Capterra Reviews Scraper

Capterra holds thousands of first-hand reviews of business software, but copying them out one card at a time is slow and the page only loads more as you scroll. This scraper does the reading for you. Paste a Capterra product link and it returns each review as a clean row: the written text, the star rating, the breakdown scores, what the reviewer liked and disliked, and who they are. Point it at one product or run it across a shortlist of competitors.

What you get

Each review comes back as one row with the same shape every time, so missing pieces arrive as null rather than disappearing and your columns stay tidy in a sheet or database. Three groups of data per review:

• Ratings — overallScore, easeOfUseScore, supportScore, featureScore, valueScore, and recommendScore (out of 10)
• Review content — headline, reviewBody, prosText, consText, previousProduct, and datePosted
• Reviewer and source — productName, authorName, authorJobTitle, authorIndustry, usagePeriod, authorPhotoUrl, sourceUrl, and collectedAt

Quick start

1. Hit Try for free and open the input form.
2. Paste a Capterra product link into Capterra product page — the bare product URL or the /reviews/ URL both work.
3. Set a Review cap to decide how many reviews to pull.
4. Press Start, then export the results as JSON, CSV, Excel, or XML when it finishes.

Use cases

• Competitor research — pull reviews for two or three rival products and compare what users praise and complain about
• Product feedback — track your own listing, route fresh reviews to a spreadsheet or chat channel, and catch issues early
• Feature planning — mine the consText from low-scoring reviews to feed a backlog of real user requests
• Sentiment and NLP work — gather review text in bulk for scoring, tagging, or model training
• Win/loss analysis — read the previousProduct field to see which tools buyers leave behind
• Market research — measure how ratings and recommendation scores shift across a category over time

Input

Field	Type	Required	Description
reviewPageUrl	string	Yes	Capterra product or reviews page URL. Only capterra.com links are accepted; the scraper adds /reviews/ when it is missing. Example: https://www.capterra.com/p/186596/Slack/reviews/.
resultsLimit	integer	No	Most reviews to collect. About 25 per page, so 200 walks roughly 8 pages. Default 50; set a large value like 999999 to take everything.
timeoutSeconds	integer	No	Seconds to wait on each request before giving up. Default 45; raise it on a slow connection.

Example input

{
    "reviewPageUrl": "https://www.capterra.com/p/186596/Slack/reviews/",
    "resultsLimit": 50,
    "timeoutSeconds": 45
}

Output

Each review becomes one row, and every field is always present — anything Capterra did not show comes back as null so your dataset stays rectangular.

Example output

{
    "productName": "Slack",
    "authorName": "Maria G.",
    "authorJobTitle": "Operations Manager",
    "authorIndustry": "Marketing and Advertising",
    "usagePeriod": "2+ years",
    "authorPhotoUrl": "https://reviews.capterra.com/cdn/profile-images/linkedin/xyz789.jpeg",
    "headline": "Keeps our whole team in sync.",
    "datePosted": "March 12, 2026",
    "overallScore": 4.0,
    "easeOfUseScore": 5.0,
    "supportScore": 4.0,
    "featureScore": 4.0,
    "valueScore": 4.0,
    "recommendScore": 9.0,
    "reviewBody": "We switched our internal chat over to Slack and adoption was painless. Channels keep projects separate and the search makes old decisions easy to find.",
    "prosText": "Threaded conversations keep channels tidy and the integrations save us a lot of tab-switching.",
    "consText": "Notifications can get noisy on busy days and the free tier hides older messages.",
    "previousProduct": "Microsoft Teams",
    "sourceUrl": "https://www.capterra.com/p/186596/Slack/reviews/",
    "collectedAt": "2026-05-20T09:30:00+00:00"
}

Output fields

Field	Type	Description
productName	string	Name of the software product the review belongs to
authorName	string	Display name or initials of the reviewer
authorJobTitle	string	Role or job title the reviewer listed
authorIndustry	string	Sector or type of company the reviewer works in
usagePeriod	string	How long the reviewer says they have used the product
authorPhotoUrl	string	Link to the reviewer's avatar, or null when none was uploaded
headline	string	Short title the reviewer gave their review
datePosted	string	When the review went live on Capterra
overallScore	number	Headline star rating, out of 5
easeOfUseScore	number	Sub-score for ease of use, out of 5
supportScore	number	Sub-score for customer support, out of 5
featureScore	number	Sub-score for the feature set, out of 5
valueScore	number	Sub-score for value relative to price, out of 5
recommendScore	number	How strongly the reviewer would recommend, out of 10
reviewBody	string	The full written review
prosText	string	What the reviewer liked
consText	string	What the reviewer disliked
previousProduct	string	Product the reviewer switched from, when mentioned
sourceUrl	string	Address of the page the review came from
collectedAt	string	ISO 8601 timestamp of when the review was pulled

Tips for best results

• Start with a small cap. Run 25–50 reviews first to confirm the output fits your pipeline, then raise resultsLimit for the full batch.
• Either URL works. Paste the product page or the /reviews/ link — the scraper normalizes it for you, and non-Capterra links fail with a clear message.
• Big caps are safe. Setting resultsLimit above the real review count is fine; the run stops on its own when the pages run out.
• Short reviews skip pros and cons. When a reviewer left only a headline, prosText, consText, and previousProduct come back as null — that is expected, not an error.
• Raise timeoutSeconds to ~60 if a slow connection causes requests to drop.

How can I use Capterra review data?

How can I use the Capterra Reviews Scraper to compare competing software? Run the scraper on each competitor's product page and you get every review in the same structure — ratings, pros, cons, and recommendation scores side by side. Group the rows by productName to see which tool wins on ease of use, support, or value, and read the prosText and consText columns to learn why users prefer one over another.

How can I export Capterra reviews to a spreadsheet for analysis? Set a resultsLimit, run the actor, and export the dataset as CSV or Excel straight from Apify. Each review is one row with the full text, sub-scores, reviewer role, and industry, so you can pivot on authorIndustry, filter by overallScore, or chart how datePosted ratings trend over time without any manual copy-paste.

How can I track new Capterra reviews for my own product? Point the scraper at your listing and schedule it to run on a cadence. New reviews land as fresh rows you can route to a sheet or chat channel, and the consText from lower scores gives you a steady feed of real complaints to fold into your roadmap.

Is it legal to scrape data?

Our actors are ethical and do not extract any private user data, such as email addresses or private contact information. They only extract what the user has chosen to share publicly. We therefore believe that our actors, when used for ethical purposes by Apify users, are safe.

However, you should be aware that your results could contain personal data. Personal data is protected by the GDPR in the European Union and by other regulations around the world. You should not scrape personal data unless you have a legitimate reason to do so. If you're unsure whether your reason is legitimate, consult your lawyers.

You can also read Apify's blog post on the legality of web scraping.

Support

Questions, feature requests, or a field you'd like added? Reach out at data.apify@proton.me and we'll get back to you.