Capterra.com Reviews Scraper: Extract Software Reviews & Ratings Data

Understanding Capterra and the Value of Review Data

Capterra is one of the world's largest software review platforms, hosting millions of verified user reviews across virtually every software category. Unlike marketing materials or sales pitches, these reviews represent authentic user experiences, making them invaluable for understanding product strengths, weaknesses, and real-world performance.

The platform's review data reveals critical insights: which features users value most, common pain points, pricing perception, customer support quality, and how products compare to alternatives. For software vendors, this data identifies improvement areas and competitive advantages. For buyers, it provides unbiased decision-making intelligence. For market researchers, it maps the competitive landscape with unprecedented detail.

Manual review collection is impractical at scale. The Capterra Reviews Scraper automates this process, transforming thousands of reviews into structured, analyzable data within minutes.

What This Scraper Delivers

The Capterra Reviews Scraper extracts comprehensive review data from any software product's Capterra page. It captures not just ratings and text, but metadata like review dates, incentivization status, verified purchase indicators, and vendor responses. This complete dataset enables sophisticated sentiment analysis, trend tracking, and competitive benchmarking.

The scraper serves multiple audiences: software companies monitoring their reputation and competitors, market research firms analyzing industry trends, product managers identifying feature priorities, sales teams understanding objections, and investors conducting due diligence on software companies.

Input Configuration Explained

The scraper accepts Capterra review page URLs. These follow the pattern https://www.capterra.com/p/[product-id]/[product-name]/reviews/. You can target specific pages using the ?page=N parameter to control which reviews to scrape.

{
  "proxy": {
    "useApifyProxy": true,
    "apifyProxyGroups": [
        "RESIDENTIAL"
    ],
    "apifyProxyCountry": "US"
  },
  "max_items_per_url": 20,
  "ignore_url_failures": true,
  "urls": [
    "https://www.capterra.com/p/174285/Amazon-S3/reviews/?page=2"
  ]
}

Example Screenshot:

Key parameters:

• proxy: Controls proxy usage. Set to true if experiencing blocks or rate limiting.
• max_items_per_url: Limits reviews extracted per URL. Set higher (50-100) for comprehensive data or lower (10-20) for sampling.
• ignore_url_failures: When true, continues scraping if some URLs fail, useful for bulk operations.
• urls: Array of review page URLs. Add multiple URLs to scrape different products or pages simultaneously.

Output Fields and Their Applications

The scraper returns JSON data with 24 fields per review, each serving specific analytical purposes:

Core Identifiers:

• Review ID: Capterra's internal identifier for the review, essential for deduplication and tracking.
• Global Review ID: Universal identifier if the review appears on multiple Gartner platforms (Capterra's parent company).
• Source Site: Confirms the review originated from Capterra versus other Gartner properties.

Review Content:

• Title: Brief summary headline written by reviewer, often captures main sentiment.
• General Comments: Primary review text containing detailed user experience.
• Pros Text: Specific positive aspects, critical for identifying product strengths.
• Cons Text: Negative points and limitations, reveals improvement opportunities.
• Advice To Others: Recommendations to potential buyers, shows use case suitability.

Ratings Breakdown:

• Overall Rating: Composite score (1-5 stars), primary metric for comparison.
• Ease Of Use Rating: Interface and learning curve assessment.
• Functionality Rating: Feature completeness and capability evaluation.
• Value For Money Rating: Price-to-benefit perception, crucial for pricing strategy.
• Customer Support Rating: Service quality indicator.
• Recommendation Rating: Whether reviewer would recommend the product.

Context and Metadata:

• Written On: Review timestamp, enables temporal analysis and trend identification.
• Reviewer: Name and potentially company/role information (may be anonymized).
• Anonymity On: Boolean indicating if reviewer chose anonymity, affects credibility assessment.
• Incentivized: Flags if reviewer received compensation or incentive, important for bias consideration.

Competitive Intelligence:

• Alternative Products: Software options reviewer considered before choosing this product.
• Reasons For Choosing: Why reviewer selected this product over alternatives, reveals competitive advantages.
• Switched Products: What software the reviewer replaced, shows competitive displacement patterns.
• Reasons For Switching: Motivations for migration, identifies competitor weaknesses.

Vendor Engagement:

• Vendor Response: Official company reply to review, demonstrates customer service approach and responsiveness.
• Review Source: Indicates if review came from verified purchase or other validation method.

