Google Maps Reviews Scraping
Get actionable business data, find customers, and track competitors with our Google Reviews dataset.
- Full review history per listing — not just the most-recent page
- Reviewer name, profile, total reviews, photos and helpful count
- Owner responses captured alongside the original review
How we extract google reviews data
The full pipeline from your brief to the final delivered file — no black box, no surprises.
1. Lock the target listings
Provide the businesses you want reviews for — by Google Place ID, by Maps URL, by business name + city, or as a category sweep ('all dentists in Sydney'). We confirm the projected review count per listing before any scraping starts.
2. Harvest the full review timeline
For each listing, our pipeline scrolls through Google's review feed all the way back to the very first review, not just the 5–10 most-recent that show by default. Sort orders captured: newest, highest, lowest, most relevant — so you get a complete archive regardless of how Google ranks them today.
3. Extract the long-tail review fields
Each review row gets: full text, star rating, posted date, detected language, reviewer name + profile URL + avatar, reviewer's total review count, local-guide level, helpful-count, photos uploaded with the review, and Google's review_id (for deduplication and citation).
4. Capture owner responses
Where the business has replied to a review, we extract the response text plus the response timestamp on the same row. Useful for measuring response rate, response time, and tone of brand engagement.
5. Deduplicate by review_id
Google sometimes serves the same review twice across different sort orders. We collapse duplicates by Google's stable review_id so each customer voice appears exactly once in the final file.
6. Optional: sentiment + topic tagging
Add per-row sentiment score (positive/neutral/negative + confidence), topic tags (food, service, ambience, value, etc.) and entity extraction. Powered by an LLM pass over each review — flat fee per 1,000 rows.
7. Deliver as CSV / XLSX / JSON
Default delivery is a single ZIP with CSV (UTF-8), XLSX (with a schema sheet) and a README. JSON or NDJSON for ML pipelines, direct push to BigQuery / Snowflake / Postgres available via our automation service.
8. Optional: schedule incremental pulls
Re-run weekly or monthly and we deliver only the new and changed reviews since the last run. Critical for live reputation dashboards — you stay in sync without re-importing the full archive every time.
Every field captured per business
31 data points per record, grouped into 6 categories. Each is a real column in your delivered CSV/XLSX.
Review identity
Stable identifiers tying every review back to the listing it belongs to.
place_idChIJ1cIlk0JZwokRQOqE6XMWUL8Google's stable identifier for the businessbusiness_nameLushful Aestheticsplace_cid13785543146475940416Numeric CID — useful for direct review-page URLsreview_idChdDSUhNMG9nS0VJQ0FnSURGcGNLSGdRRRABStable per-review identifier — primary key for dedupreview_urlhttps://search.google.com/local/reviews?placeid=...Direct deep-link to the review on Google
Review content
What the customer actually wrote, when, in what language, and how it scored.
rating51–5 star scorereview_textBest laser facial in NYC — staff were attentive...Full text, no truncationreview_date2026-03-14ISO 8601 date the review was postedreview_date_relative5 weeks agoOriginal Google relative format if you need itreview_languageenISO 639-1 language code, auto-detectedreview_length127Character count — handy for filtering out one-word reviews
Reviewer profile
Who left the review — their public Google profile, history and credibility signals.
reviewer_nameSarah Chenreviewer_id106355654370582843500Stable ID for the reviewer (their Google contributor profile)reviewer_profile_urlhttps://www.google.com/maps/contrib/106355.../reviewsreviewer_avatar_urlhttps://lh3.googleusercontent.com/a/AcHTtcS...=s44-creviewer_total_reviews47How many reviews this person has written across Googlereviewer_total_photos12reviewer_is_local_guidetrueBoolean — Google's Local Guide programme membershipreviewer_local_guide_level5Local Guide level (1–10) where applicable
Owner response
How the business engaged with the review — text, timing, and response-rate signal.
has_owner_responsetrueBoolean — quick filter for engaged vs unanswered reviewsowner_responseThank you Sarah! We're so happy you enjoyed your visit.owner_response_date2026-03-15owner_response_lag_days1Days between the review and the owner response
Engagement
Helpful votes and uploaded photos — proxy for which reviews other customers find most useful.
review_helpful_count3Number of users who marked the review as helpfulphoto_count2Number of photos the reviewer uploaded with the reviewphoto_urlshttps://lh3...=w800; https://lh3...=w800Semicolon-separated list of full-size photo URLs
Optional add-ons
Sentiment + topic tagging available as a paid LLM pass over the review text.
sentimentpositivepositive / neutral / negative — LLM-generatedsentiment_score0.920.0–1.0 confidence score from the modeltopicsservice, results, atmosphereComma-separated tags extracted from the textentitiesSarah Chen → reviewer; laser facial → serviceEntity extraction (people, products, services)language_detected_confidence0.99Confidence of the language detection model
Need a custom field that's not listed? Mention it in the quote request and we'll confirm whether the source page exposes it.
Download a sample of our Google Reviews dataset
Find new clients and close more deals with the world's best business leads provider. Grab a 25-row sample CSV — same schema as the paid extracts, real records, no card required.
