GMB scraping and report

[CBUK]

Hello,

I am looking for an experienced data analyst or researcher to help us investigate the authenticity of Google My Business reviews across 40 different profiles.

The project involves the following:

1. SCRAPING
Scrape all reviews from 40 provided Google My Business listings and compile them into a clean, organised spreadsheet including reviewer name, profile link, review date, star rating, review content and reviewer location where available.

2. ANALYSIS
Analyse the scraped data to identify patterns and anomalies including but not limited to:
- Reviewer account age and activity history
- Geographic location of reviewers versus the business location
- Similarities in language, phrasing and writing style across reviews
- Clusters of reviews posted within short timeframes
- Reviewers with limited or no other review history
- Any cross-referencing of the same accounts across multiple listings

3. REPORT
Produce a clear, professional report summarising your findings with evidence to support whether the reviews appear genuine or fraudulent. The report should be suitable for submission to Google, Trading Standards or use in a legal context if required.

Ideal candidate will have:
- Experience in data scraping and analysis
- Familiarity with Google My Business and review platforms
- Strong attention to detail
- Ability to present findings clearly and professionally
- Experience producing reports for legal or compliance purposes is a bonus

Please provide examples of similar work and your proposed approach along with your quote. Timescale is important so please also confirm your availability and estimated turnaround time.

Looking forward to hearing from you!

 

[Link Builder007]

Hello,

I am looking for an experienced data analyst or researcher to help us investigate the authenticity of Google My Business reviews across 40 different profiles.

The project involves the following:

1. SCRAPING
Scrape all reviews from 40 provided Google My Business listings and compile them into a clean, organised spreadsheet including reviewer name, profile link, review date, star rating, review content and reviewer location where available.

2. ANALYSIS
Analyse the scraped data to identify patterns and anomalies including but not limited to:
- Reviewer account age and activity history
- Geographic location of reviewers versus the business location
- Similarities in language, phrasing and writing style across reviews
- Clusters of reviews posted within short timeframes
- Reviewers with limited or no other review history
- Any cross-referencing of the same accounts across multiple listings

3. REPORT
Produce a clear, professional report summarising your findings with evidence to support whether the reviews appear genuine or fraudulent. The report should be suitable for submission to Google, Trading Standards or use in a legal context if required.

Ideal candidate will have:
- Experience in data scraping and analysis
- Familiarity with Google My Business and review platforms
- Strong attention to detail
- Ability to present findings clearly and professionally
- Experience producing reports for legal or compliance purposes is a bonus

Please provide examples of similar work and your proposed approach along with your quote. Timescale is important so please also confirm your availability and estimated turnaround time.

Looking forward to hearing from you!

I can scrape and check GMB reviews with full report details. DM me for price and time.

 

[noneisnone]

I can do that i have previous experience in that

 

[PBN_king]

I can check and analyse GMB reviews with detailed report DM me for pricing and trunaround time

 

[Rankviking1]

Hello,

I am looking for an experienced data analyst or researcher to help us investigate the authenticity of Google My Business reviews across 40 different profiles.

The project involves the following:

1. SCRAPING
Scrape all reviews from 40 provided Google My Business listings and compile them into a clean, organised spreadsheet including reviewer name, profile link, review date, star rating, review content and reviewer location where available.

2. ANALYSIS
Analyse the scraped data to identify patterns and anomalies including but not limited to:
- Reviewer account age and activity history
- Geographic location of reviewers versus the business location
- Similarities in language, phrasing and writing style across reviews
- Clusters of reviews posted within short timeframes
- Reviewers with limited or no other review history
- Any cross-referencing of the same accounts across multiple listings

3. REPORT
Produce a clear, professional report summarising your findings with evidence to support whether the reviews appear genuine or fraudulent. The report should be suitable for submission to Google, Trading Standards or use in a legal context if required.

Ideal candidate will have:
- Experience in data scraping and analysis
- Familiarity with Google My Business and review platforms
- Strong attention to detail
- Ability to present findings clearly and professionally
- Experience producing reports for legal or compliance purposes is a bonus

Please provide examples of similar work and your proposed approach along with your quote. Timescale is important so please also confirm your availability and estimated turnaround time.

Looking forward to hearing from you!

i can handle your gmb scraping and deliver customized data reports quickly check dm.

 

[Genius Ranker]

We have Experience for this Service. DM US for More Details

Thanks and regards
Genius Ranker

 

[Adeebkhatri]

I can do that. I have experience in GMB scraping.

