Monitor Google reviews across every location

The problem

Google\'s own dashboard only shows you the latest 10 reviews per location. When you operate 50+ locations, "what changed this week" becomes impossible to read manually. Reputation-management SaaS tools cost $200–$500/location/month and lock the raw data inside their proprietary UI.

Livescraper pulls every review for every location, with full reviewer text, rating, timestamp, owner-reply status, and language — into a clean CSV. From there, you can pipe into Slack, Snowflake, a BI dashboard, or just a weekly digest email to operators. You own the data.

How it works in Livescraper

1
Build your location list
Either upload a CSV of place_ids (most reliable) or feed in the Google Maps URLs of each location. For brands without place_ids handy, run a quick Google Maps Data Scraper first to grab them.
2
Pick the review window
Pull all reviews ever, or just the last 7 / 30 / 90 days. The "last N days" mode is cheapest for weekly monitoring — you only pay for new reviews each run.
3
Choose your sort and language settings
"Most recent" is the right pick for monitoring. "Most relevant" matches what shows on the Maps UI but skips older reviews. Pick your language(s) — the API can return only English, only French, or all.
4
Run weekly on a schedule
Most teams either set a cron job that calls our API or just run the task manually every Monday morning. Both work. We email a download link on completion.
5
Route by rating
Pipe 1-star and 2-star reviews to your support inbox so they can respond. Pipe 5-star reviews to marketing for social proof. Pipe trends to your weekly ops dashboard.

Worked example

A regional fitness chain (87 gyms across 6 states) was getting blindsided by review spikes from a single underperforming location pulling down the brand-wide rating. They set up a weekly Livescraper pull of all reviews from the past 7 days across all 87 locations. Average pull: 240 new reviews per week, completed in 3 minutes, costing ~$0.48. They piped 1-stars to Slack with @-mentions for the location GM. Within 2 months their brand-average rating climbed from 4.2 to 4.4 — driven entirely by faster response times to negative reviews, which Google rewards in ranking.

What you get back

One row per review. Fields returned by Google Maps Reviews Scraper:

✓Location join: business_name, place_id, google_id, place_cid, google_place_url
✓Review identity: review_id, review_link, reviews_id, review_url
✓Reviewer: author_title, author_link, author_id, author_image
✓Content: review_text, review_rating (1–5), review_img_url, review_img_urls, review_likes
✓Timing: review_timestamp, review_datetime_utc
✓Owner reply: owner_answer, owner_answer_timestamp, owner_answer_timestamp_datetime_utc
✓Place stats (denormalised on each row): total_reviews, average_rating, reviews_per_score

review_datetime_utc	business_name	review_rating	author_title	review_text	owner_answer
2026-05-10T14:22:00Z	Acme Gym — Austin	5	Sarah K.	Great trainers and clean equipment…	Thanks Sarah! We appreciate it.
2026-05-09T18:01:00Z	Acme Gym — Dallas	2	Mike R.	Showers were out of order on a Saturday…
2026-05-08T09:47:00Z	Acme Gym — Houston	4	Priya V.	Solid gym overall, peak hours are crowded…	Thanks for the feedback — we're adding more equipment.

Sample rows · not a real query result · your data will be richer

Best for / Not for

Best for

Multi-location brands wanting raw review data in their own warehouse
CX teams routing reviews to support / marketing automatically
Reputation-management agencies billing clients per-location
Investors / corp dev doing diligence on review sentiment trends

Not for

Real-time alerting (Livescraper is pull-based, not push)
Yelp / TripAdvisor / Facebook reviews — Maps reviews only
Sub-3-location businesses (just check the Maps app daily)

FAQ

Can I pull every review ever for a location, not just recent?

Yes — set the window to "all time". Expect 5,000+ reviews on a popular long-running location; the run takes minutes per place. Most teams do a one-time historical pull then switch to weekly incremental.

How do you handle non-English reviews?

Reviews are returned in their original language with a language code per row. Sentiment / topic analysis is on your side — most teams pipe to GPT-4-mini for classification, which costs about $0.50 per 1,000 reviews.

Can I get owner replies too?

Yes. Every row includes an "owner_replied" boolean plus the reply text and timestamp if present. Great for auditing your own team's response SLA.

What about review changes / edits / deletions?

Each review has a stable review_id. Run weekly and you'll see new reviews appear; deleted reviews simply stop appearing in future runs. We don't track edit history (Google doesn't expose it).