Table of Contents
You’re checking competitor reviews manually. One by one. Copying ratings into spreadsheets. Taking notes on customer complaints.
It takes hours. And by the time you finish, you’ve only scratched the surface of what customers are saying.
There’s a better way. Trustpilot scraping lets you extract thousands of competitor reviews in minutes, analyze patterns automatically, and spot opportunities your competitors are missing.
What Is Trustpilot Scraping?
Trustpilot scraping extracts review data programmatically from company profiles. Instead of reading reviews one at a time, you pull ratings, comments, dates, and company information in bulk.
The data sits in Trustpilot’s code as JSON objects. Scrapers access these hidden endpoints to collect structured information at scale.
This approach works for competitor review analysis because it processes volume that’s impossible manually. You can compare 10 competitors across 5,000 reviews each in the time it takes to read 50 reviews by hand.
Step 1: Identify Your Target Competitors
Start with 3-5 direct competitors who serve your market. Look for companies with substantial review counts—at least 100 reviews gives you enough data for meaningful patterns.
Check their Trustpilot profiles manually first. Note their overall ratings, review volume, and recent activity. This baseline helps you validate your scraped data later.
What to collect:
- Company name and Trustpilot URL
- Current overall rating (for verification)
- Total review count
- Industry category
Tell Us What You Think
Step 2: Choose Your Scraping Method
You have two main options for extracting Trustpilot data.
Manual Approach: Python Scripts
Build a custom scraper using Python libraries like requests and BeautifulSoup. This method requires coding knowledge but offers complete control.
⏱️ Time investment: 8-12 hours for initial setup
💰 Cost: Free (aside from proxy services if needed)
🎯 Best for: Technical teams with specific data requirements
You’ll need to handle anti-bot protections like rate limiting and IP blocking. Use realistic user agents, space out requests, and consider rotating proxies.
Automated Tools: No-Code Scrapers
Platforms like WebAutomation.io and LeadsScraper.io offer pre-built Trustpilot scrapers. Input competitor URLs, and the tool extracts reviews automatically.
⏱️ Time investment: 15-30 minutes setup
💰 Cost: Subscription-based (typically $50-200/month)
🎯 Best for: Non-technical users or teams needing quick results
These tools handle the technical complexity—proxies, rate limiting, data formatting—so you focus on analysis instead of infrastructure.
Manual vs. Automation: Scraper That Suits You!
Step 3: Extract Competitor Review Data
Configure your scraper to collect these data points for each competitor:
- Individual review text and ratings (1-5 stars)
- Review dates and timestamps
- Reviewer location (when available)
- Response status (whether company replied)
- Review helpfulness votes
Set your scraper to collect the most recent 500-1,000 reviews per competitor. This sample size captures current sentiment without overwhelming your analysis.
Export data in CSV or JSON format for easier processing. Most tools let you schedule regular extractions—weekly or monthly—to track reputation changes over time.
Gain the Competitive Edge Now
Step 4: Analyze Sentiment and Common Themes
Raw review data means nothing until you identify patterns. Look for recurring phrases in both positive and negative feedback.
Group reviews by rating. Read through 20-30 one-star reviews for each competitor. What specific issues appear repeatedly? Shipping delays? Poor customer service? Product quality?
Do the same with five-star reviews. What do customers praise? Fast delivery? Helpful support? Product features?
Sentiment Analysis Tools
For larger datasets, use sentiment analysis tools to quantify emotional tone automatically. Options include:
- MonkeyLearn (no-code sentiment classifier)
- Python’s TextBlob library (requires coding)
- AI-powered platforms like Latenode (automated analysis)
These tools assign sentiment scores to each review, letting you calculate average sentiment per competitor and track changes over time.
Transform Feedback into Business Success
Step 5: Benchmark Performance Metrics
Create a comparison table with these benchmarks:
| Metric | Your Company | Competitor A | Competitor B |
|---|---|---|---|
| ⭐ Average Rating | 4.2 | 4.5 | 3.8 |
| 📊 Total Reviews | 342 | 1,247 | 823 |
| 📈 Monthly Growth | +12 | +45 | +8 |
| 💬 Response Rate | 67% | 89% | 34% |
This quantitative view reveals where you stand. Maybe Competitor A has higher ratings but slower review growth. Or Competitor B has volume but poor response rates.
Track these metrics monthly. Changes indicate shifts in customer satisfaction or competitive positioning.
Compare, Analyze, and Outperform
Step 6: Turn Insights Into Action
Analysis without action wastes time. Use your findings to make specific improvements:
If competitors excel at customer service: Review your support processes. Can you match their response times? Train staff on common issues mentioned in reviews?
If customers complain about competitor pricing: Test competitive pricing strategies or emphasize your value proposition more clearly.
If competitors get praised for specific features: Evaluate whether adding similar capabilities makes strategic sense for your product roadmap.
Document your findings in a quarterly competitor intelligence report. Share with product, marketing, and customer success teams so everyone understands the competitive landscape.
☐ Identify 3-5 direct competitors with 100+ reviews
☐ Choose scraping method (Python script or no-code tool)
☐ Extract last 500-1,000 reviews per competitor
☐ Analyze sentiment and identify recurring themes
☐ Create benchmark comparison table
☐ Document findings in quarterly intelligence report
☐ Share insights with product and marketing teams
Make Your Insights Work for You
Ethical Considerations and Best Practices
Scraping public review data is generally acceptable, but approach it responsibly.
Space your requests appropriately. Hitting Trustpilot’s servers too aggressively can disrupt their service. Use 2-3 second delays between requests minimum.
Don’t republish scraped reviews verbatim on your own site. Use the data for internal analysis and strategic planning only.
Consider using proxy services for large-scale scraping to distribute requests across multiple IP addresses. This prevents blocks and maintains access.
Store scraped data securely. Review content sometimes includes personal information even though it’s public. Treat it with appropriate data protection measures.
Competitor review analysis through Trustpilot scraping gives you market intelligence that’s impossible to gather manually. You spot patterns faster, benchmark performance accurately, and identify opportunities before competitors react. The key is consistent analysis—monthly or quarterly—so you track changes instead of just snapshots.
FAQ
Trustpilot scraping is the automated process of extracting customer review data and company information from the Trustpilot website.
Direct Competition are the businesses that offer the same or very similar products or services to the same target audience
Indirect competitors offer different products or services but satisfy the same customer need or problem.
Python is a versatile programming language that, when paired with the BeautifulSoup library, enables easy parsing and navigation of HTML and XML web content.
Sentiment analysis tools are software platforms that use AI and natural language processing (NLP) to analyze customer opinions, emotions, and attitudes expressed in text or speech.
Benchmark performance metrics are standardized measures used to evaluate and compare the performance of a business, process, product, or service against industry standards or competitors.
Legal and ethical considerations in web scraping are critical because scraping involves automated data extraction from websites, which raises questions of privacy, intellectual property, and terms of use compliance.
Actionable insights are clear, practical conclusions drawn from data analysis that guide decision-making and prompt specific actions to improve business outcomes.ontent