- · 25 real records with the full schema
- · UTF-8 CSV — opens in Excel, Sheets, Airtable
- · Documented fields and data types
- · No credit card · sent to your inbox
Why choose us for your business
The same operating principles every project, regardless of scope: flexible, secure, scalable.
Flexible
Custom-built per project. Tell us the source, the fields, the volume, the cadence — we deliver to that exact spec.
Secure
Stripe-secured checkout, GDPR-aware delivery, signed download URLs that expire. Your data and your buyers' privacy are protected end-to-end.
Scalable
From a single suburb pull to a daily multi-million-record pipeline. Same infrastructure, scaled to whatever volume you need.
How B2B Connection helps businesses with google reviews
We pull every public review attached to a Google Maps business listing — the entire history, not the 5-10 most-recent reviews you see when you visit the page. Each row contains the full review text, star rating, reviewer profile, language, posted date, photos uploaded with the review, and the business owner's response if one exists.
Use it to power sentiment analysis, reputation-recovery campaigns, competitor benchmarking, support-ticket back-mining, or to feed an LLM a structured corpus of customer voice for any vertical. Delivered as CSV + XLSX with a documented schema; JSON / NDJSON available for ML pipelines.
What's included
- Complete review history (not paginated truncation)
- Star rating, posted date, language and review length per row
- Owner response text + response date when available
- Reviewer profile: name, avatar, total reviews authored, local-guide status
- Photo URLs uploaded with the review
- Sentiment / topic tagging available as a paid add-on
Common use cases
- Brand sentiment + NPS-style reputation monitoring at scale
- Competitor analysis — what customers love or hate about rival venues
- Back-mining customer-support tickets and product feedback
- Reputation-recovery campaigns targeting venues with sub-3.5★ ratings
- LLM training data — structured customer voice across any vertical
Why enterprises use B2B Connection
Six things our buyers consistently mention when they renew or refer us.
1,500+ clients
From SaaS vendors to global recruiters and hospitality groups, across Australia, the US and Europe.
500M+ records scraped
180M phones, 100M+ emails, deduplicated and verified across our pipelines.
Stripe-secured checkout
Card data never touches our servers. Refunds processed inside Stripe's standard 5-business-day window.
GDPR-aware delivery
Optional PII stripping for EU-bound deliveries. Data retention defaults to 30 days post-handover.
Same-day quotes
Project briefs quoted within one business day. First sample within five.
Spam Act 2003 compliant
All B2B records sourced from publicly listed business pages — inferred-consent safe under Australian and US/UK rules.
Related services
Google Maps Scraping
Extract every business listed on Google Maps for any region or category — names, addresses, phones, websites, ratings, reviews and social profiles.
Custom Web Scraping
Pull structured data from any public website — directories, marketplaces, news sites, B2B catalogues, real-estate portals.
Review & Rating Scraping
Extract every review from Google, Tripadvisor, Yelp, Trustpilot and others — with text, rating, date, reviewer and reply.
Ready to get a quote for google maps reviews scraping?
Tell us your source, fields and timeline. We'll respond within one business day.
Frequently asked questions
How far back can you pull reviews?
All the way to the very first review on the listing. Google's UI defaults to the most-recent ~10 but the underlying review feed is paginated infinitely — our scraper walks the entire history regardless of how old the listing is.
Do I get the full review text or just an excerpt?
Full text, untruncated. We click 'See more' on every multi-paragraph review so you get the complete content rather than the 200-character preview Google shows by default.
Are owner responses included?
Yes — when the business has replied to a review, you get both the response text and the response timestamp on the same row. We also compute response lag in days so you can analyse engagement velocity.
Can you tag sentiment and topics?
Yes, as a paid add-on. We run each review through an LLM pass that tags sentiment (positive/neutral/negative + confidence), extracts topics (food, service, ambience, value, etc.) and identifies named entities. Flat fee per 1,000 rows — quoted at brief time.
How do you handle the same review appearing in multiple sort orders?
Google sometimes returns the same review across newest/highest/lowest sort orders. Every review carries Google's stable review_id, and we deduplicate on that key — every customer voice appears exactly once in the final file.
Is reviewer profile data legal to extract?
Yes — every field we capture is publicly visible on the reviewer's Google contributor page. We never extract private information (email, phone) and never extract data from reviews protected behind a login. Output complies with the same public-data principles as our other scraping services.
What format do I get the data in?
Default is a ZIP containing CSV (UTF-8, header row), XLSX (with a second sheet documenting the schema) and a README. JSON / NDJSON available on request — recommended when you're piping the reviews into an ML or sentiment-analysis pipeline.
How quickly can you deliver?
1–3 business days for a one-shot pull from up to a few thousand listings, including the full review history per listing. Larger or scheduled extracts quoted on a per-volume basis. We share a sample within 24 hours so you can verify the schema before the full extract runs.
Can you run this on a schedule for ongoing monitoring?
Yes. We can re-run weekly or monthly and deliver only the new + changed reviews since the last run, so your sentiment dashboard or reputation monitoring tool stays in sync without re-importing the entire archive.