 

[AWANF Agency]

just send details, time, and price you provide

 

[saraali786]

Hello,

I am looking for an experienced data analyst or researcher to help us investigate the authenticity of Google My Business reviews across 40 different profiles.

The project involves the following:

1. SCRAPING
Scrape all reviews from 40 provided Google My Business listings and compile them into a clean, organised spreadsheet including reviewer name, profile link, review date, star rating, review content and reviewer location where available.

2. ANALYSIS
Analyse the scraped data to identify patterns and anomalies including but not limited to:
- Reviewer account age and activity history
- Geographic location of reviewers versus the business location
- Similarities in language, phrasing and writing style across reviews
- Clusters of reviews posted within short timeframes
- Reviewers with limited or no other review history
- Any cross-referencing of the same accounts across multiple listings

3. REPORT
Produce a clear, professional report summarising your findings with evidence to support whether the reviews appear genuine or fraudulent. The report should be suitable for submission to Google, Trading Standards or use in a legal context if required.

Ideal candidate will have:
- Experience in data scraping and analysis
- Familiarity with Google My Business and review platforms
- Strong attention to detail
- Ability to present findings clearly and professionally
- Experience producing reports for legal or compliance purposes is a bonus

Please provide examples of similar work and your proposed approach along with your quote. Timescale is important so please also confirm your availability and estimated turnaround time.

Looking forward to hearing from you!

Hi,

I'm very interested in this project and I'm confident I'm the right person for it , this is exactly the kind of work I specialise in.

I have hands-on experience investigating fake and fraudulent Google My Business reviews, identifying inauthentic patterns, and producing evidence-based reports. Alongside this, I specialise in negative review removal which means I understand not just how to spot suspicious reviews, but how to build a case around them that holds up when submitted to Google or escalated further.

Here's how I'd approach your project:

Data Collection — I'd use a compliant scraping tool to extract all reviews from your 40 listings into a clean, structured spreadsheet covering reviewer name, profile link, date, star rating, review content, and location where available.

Analysis — Drawing on my experience in fake review investigations, I'd systematically examine the data for:
- Timing clusters and review spikes
- Reviewer account age and activity history
- Geographic mismatches between reviewers and business locations
- Linguistic similarities and repeated phrasing across profiles
- The same accounts appearing across multiple listings

Report— I'd produce a professional, well-evidenced report with clear findings and conclusions, formatted for submission to Google, Trading Standards, or use in a legal context if required.

I've done this before and I know what Google and compliance bodies look for in a submission. That experience makes a real difference in how the report is structured and how effective it is.

I'm available to start immediately. Happy to share examples of past work and discuss timescales and pricing once I know more about the 40 listings.

Looking forward to speaking with you.

Thank you Sara

 

[floqal]

Hi CBUK,

You laid out a clear three-stage scope: scraping reviews across 40 Google Business listings, analysing them for fraud patterns, then producing a report fit for Google, Trading Standards or legal use. We have run this exact three-stage pipeline before.

Floqal is a technology studio. We operate live data-extraction across multiple platforms, and the same approach applies to Google Business Profile reviews.

How we would handle each stage:

Stage 1: Scraping
40 listings, full review history, delivered as a structured CSV with reviewer name, profile link, review date, star rating, review text, and a persistent reviewer ID for cross-listing tracking.

Stage 2: Analysis
We flag suspicious patterns including: account age and activity history per reviewer, language and writing-style similarity across reviews, timestamp clustering on posting bursts, the same account appearing across multiple of your listings, and reviewers with minimal review history.

Stage 3: Report
Roughly 30-page PDF: executive summary, methodology overview, per-listing case table. Raw data archive attached. Suitable for the CMA Citizens Advice route under the DMCC Act 2024 in force since April 2025, and as supporting evidence when you flag each review individually in Google Business Profile (Google does not accept volume takedown submissions).

Two points you should know up front:

1. Reviewer city or country field is no longer accessible since Google enabled custom name plus avatar profiles in November 2025. We can infer geographic concentration from contribution history, but that is a signal not a fact, and we mark it as such in the report.

2. The report we deliver is an investigative analysis: solid for Google flagging and Trading Standards or CMA submissions. For court use, a separate forensic expert sign-off is required, which is outside our scope and arranged on your side.

Fee and timeline: nine hundred US dollars, ten business days, all-inclusive (no extras).

For sample work, our public surface is at Floqal.com. Our ProspectPulse covers the closest category of data work, different source.

Send the list of 40 Google Business links and we start the same day.