Example output:

[
  {
  "review_id": "Capterra___6545977",
  "title": "S3 is the gold standard for cloud object storage",
  "written_on": "October 18, 2024",
  "general_comments": "It's a great product that's always available and is the gold standard for object storage in the cloud. I don't have any complains.",
  "incentivized": "NoIncentive",
  "customer_support_rating": "3.0",
  "ease_of_use_rating": "5.0",
  "functionality_rating": "5.0",
  "value_for_money_rating": "5.0",
  "overall_rating": "5.0",
  "recommendation_rating": "10.0000000000",
  "cons_text": "I wish I could see a preview of objects stored in the buckets without having to download them",
  "pros_text": "It's simple, it works and it's cheap. Any time I need to store unorganised data my default is to put it in an S3 bucket. I love how it's the industry standard and any data analytics tools always have an s3 integration.",
  "source_site": "Capterra",
  "global_review_id": "Capterra___6545977",
  "anonymity_on": false,
  "advice_to_others": "",
  "chosen_reasons": "",
  "switching_reasons": "",
  "review_source": {
    "code": "NO",
    "tooltip": "No Incentive Offered: This review was submitted organically."
  },
  "reviewer": {
    "company_size": "11-50 employees",
    "industry": "Computer Software",
    "time_used_product": "2+ years",
    "full_name": "Sean M.",
    "job_title": "DevOps Engineer",
    "verified_linked_in": false,
    "profile_pic_url": null,
    "anonymity_on": false,
    "is_validated": true,
    "validations_passed": [
      "ProofOfLink"
    ]
  },
  "alternative_products": [],
  "switched_products": [],
  "vendor_response": {},
  "from_url": "https://www.capterra.com/p/174285/Amazon-S3/reviews/?page=2"
}
]

Using the Scraper Effectively

Start by identifying target products on Capterra. Navigate to their review sections and copy the URLs. For comprehensive analysis, include multiple page URLs to capture review volume (Capterra typically shows 20 reviews per page).

Configure max_items_per_url based on your needs: use 100+ for complete datasets, 20-50 for representative samples. Enable proxies if scraping extensively or if you encounter rate limiting.

Monitor the scraper's progress through Apify's console. Typical execution speed: 50-100 reviews in 2-3 minutes. Once complete, export data as JSON for analysis tools or CSV for spreadsheet review.

For error handling: if URLs fail, verify they're review pages (not product landing pages). Enable ignore_url_failures when scraping many products to prevent single failures from stopping the entire run.

Practical Applications

Competitive Intelligence: Track competitor ratings over time, analyze their review sentiment trends, identify features customers praise or criticize. Compare your product's ratings across dimensions (ease of use, support, value) against competitors.

Product Development: Mine "cons" and "advice to others" fields to build feature prioritization roadmaps. Identify frequently mentioned pain points requiring immediate attention. Track how feature requests evolve as your product matures.

Market Research: Analyze "reasons for switching" to understand competitive displacement patterns. Study "alternative products" to map the competitive landscape and identify emerging threats. Segment reviews by company size or industry when that metadata is available.

Reputation Management: Monitor your own product reviews daily, identify and respond to negative feedback promptly. Track sentiment changes following product updates or support improvements. Use vendor responses strategically to demonstrate commitment to customer success.

Sales Enablement: Extract common objections from negative reviews and develop counter-arguments. Identify winning themes in positive reviews to strengthen sales messaging. Use "reasons for choosing" data to understand your competitive differentiation in real buyer language.

Pricing Strategy: Analyze "value for money" ratings correlated with pricing tiers. Identify price sensitivity patterns across customer segments. Compare value perception against competitors at similar price points.

Best Practices for Sustainable Scraping

Implement regular collection schedules rather than one-time extractions. Weekly or bi-weekly scraping captures new reviews while building historical datasets for trend analysis. Store data with timestamps to enable time-series analysis of rating changes.

Combine Capterra data with reviews from G2, TrustRadius, and other platforms for comprehensive sentiment analysis. Cross-platform comparison reveals where your product performs consistently versus platform-specific issues.

Implement data quality validation: check for duplicate reviews, verify rating fields are within expected ranges (1-5), flag suspiciously similar review text that might indicate manipulation.

Use responsible scraping rates. While this scraper manages technical politeness, avoid overwhelming Capterra with hundreds of simultaneous requests. Batch large scraping jobs and space them appropriately.

Enrich scraped data with external context: map reviewer company names to firmographic data, categorize products into market segments, tag reviews with custom sentiment scores using NLP tools.

Conclusion

The Capterra Reviews Scraper transforms one of the world's largest software review repositories into actionable intelligence. Whether monitoring your product's reputation, analyzing competitors, conducting market research, or informing product strategy, this tool provides the structured data foundation for data-driven decision-making in the software industry.