Capterra Software Reviews Scraper

Scrapes user reviews of any software product on Capterra — rating, pros, cons, reviewer job title and industry, likelihood to recommend. No login, no API key.

Useful for competitive research, product feedback analysis, and tracking sentiment on your own or a competitor's software listing.

What makes this different

Pros and cons come through as real text, not just a star rating. Capterra's structured data (schema.org markup) only carries the review title and rating — the actual pros/cons text and reviewer detail live in the surrounding page markup with no JSON equivalent. This Actor recovers them and merges everything into one row per review, so you get the reasoning behind the rating, not just the number.

Reviewer context, not just an anonymous score. Job title, industry, and how long they've used the product are attached to every review where Capterra shows them — useful for weighting reviews from your actual buyer persona differently from the rest.

A pagination trap most scrapers would never notice. Request a page past a product's real archive depth and Capterra doesn't error or come back empty — it silently re-serves page 1. This Actor tracks which reviews it has already emitted and stops the instant a page contributes nothing new, so you get a clean, deduplicated set instead of the same reviews repeated as if they were new ones.

The TLS gate is handled. Every Chrome or Safari fingerprint gets a bare 403 challenge from Capterra's edge; Firefox's fingerprint does not. This was found by testing six fingerprints, not guessed — the Actor uses the one that actually works.

Input

Field	Type	Notes
productPaths	array	The /p/{id}/{Slug} part of the product's Capterra URL.
maxItems	integer	Per product, 25/page. 0 walks the entire archive.
incremental	boolean	Only reviews not seen in previous runs.
proxyConfiguration	object	Not normally needed.

Finding the product path

From https://www.capterra.com/p/135003/Slack/, the path is p/135003/Slack. A wrong path fails clearly on the first page; a page number beyond a product's real review depth ends the run gracefully rather than erroring, since that's the normal, expected end of a smaller product's archive.

Output

{
  "product": "Slack",
  "reviewer_name": "Richard M.",
  "job_title": "Founder",
  "industry": "Marketing and Advertising",
  "tenure": "2+ years",
  "review_title": "Slack: Essential for Desk Based Workforces",
  "rating": 5,
  "likelihood_to_recommend": 10,
  "pros": "Slack is the best way for a desk based workforce to communicate...",
  "cons": "Honestly, nothing. Occasionally there will be a bug or issue...",
  "reviewed_at": "2026-07-25"
}

Each run also writes a RUN_COVERAGE record to the key-value store with what was requested, what came back, and any per-product failures — so a partial run is visible rather than silent.

Local development

pip install -r requirements.txt
python test_local.py p/135003/Slack --max 60 --out sample_output.json

sample_output.json is real output from a live three-page run.