Thirdwatch

The Complete Guide to Scraping Reviews & Ratings (2026)

Published April 27, 2026 · 1406 words · For researchers

Thirdwatch publishes 6 review-platform scrapers covering Trustpilot, G2, Capterra, AmbitionBox, Glassdoor, and Shopify Reviews. This guide is the decision tree for picking the right one (or combination) for your use case — brand monitoring, competitive research, employer-brand drift, B2B SaaS positioning, M&A diligence.

The reviews-scraping landscape

Review platform coverage is fragmented by audience type. According to G2's 2024 Buyer Behavior report, 86% of B2B buyers reference review platforms during purchase decisions. For brand-monitoring, competitive research, and reputation management, multi-platform coverage is essential — different platforms attract different reviewer cohorts.

For a reputation-monitoring team, the right answer is usually 2-3 review platforms covering both consumer and B2B-buyer perspectives. For competitive-research at scale, 4-5 to capture the full review-platform footprint of competitor brands.

Compare Thirdwatch reviews scrapers

Scraper Coverage Approach Cost/1K Best for
Trustpilot Consumer-side global HTTP + Next.js $2 Consumer brand monitoring
G2 B2B SaaS dominant Camoufox + residential $8 B2B SaaS competitive
Capterra B2B SaaS (Gartner) Camoufox + cookie pool $8 SMB SaaS research
AmbitionBox India employer reviews impit + Next.js data $6 India HR research
Glassdoor Global employer reviews Playwright + residential $8 Global employer brand
Shopify Reviews D2C product reviews HTTP $2 Product-quality signal

Decision tree

"I'm tracking brand reputation across consumer touchpoints." Trustpilot (canonical consumer-side, 50K+ verified brands) + Google Maps reviews (local-business perspective). Add Shopify Reviews if your brand sells through Shopify storefronts.

"I'm researching B2B SaaS competitors." G2 (canonical, primary) + Capterra (catches smaller vendors). For comprehensive coverage, run both — there's only ~50% overlap. Trustpilot adds consumer-side perspective for SaaS with B2C exposure.

"I'm monitoring employer brand." Glassdoor (global) + AmbitionBox (India). Daily snapshot, alert on 0.3-star rating drops with 20+ new reviews (real signal vs noise).

"I'm doing M&A diligence on a target's brand." All platforms covering target's audience surfaces. Trustpilot for B2C, G2/Capterra for B2B SaaS, Glassdoor for employer brand. Snapshot weekly during diligence window + retrieve historical rating trajectory.

"I'm building a review-aggregation product." Trustpilot + G2 + Capterra + Glassdoor at minimum. Compute cross-platform aggregate rating per brand (median across platforms is more stable than any single platform).

"I'm a D2C founder studying competitor product reviews." Shopify Reviews (where peer D2C brands' reviews live) + Trustpilot (where consumer complaints surface) + Amazon reviews (where mass-market shops). Cross-tab review themes for content-strategy insight.

Cross-platform recipe: brand reputation aggregation

import os, requests, pandas as pd

TOKEN = os.environ["APIFY_TOKEN"]

def run(actor, payload, timeout=3600):
    r = requests.post(
        f"https://api.apify.com/v2/acts/{actor}/run-sync-get-dataset-items",
        params={"token": TOKEN}, json=payload, timeout=timeout
    )
    return r.json()

BRANDS = ["stripe", "adyen", "checkout-com"]

trust = run("thirdwatch~trustpilot-reviews-scraper",
            {"queries": BRANDS, "maxResults": 100})
g2 = run("thirdwatch~g2-software-reviews-scraper",
         {"queries": BRANDS, "maxResults": 100})

trust_df = pd.DataFrame(trust).assign(platform="trustpilot")
g2_df = pd.DataFrame(g2).assign(platform="g2")

combined = pd.concat([trust_df, g2_df], ignore_index=True)

per_brand = (
    combined.groupby(["company_name", "platform"])
    .agg(
        median_rating=("rating", "median"),
        review_count=("id", "count"),
    )
    .reset_index()
    .pivot_table(index="company_name", columns="platform",
                 values=["median_rating", "review_count"])
)
print(per_brand)

Cross-platform rating differences highlight where each brand is winning vs losing — Stripe might be 4.7 on G2 (B2B technical buyers) but 3.9 on Trustpilot (end-user merchants).

All use-case guides for review scrapers

Glassdoor

AmbitionBox

JustDial Reviews (local business)

(Trustpilot, G2, Capterra, Shopify Reviews use-case guides in Wave 3.)

Common patterns across reviews scrapers

Canonical natural keys.

Rating-volume bias. Companies under 50 reviews show statistical noise in rating averages. For benchmarking, require 100+ reviews for stable ratings; 500+ for tight percentile bands.

