Scraping Review Sites for Business Intelligence and Lead Signals
By Email ExtractorPublished 8 min read
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Why Review Sites Are Valuable for Business Intelligence
Review platforms contain structured, public data that reveals what companies use which tools, what they like and dislike, and when they are considering a change. This data is useful for competitive intelligence, market research and lead identification:
Intelligence type
What reviews reveal
Business application
Competitive analysis
What people like and dislike about competitor products
Position against weaknesses; reinforce strengths
Churn signals
Reviewers expressing dissatisfaction or intent to switch
Target unhappy customers of competitors
Technology stack
"We also use X and Y alongside this product" mentions
Identify technology combinations and integration needs
Buyer personas
Job titles, company sizes, industries of reviewers
Refine your ideal customer profile
Use case discovery
How people actually use a product vs how it is marketed
Identify underserved use cases
Market trends
Feature requests and pain points across a category
Product development and positioning insights
Pricing sensitivity
Comments about value, cost and pricing changes
Inform pricing strategy
Review Platforms and What They Contain
B2B software review platforms
Platform
Content type
Structured data available
G2
Enterprise and SMB software reviews
Reviewer name, title, company size, industry, star ratings by category, pros/cons, alternatives considered
Capterra
SMB-focused software reviews
Reviewer name, company size, industry, ratings, pros/cons, "switched from" field
TrustRadius
In-depth B2B reviews
Reviewer title, company size, detailed feature ratings, use cases
Gartner Peer Insights
Enterprise software reviews
Reviewer role, company size, industry, deployment experience
PeerSpot (formerly IT Central Station)
Enterprise IT reviews
Reviewer role, company, detailed comparisons
Software Advice
SMB software recommendations
Reviewer details, ratings, filtered comparisons
B2C and local business review platforms
Platform
Content type
Structured data available
Google Business Profile
Local business reviews
Reviewer name, star rating, date, business response
Yelp
Local business reviews
Reviewer name, rating, category, business details
Trustpilot
Company reviews (B2C and B2B)
Reviewer name, star rating, date, company response
BBB (Better Business Bureau)
Complaints and reviews
Company details, complaint history, resolution status
Industry-specific (Avvo for lawyers, Healthgrades for doctors, Houzz for contractors)
Professional reviews
Professional details, ratings, specialisations
Job review platforms (as business intelligence)
Platform
Intelligence value
Glassdoor
Employee satisfaction, management quality, company culture, salary data
Indeed reviews
Similar to Glassdoor; different reviewer pool
Blind
Anonymous employee discussions; candid about tools, processes, leadership
Extracting Intelligence from Reviews
What to look for in B2B software reviews
Signal
What it means
Example text
"Switching from..."
Active evaluation or migration
"We switched from [Competitor] because..."
"Compared to..."
Competitive positioning
"Compared to [Alternative], this tool is better at..."
"Missing feature"
Product gap that you may fill
"I wish it had better reporting/integration with..."
"We also use..."
Technology stack information
"We use this alongside Salesforce and Slack"
"Not worth the price"
Price sensitivity; potential churn
"After the latest price increase, we're looking at..."
"Our team of X"
Company size signal
"Our team of 50 found it easy to implement"
Star rating trend
Improving or declining product
Average rating dropping over 6 months signals problems
Response from vendor
Vendor engagement and priorities
How the company responds to criticism
Structured extraction approach
Step
Action
Output
1. Identify target
Choose the competitor or category to analyse
List of review pages to process
2. Collect reviews
Save review pages as HTML or export if the platform allows
Raw review data
3. Extract structured data
Parse reviewer details, ratings, text
Structured dataset
4. Analyse sentiment
Categorise positive and negative themes
Theme frequency analysis
5. Identify leads
Find reviewers expressing dissatisfaction or specific needs
Lead list with context
6. Extract contacts
Find reviewer identities where available
Contact information for outreach
Extracting reviewer contact information
Review platforms vary in how much reviewer information is visible:
Platform
Reviewer information typically visible
Contact extraction approach
G2
Name, title, company size (company name sometimes visible)
Cross-reference name + title + company size on LinkedIn
Capterra
Name, company name sometimes visible
Match reviewer to company; find email through company domain
TrustRadius
Title, company size, sometimes company
LinkedIn cross-reference
Google Business Profile
Google account name (often personal)
Limited B2B value
Glassdoor
Job title, location, "current/former employee"
Cross-reference with company employee lists
For reviews saved as HTML or PDF, upload to Email Extractor to extract any email addresses that appear in reviewer profiles, responses or contact sections. Note that most review platforms do not display reviewer email addresses, so this extracts addresses from related content on the page rather than reviewer emails directly.
