, please use the Issues section of this actor. - If you need customization or have questions, feel free to contact the author: - Author's website: https://muhamed-didovic.github.io/ - Email: muhamed.didovic@gmail.com ## Additional Services - Request customization or whole dataset: muhamed.didovic@gmail.com - If you need anything else scraped, or this actor customized, email: muhamed.didovic@gmail.com - For API services of this scraper (no Apify fee, just usage fee for the API), contact: muhamed.didovic@gmail.com