Every review. With the owner's reply.
For each Google Maps listing, outsend extracts every published review (author, rating, date, text, owner reply) and explicitly flags reviews with no owner reply. Instant online reputation signal for your prospects, clients, or competitors.
You want to understand the voice of customers at a restaurant, a shop, a hotel — for competitive benchmarking, online reputation auditing, or sector research. Google Maps hosts thousands of public, dated, signed reviews, but offers no native export. Reading 200 reviews manually takes hours; turning them into analysable data takes a full day.
outsend's Google Maps review scraper extracts all this public data for the listings you provide — with one essential signal: the owner's reply is explicitly identified. You immediately see, per listing, the response rate, negative reviews left unanswered, and can cross-reference this with your sales or advisory strategy.
5 structured fields per review
For every review retrieved, here's what you'll find in your CSV — ready to use in Excel, Sheets, or your BI tool. The listing columns (name, address, phone, website) are repeated on every row.
avis_auteur
Name publicly displayed by the review author.
"Marc D."avis_note
Review rating, from 1 to 5 stars.
5avis_date
Date as Google displays it, often relative.
"3 months ago"avis_texte
Review content.
"Flawless service…"avis_reponse_proprietaire
Public reply of the owner. Empty when there is none.
"Thank you Marc…"An owner's silence is a signal
Most review scrapers give you raw text. outsend returns the owner's reply in a dedicated column: when it is empty, the review has no reply. You get a directly actionable online reputation signal: "this business ignores its critics" or "this owner is engaged and looks after their customers."
Combined with cluster scoring (4★+ with reply, 2★- without reply), you turn raw reviews into a strategic indicator.
The scraper in 3 steps
You can scrape up to 100 listings in a single batch operation.
Identify the listing
The Google Maps link of each listing. You can pass a list of listings as a batch, or chain directly from a previous Google Maps scrape.
Extract all reviews
From most recent to oldest, with no cap. For each review: author, rating, date, text, and the owner's reply if it exists.
Analyse or export
CSV / XLSX / JSON ready for Sheets, Excel, or your BI tool.
Four typical user profiles
Restaurant owner benchmarking
Extract reviews from your 15 direct competitors to identify recurring complaints (wait times, cleanliness, quality) and find your differentiation angles backed by real market feedback.
E-reputation agency
Monitor customer voice across your 50 restaurant and retail clients, flagging negative reviews without a reply for rapid intervention. Weekly report with an open review counter.
Medical / healthcare practice
Analyse your own reviews to identify positive patterns (to highlight) and negative ones (to fix internally). Without paying a reputation platform $100/month.
Marketing student / researcher
Build a customer review dataset for your thesis or sector study, with cross-region benchmarking. Public source, no budget required.
Vs Outscraper, Trustfolio, manual scraping
Direct answer: three families of tools can extract the reviews from a Google Maps listing — browser extensions, suited to a handful of listings; general-purpose extraction platforms, which handle volume but return a raw export; and prospecting tools that fold extraction into a qualification chain. What separates them is not the number of reviews extracted but the fields returned: an export limited to author, rating, date and text lets you compute an average, not assess a reputation. For that you need the field missing almost everywhere — the owner's reply, and above all its absence: a negative review left unanswered for weeks is the signal, not the rating itself. outsend returns 5 fields per review, including the owner's reply, empty when there is none.
| Capability | outsend | Outscraper | Trustfolio | Manual |
|---|---|---|---|---|
| All reviews (not just 10) | ✓ | ✓ | own reviews only | possible, slow |
| Owner reply explicitly flagged | ✓ | raw only | not the focus | visible |
| Pipelinable with Maps scrape | ✓ native | via API | no | no |
| Entry price | Alpha €0 | ~$0.003/review | ~€100/mo | €0 (time cost) |
Compliance — public professional reviews
Google Maps reviews are voluntarily published by their authors on a public platform, in a professional context (rating a business or establishment). Extracting them falls under legitimate interest for web harvesting under GDPR. outsend collects no private data (email, personal phone number) — only what is publicly visible to any Google Maps visitor.
Analyse your reviews (or your competitors')
Every Google Maps review with the owner's reply explicitly flagged. Free alpha — no per-review cost like Outscraper.
Create a free account →Frequently asked questions
Can you scrape all reviews or only the first 10?
All of them. outsend scrolls through the complete review history of a listing, from newest to oldest, with no imposed cap. For very dense listings (restaurants with 2,000+ reviews), allow a few minutes for the scrape.
How does owner reply detection work?
Google Maps clearly marks owner replies with a specific label ("Owner's response") and distinct typography. outsend reads this marker and extracts the reply text into the avis_reponse_proprietaire column.
How many listings can be processed in a batch?
Up to 100 listings in a single batch. Beyond that, it's better to chain multiple batches or use a pipeline that combines a Maps scrape → reviews automatically. No global cap on total reviews retrieved in alpha.
What export formats are available?
CSV (UTF-8, semicolon delimiter), XLSX and JSON, one row per review. You choose at export time.
Does the scraper retrieve photos attached to reviews?
No. The module returns the author, rating, date, text and owner reply; attached photos are not retrieved.
What about Google Maps ToS risk?
outsend reads public Google Maps pages as a browser would, without authentication or session cookies, at a respectful pace that avoids overloading their servers. The GDPR legitimate interest framework recognises the harvesting of public data under appropriate conditions — which applies to public professional reviews.