Competitive Intelligence from Reviews
Building a competitive analysis
Analysis component
Data source
Method
Feature comparison
Pros/cons across reviews
Tally mentions of specific features
Satisfaction by segment
Reviewer company size and industry
Cross-reference ratings with reviewer demographics
Switching patterns
"Switched from" and "considering" mentions
Map migration flows between products
Common complaints
Negative reviews and low-star ratings
Categorise and rank complaint frequency
Pricing perception
Price-related comments
Analyse sentiment around value and cost
Support quality
Support-related comments
Compare support satisfaction across competitors
Implementation experience
Implementation/onboarding comments
Identify which products are easiest to adopt
Tracking competitor review trends
Metric
How to track
What it signals
Average rating over time
Monthly average of new reviews
Product quality trend
Review volume
Number of new reviews per month
Market activity and adoption
Response rate
Percentage of negative reviews with vendor response
Vendor engagement level
Feature mention frequency
How often specific features are mentioned
Feature importance and awareness
Competitor mention frequency
How often your product or competitors are named
Competitive dynamics
Sentiment shift
Proportion of positive vs negative reviews over time
Improving or declining perception
Lead Identification from Reviews
Churn signal mining
Signal strength
Review content
Outreach timing
Strong
"We are actively looking for alternatives"
Immediate outreach
Strong
"Our contract is up in [month] and we will not renew"
Outreach before the named date
Medium
"The latest update broke our workflow"
Outreach within a week; offer a solution
Medium
"Support has been unresponsive for months"
Outreach with emphasis on your support quality
Weak
"It would be better if it had [feature you have]"
Add to nurture; mention the specific feature
Weak
"It is okay for now but not ideal for our size"
Monitor for stronger signals
Outreach based on review intelligence
Approach
When to use
How to reference
Direct reference
Reviewer's review is public and clearly attributed
"I noticed your review on [platform] mentioned [specific pain point]"
Indirect reference
You identified the need but do not want to cite the review
"Companies in [industry] at your stage often struggle with [pain point]"
Content-first
The reviewer mentioned a need your content addresses
Share a relevant article, guide or case study
Competitor comparison
The reviewer compared your competitor to alternatives
Offer a specific comparison or trial
Important considerations:
Consideration
Guidance
Privacy
Do not reference private or anonymous reviews in outreach
Attribution
If you reference a public review, link to it so the prospect can verify
Tone
Never disparage the competitor; focus on how you solve the specific problem
Timing
Reviews have a shelf life; a 2-year-old complaint may be resolved
Consent
Reviews are public, but the reviewer did not opt in to your outreach; follow standard cold email best practices
Legal and Ethical Considerations
Terms of service
Platform
Scraping stance
Alternative data access
G2
Prohibits scraping in ToS
G2 Buyer Intent data (paid product); manual research
Capterra
Prohibits automated data collection
Manual research; Capterra advertising partnership
TrustRadius
Prohibits scraping
TrustRadius for Vendors (paid product)
Google Business Profile
Google ToS prohibits scraping
Google Business Profile API (with restrictions)
Yelp
Prohibits scraping; actively blocks
Yelp Fusion API (limited data)
Trustpilot
Prohibits scraping
Trustpilot Business (paid product)
Glassdoor
Prohibits scraping
Manual research
Ethical guidelines
Principle
Application
Respect terms of service
Use official APIs or manual research rather than automated scraping when ToS prohibits it
Do not fabricate or misrepresent reviews
Never create fake reviews or misquote real ones
Respect reviewer privacy
Do not collect or store reviewer personal data beyond what is publicly visible
Be transparent in outreach
If you reference a review, be honest about how you found it
Do not harass
One outreach attempt based on a review is acceptable; repeated messages are not
Use data for legitimate business purposes
Competitive analysis and lead generation are legitimate; harassment or defamation are not
Alternatives to scraping
Method
What it provides
Manual research
Read reviews, take notes, identify leads by hand
Official APIs
Some platforms offer APIs with limited data access
Vendor data products
G2, TrustRadius and others sell buyer intent data
Review monitoring tools
Services that aggregate and alert on new reviews (Mention, ReviewTrackers)
RSS feeds
Some platforms offer RSS for new reviews (check each platform)
Export your own reviews
Most platforms let you export reviews of your own product
Building a Review Intelligence Programme
Ongoing monitoring workflow
Step
Frequency
Action
1. Monitor competitor reviews
Weekly
Check for new reviews of key competitors
2. Categorise new reviews
Weekly
Tag by theme, sentiment and signal strength
3. Identify leads
Weekly
Flag reviewers expressing churn intent or specific needs
4. Research leads
As identified
Find contact information through LinkedIn and company websites
5. Outreach
Within days of review
Contact high-signal leads promptly
6. Update competitive analysis
Monthly
Refresh rating trends, feature comparisons and complaint patterns
7. Share insights
Monthly
Distribute findings to product, marketing and sales teams
