Viator Reviews Scraper: Extract Tour and Activity Customer Feedback

Understanding Viator Reviews and Their Business Value

Viator, a TripAdvisor company, hosts millions of reviews for tours, activities, and experiences worldwide. These reviews contain authentic customer feedback about tour quality, guide performance, value for money, and overall experience—critical intelligence for tour operators, travel agencies, and destination marketers.

Reviews reveal what truly matters to travelers: timing issues, accessibility concerns, guide expertise, group sizes, and unexpected highlights. For businesses, this data powers competitive analysis, service improvement, and reputation management. The Viator Reviews Scraper automates collecting this valuable feedback into structured datasets ready for sentiment analysis, trend identification, or quality monitoring.

What This Scraper Extracts

The scraper processes individual tour/activity URLs from Viator, extracting all customer reviews from each listing. Unlike manual collection, it captures complete review metadata including ratings, helpful votes, reviewer profiles, and response data.

Key Data Fields:

Rating: Numerical score (typically 1-5 stars) showing overall satisfaction. Essential for calculating average ratings and identifying satisfaction trends.

Title & Review Body: Review headline and full text content. The body contains detailed experiences, specific praise, complaints, and recommendations. Critical for sentiment analysis and identifying recurring themes.

Published: Review publication date enabling trend analysis over time, seasonal pattern identification, and freshness assessment.

Helpful Votes: Number of users who found the review helpful. High helpful votes indicate particularly valuable feedback worth prioritizing in analysis.

Is Helpful: Boolean flag possibly indicating algorithmic helpfulness assessment beyond user votes.

Reviewer: User profile information including username, location, and contribution history. Helps assess reviewer credibility and analyze demographic patterns.

Party Type: Travel group composition (solo, couple, family, friends). Crucial for segmenting feedback by traveler type and tailoring services accordingly.

Photos: Customer-uploaded images providing visual evidence of experiences. Useful for quality verification and marketing content.

Owner Response: Tour operator's reply to review. Tracks engagement levels and resolution of complaints, indicating customer service quality.

Machine Translated & Machine Translation Provider: Flags indicating auto-translated content. Important for understanding original language and considering translation accuracy in analysis.

Is Provided by TripAdvisor: Indicates cross-posted reviews from TripAdvisor platform.

ID & Search Meta: Unique identifiers and metadata for database integration and deduplication.

Input Configuration

The scraper targets individual Viator tour/activity pages containing reviews.

Example Configuration:

{
  "product_code": "6506LONHOHO",
  "sort_by": "DATE",
  "offset": 0,
  "ignore_url_failures": true,
  "max_items_per_url": 50
}

Parameters:

product_code: Product code (product code on viator.com product review page url, e.g. tours/London/Big-Bus-London-Hop-On-Hop-Off-Tour-with-Optional-River-Cruise/d737-6506LONHOHO -> 6506LONHOHO)",

sort_by: Review sorting method:

• ML_SORTED (Most insightful) - Algorithm-ranked helpful reviews
• DATE (Most recent) - Newest reviews first
• RATING (Highest rating) - Best ratings first

Choose DATE for trend monitoring, ML_SORTED for quality analysis, RATING for highlighting positive feedback.

offset: Starting position for pagination. Set 0 to start from beginning. Use higher values (50, 100) to continue from specific points when collecting large review sets.

ignore_url_failures: Set true to continue scraping remaining URLs if some fail. Essential for batch processing where some tours may be removed or have URL changes.

max_items_per_url: Maximum reviews to extract per tour (default 20, in example 50). Higher values collect comprehensive datasets but increase runtime. Set 100+ for complete review histories.

Output Structure and Field Definitions

Sample Output:

{
  "helpful_votes": 0,
  "id": "1056159397",
  "is_helpful": false,
  "is_provided_by_tripadvisor": false,
  "machine_translated": false,
  "machine_translation_provider": null,
  "owner_response": {
    "__typename": "Review",
    "id": "1056489620",
    "machine_translated": false,
    "machine_translation_provider": null,
    "party_type": null,
    "published": "2026-04-13T00:00:00Z",
    "rating": 0,
    "review_body": {
      "__typename": "TextValue",
      "locale": "en",
      "text": "Hello Jennifer_! We want to express our gratitude for your feedback! It's great to know that our tour around London was absolutely fabulous! We look forward to seeing you again soon! Wishing you a wonderful day! Alexandru from Big Bus Tours"
    },
    "reviewer": {
      "__typename": "Reviewer",
      "user_identifier_value": "28E2FCF1F33A74F128110F075393BB95",
      "username": "Customer S"
    },
    "title": {
      "__typename": "TextValue",
      "text": "Owner response"
    }
  },
  "party_type": "FRIENDS",
  "photos": {
    "__typename": "TravellerReviewPhotoSet",
    "count": 0,
    "photos": []
  },
  "published": "2026-04-11T00:00:00Z",
  "rating": 5,
  "review_body": {
    "__typename": "TextValue",
    "locale": "en",
    "text": "The best tour around London was absolutely fabulous! The commentary was wonderful. The sites were amazing and we had the most wonderful day. I highly recommend this tour."
  },
  "reviewer": {
    "__typename": "Reviewer",
    "user_identifier_value": "120939817",
    "username": "Jennifer_G"
  },
  "search_meta": null,
  "title": {
    "__typename": "TextValue",
    "text": "Big Bus Tour!"
  }
}

Field Purposes:

Helpful Votes: Identifies most valuable reviews for prioritization. Reviews with 20+ votes often highlight critical issues or exceptional experiences worth immediate attention.

Party Type: Enables segmentation analysis—families may prioritize safety, couples value romance, solo travelers seek social opportunities. Tailor marketing and service improvements accordingly.

Photos: Visual validation of claims. Multiple photos in positive reviews provide marketing assets; photos in negative reviews require investigation.

Owner Response: Tracks customer service responsiveness. Response rate and quality correlate with business reputation. Lack of responses to negative reviews signals engagement issues.

Machine Translated: Identifies non-English reviews requiring careful interpretation. Translation quality affects sentiment analysis accuracy.

Published Date: Enables time-series analysis tracking quality improvements, seasonal variations, or impact of operational changes.

Implementation Guide

1. Collect Tour code: Identify Viator tours to monitor—competitors, partners, or your own listings.

2. Configure Parameters: Set sort_by based on analysis goals (recent trends = DATE, quality assessment = ML_SORTED). Set max_items_per_url to 50-100 for comprehensive data or 20 for quick sampling.

3. Execute Scrape: Run via Apify console. Processing 5 tours with 50 reviews each typically completes in 2-3 minutes. Monitor progress in real-time.

4. Analyze Results: Filter by rating to identify problems (1-2 stars) and successes (5 stars). Group by party_type to understand different traveler needs. Track helpful_votes to prioritize actionable feedback.

5. Export Data: JSON for databases and analysis tools, CSV for spreadsheets and reporting, Excel for business presentations.

Best Practices: Schedule weekly scraping for active monitoring. Store historical data to track improvement trends. Cross-reference reviews with booking data to calculate review-to-booking conversion rates.

Strategic Applications

Service Quality Monitoring: Track rating trends over time. Sudden drops signal operational issues requiring immediate investigation. Consistent 4.5+ ratings validate quality standards.

Competitive Benchmarking: Compare your tour ratings, review volume, and common complaints against competitors. Identify service gaps and competitive advantages.

Guide Performance Analysis: Extract mentions of specific guides from review text. Identify top performers for recognition and training needs for coaching.

Seasonal Optimization: Analyze review patterns by month. Summer complaints about overcrowding suggest capacity limits; winter praise for personalized attention indicates off-season opportunities.

Marketing Content Mining: High-rated reviews with photos provide authentic testimonials and user-generated content for marketing campaigns.

Response Strategy Development: Study owner responses (yours and competitors) to identify effective complaint resolution patterns and service recovery techniques.

Product Development: Recurring suggestions in reviews ("wish it included lunch," "needed more time at each stop") guide tour enhancement decisions.

Maximizing Data Value

Sentiment Analysis Integration: Use review text with NLP tools to quantify sentiment beyond star ratings. Detect emotional language patterns indicating strong satisfaction or frustration.

Photo Quality Assessment: Analyze customer photos for authenticity signals in marketing. Poor-quality photos in positive reviews may indicate fake reviews.

Reviewer Credibility Scoring: Weight feedback from users with high review counts and helpful votes more heavily than one-time reviewers.

Multi-Language Intelligence: Even if machine-translated, reviews in various languages reveal international market perceptions and cultural preferences.

Response Rate KPIs: Calculate percentage of reviews receiving owner responses. Industry benchmark is 60%+ for quality-focused operators.

Temporal Trend Analysis: Compare recent reviews (last 3 months) with historical averages to detect quality shifts before they impact bookings.