Capterra Review Scraper ⭐

Extract verified software reviews from Capterra — ratings, reviewer metadata, pros/cons, detailed comments. Fast direct API scraping at scale.

---

⭐ What It Does

This tool pulls verified software reviews directly from Capterra without needing a browser. It grabs the full review picture — overall score, sub-ratings (ease of use, value for money, functionality, customer support), pros/cons, author details, and more — then outputs structured JSON ready for analysis, import, or integration.

---

👥 Who It's For

• Product teams — gather user feedback to prioritize roadmaps and fix pain points
• Marketers & brand managers — monitor sentiment across software categories
• Analysts & researchers — build training datasets, benchmark software vs. competitors
• Lead gen & sales teams — identify verified current-user reviews for social proof campaigns
• Software reviewers & bloggers — collect structured review data at scale

---

⭐ Data You Get

Field	Description
review_id	Unique Capterra review identifier
product_id, product_slug, product_url	Product the review belongs to, including in multi-product runs
title	Review headline
body	Full review text
rating	Overall star rating (1–5)
author_name	Reviewer display name
author_job_title	Their job role
author_company_size	Employee count bucket
industry_name	Industry sector
date_published	When the review was posted
is_validated	Verified Capterra user flag
verified_linkedin	LinkedIn profile verification status
time_used_product	How long the reviewer used the product
customer_support_rating	Sub-rating for support (1–5)
ease_of_use_rating	Sub-rating for ease of use (1–5)
functionality_rating	Sub-rating for features/functionality (1–5)
value_for_money_rating	Sub-rating for value for money (1–5)
recommendation_rating	Recommendation score (out of 10)
pros_text	Pros — what the reviewer liked
cons_text	Cons — what the reviewer disliked
incentivized	Incentive type (e.g. NominalGift)
source	Always capterra
page	Page number this review came from

---

💡 Use Cases

Product Research & Competitive Analysis

Compare Capterra ratings across competitor software. Spot common complaints to find gaps your product can fill, or lean into strengths you already have.

Sentiment Analysis at Scale

Feed clean, structured review text into NLP pipelines. Filter by industry, rating, or time period for targeted sentiment studies.

Lead Generation from Verified Reviews

Pull reviewer metadata (job title, company size, industry) to build warm B2B outbound lists from real, verified users.

B2B SaaS Benchmarking

Track sub-ratings (ease of use, value for money, customer support) over time to benchmark how your product stacks up against the market.

Review Aggregation for Comparison Pages

Populate product comparison pages or internal wikis with real, up-to-date Capterra reviews — without manual copy-pasting.

---

🚀 Quick Start

1. Go to apify.com/capterra-review-scraper and click Try it out

2. Enter a product slug (e.g. jira, slack), an ID/slug pair (135003/Slack), or a full Capterra URL (https://www.capterra.com/p/135003/Slack/reviews/). Full URLs avoid an extra product lookup.

3. Set Max Reviews (min 30) and choose a Sort Order, then click Start

4. Download results as JSON from the dataset output

That's it. No setup needed.

---

⚙️ Input Parameters

Parameter	Type	Required	Description
queries	array of strings	✅ Yes	Product slugs, ID/slug pairs, or full product/review URLs. Example: ["jira", "135003/Slack"].
max_reviews	integer	No	Maximum number of reviews to scrape per product (min30, max 1000). Default: 50. Each page returns ~20–25 reviews.
sort	string	No	Review sort order. Options: HIGHEST_COMPLETENESS_SCORE (default, most detailed reviews first) or MOST_RECENT (latest first).
proxyConfiguration	object	No	Defaults to Apify residential proxies. Supports country selection and custom proxy URLs. For local runs, set PROXY_URL when this input is omitted.

---

📝 Example

Input

{
  "queries": ["jira"],
  "max_reviews": 50,
  "sort": "MOST_RECENT"
}

Output

[
  {
    "source": "capterra",
    "review_id": "Capterra___7133000",
    "title": "Powerful tool for managing development tasks and team collaboration",
    "body": "Overall, my experience with Jira has been very positive...",
    "rating": 5.0,
    "author_name": "Kaynan S.",
    "author_job_title": "Senior Software Engineer",
    "author_company_size": "51-200 employees",
    "industry_name": "Entertainment",
    "date_published": "April 17, 2026",
    "is_validated": true,
    "verified_linkedin": true,
    "time_used_product": "2+ years",
    "customer_support_rating": 4.0,
    "ease_of_use_rating": 4.0,
    "functionality_rating": 5.0,
    "value_for_money_rating": 5.0,
    "recommendation_rating": 9.0,
    "pros_text": "What I like most about Jira is how powerful it is for managing tasks...",
    "cons_text": "One downside is that Jira can feel a bit complex, especially for new users...",
    "incentivized": "NominalGift",
    "page": 1
  }
]

---

❓ FAQ

Q: Do I need a proxy? A: The Actor uses Apify residential proxies by default. Capterra often blocks datacenter and direct connections.

Q: How is this different from a traditional web scraper? A: Most scrapers need a full browser to load pages — slower startup, more memory, and extra overhead. This tool calls Capterra's data endpoint directly, so it starts instantly and handles large scrape jobs without breaking a sweat.

Q: What does recommendation_rating mean? A: A 0–10 score measuring how strongly the reviewer recommends the product. It's separate from the 5-star overall rating and gives finer-grained signal for recommendation intent.

Q: Can I scrape multiple products in one run? A: Yes. Enter multiple product slugs in the queries array (e.g. ["jira", "slack", "zendesk"]). The scraper handles up to 3 concurrently per run.

Q: What does incentivized mean? A: Indicates the incentive status reported by Capterra (e.g. NominalGift or NoIncentive). An empty string means the site did not specify it.

---

⚡ Features

• Direct API access — fast, lightweight, reliable at scale
• Paginated scraping — pull up to 1,000 unique reviews per product
• Two sort options — highest completeness, most recent
• Verified reviewer data — LinkedIn verification status, job title, company size, industry, time-using-product
• Sub-category ratings — customer support, ease of use, functionality, value for money, recommendation score

Run quality and local testing

The SUMMARY output reports complete, partial, or failed, with the actual pages fetched and any per-product errors. Partial runs retain the reviews already collected; failed pages are reported rather than silently skipped. A product with fewer reviews than requested finishes normally when the source is exhausted.

Most Recent uses Capterra's cursor-based reviews action. If that action is unavailable, the run reports an error rather than returning the default order. Short default-sort runs use the review page data directly. Long text references in the response are decoded into the original review text.

Run regression checks with python -m unittest discover -s tests -v. For live local checks, provide PROXY_URL through your environment and run python local_traffic_test.py --queries jira --reviews 50 --sort MOST_RECENT. Both python main.py and python -m src.capterra use the same Actor entry point.