Review-bombing detection. Three signals: volume spike (>10 negative in 24h vs daily baseline), new-account concentration, templated language. Filter on all three to distinguish coordinated attacks from real quality issues.

Recency weighting. Most platforms weight recent reviews (last 12 months) higher in headline ratings than older reviews. For trajectory analysis, compute trailing-12-month aggregate ratings separately.

Moderation lag. Reviews are often removed 1-30 days after publication for policy violations. For longitudinal rating analysis, snapshot weekly and treat data as time-frozen rather than authoritative-current.

Operational best practices for production pipelines

A handful of patterns matter more than the per-actor specifics once you're running these scrapers in production at scale.

Tier the cadence to match signal half-life. Daily polling is canonical for monitoring use cases (price drift, hiring velocity, brand mentions), but most teams over-poll long-tail watchlist items. Tier the watchlist into Tier 1 (high-stakes, hourly), Tier 2 (active monitoring, daily), Tier 3 (research-only, weekly). Typical 60-80% cost reduction with negligible signal loss.

Snapshot raw payloads alongside derived fields. Pipeline cost is dominated by scrape volume, not storage. Persisting the raw JSON response per snapshot lets you re-derive metrics without re-scraping when your sentiment model improves, your category-classifier evolves, or you discover a previously-ignored field. Compress with gzip at write-time (4-8x size reduction).

Three-tier retention. Most production pipelines run: 90 days of raw snapshots at full fidelity + 12 months of derived per-record aggregates + indefinite retention of derived metric time-series. Storage costs stay flat at scale; query patterns map cleanly to the right retention tier.

Cross-source dedup via the canonical 4-tuple. Across-source dedup (LinkedIn vs Indeed vs Google Jobs; Talabat vs Deliveroo vs Noon Food; Trustpilot vs G2) typically uses (name-norm, location-norm, identifier-norm, key-numeric). Within-source dedup uses each platform's stable natural key (place_id, asin, videoId, shortcode, etc.). Both are essential — get either wrong and metrics become noisy.

Validate live before declaring fields stable. Schemas drift. Run a daily validation suite that asserts each scraper returns the expected core fields with non-null rates above 80% (for required fields) and 50% (for optional). Alert on schema breakage same-day.

Tag scrape timestamps in every record. Platform-displayed timestamps lag actual events by minutes-to-hours. For accurate freshness analysis, treat (platform_timestamp, scrape_timestamp) as a tuple — the larger of the two is your "actively-listed since" anchor, the smaller is your "first-detected" anchor.

Frequently asked questions

Which review platform should I scrape for B2B SaaS competitive research?

G2 (canonical for B2B SaaS, 100K+ products) + Capterra (Gartner-owned, complementary coverage) + Trustpilot (consumer-side B2C overlap). For comprehensive B2B SaaS research, G2 is primary; Capterra catches what G2 misses (smaller vendors). Trustpilot adds end-user perspective for consumer-facing SaaS.

How fresh do review snapshots need to be?

For brand-monitoring (rating drift, crisis detection), daily cadence catches signals same-day. For competitive-research and benchmarking, weekly is sufficient. For longitudinal academic research, monthly. For M&A diligence (rating trajectory under acquisition narrative), daily during the diligence window + monthly historical baseline.

What about employer reviews (HR research)?

Glassdoor for global + US/UK depth (1.4M+ companies). AmbitionBox for India-specific employer reviews (richer comp data than Glassdoor India). For full HR-research coverage, run both — Glassdoor for MNC + product, AmbitionBox for IT services + mid-market India.

Can I detect coordinated review attacks?

Yes. Three signals: (1) review-volume velocity (>10 negative reviews in a 24h window vs typical baseline of 1-2/day); (2) reviewer-account age (newly-created accounts disproportionate); (3) language similarity (templated phrases, copy-pasted patterns). Combine all three for robust attack-detection. Real quality issues show distributed timing + organic-account distribution.

How does review-rating moderation lag affect data?

All major review platforms (Google, Trustpilot, Glassdoor) moderate reviews after publication for policy violations. About 5-10% of reviews get removed within 30 days of publication. For longitudinal rating analysis, expect apparent rating "improvements" that don't reflect underlying quality change. Cross-reference with employee-pulse signals or sales data to interpret.

How does this compare to Reputation.com or Birdeye?

Reputation.com and Birdeye are reputation-management SaaS at $5K-$50K/year per seat. They aggregate across review platforms with sentiment analysis and response-management workflows. The actor gives you raw review data at $2-$8/1K — for high-volume monitoring or cost-optimized stacks, the actor is materially cheaper. For full-stack reputation operations, Reputation.com / Birdeye win.

Browse all Thirdwatch scrapers on Apify Store — pay-per-result, free to try, no credit card to test.