A large International Hotel Chain approached for us with a challenge. They have operated multiple premium and luxury properties across the Aisa, and their management team required the centralized and data driven insight of customer sentiment.
Our client has thousands of customer Reviews and Ratings across multiple – domains, with different formats. Without a unified system, the management struggled to understand overall sentiment, identify recurring issue, or track services quality across regions.
They needed complete data visibility and what’s limited their ability:
- Heavy JavaScript and AJAX
- Dynamic page rendering
- GraphQL - Rest APIs Endpoints
- Paginated lists
- Multiple security layers and Challenges
- Time consuming and Human Errors
- URL Crawling queue management
- Inconsistent DOM selectors and frequent layout drift
- Multi-language encoding and normalization issues
- High-volume pagination and long-running job stability
This made manual extraction impossible and traditional scraping tools ineffective.
The Hotel Chain wanted:
- Collect 100% of all customer reviews (not just a few hundred samples)
- Centralize multi-domain review and rating data to unlock full-coverage sentiment analysis
- Analyze common complaints, repeated pain points, trending issues
- Track the impact of management responses on customer satisfaction
- Improve their NPS score
- Feed the data into their analytics and operational teams
- measure response effectiveness
- feed BI/operational workflows.
But the website structure and security prevented them from accessing the data at scale. This is where BotScraper’s stepped in.
We designed and architected a full-scale Automated Review and Rating extraction Solution based on the client requirement.
The Solution designed with core principle which strictly follows the SDLC architecture patterns:
Hotels.com, Expedia, Trip.com, Agoda, TripAdvisor, Booking.com
- Cursor-based pagination handling
- Infinite-scroll and paged endpoints supported
- Page-level throttling and pacing per domain
- Review text, star rating, timestamp, language, reviewer metadata
- Paired hotel management responses captured
- Reverse-engineered GraphQL and REST endpoints
- Token generation, cursor logic, API fallback mechanisms
- Fault detection → isolation → retry → recovery → validation loop
- Deduplication and mandatory-field enforcement
- Weekly automated scraping
- OS-level and internal BotScraper scheduler
- Auto-initiated full runs, delta runs, retries, and fallback logic
- Anti-bot detection mitigations
- Token refresh and signature logic
- IP/proxy rotation and header randomization
- Incomplete text, null ratings, truncated API responses
- Duplicate IDs detection
- Flag → retry → manual queue if unresolved
- CSV and Excel formats for analytics ingestion
- JSON APIs for downstream services
- SQL-formatted tables for BI integration
- Database-level tracking
- Automated instigation and resolution workflows
- Headless browser orchestration for client-rendered content
- AJAX lifecycle monitoring and event-driven capture
- Multi-node crawler infrastructure
- Progressive backoff and alternate routing on repeated failures
- Real-time monitoring and health checks
- Mapped hidden containers and dynamic selectors
- Decoded paginated GraphQL and REST flows with cursor logic
- Continuous logs, alerts, and auto-recovery triggers
- Operational runbooks for manual escalation when required
- Real-time failure recovery with retry and fallback strategies
- Multi-language normalizers and encoding fixes
- Browser-simulation support
- Multi-layer validation: schema, null, and field-level accuracy checks
- Automated regression testing after every scraper update
- QA monitoring for broken selectors, missing fields, and API failures
- Test cases for rate-limits, throttling, and pagination consistency
- Pre-delivery data audits ensuring 99%+ clean output
Travel & Hospitality
This project went beyond scraping — It delivered business intelligence, operational improvement, and competitive advantage.
We designed and architected a full-scale Automated Review and Rating extraction Solution based on the client requirement.
The Solution designed with core principle which strictly follows the SDLC architecture patterns:
Achieved 99.99% validated accuracy after automated fault-recovery.
100% reduction in manual review and verification efforts.
Delivered 100+ actionable insights across all hotel brands and review domains.
+20% increase in NPS for downstream stakeholders who utilized the insights.
Identified and corrected 4,200+ faulty or incomplete review records during pipeline runs.
Centralized data from six major travel platforms into a single standardized schema.
Distributed crawling improved extraction throughput by 4.7× during peak loads.
Weekly delta-detection reduced redundant crawling by 85%, saving bandwidth and operational cost.
This case study shows how BotScraper's powerful automation and intelligent data engineering enabled the client to convert chaotic, multi-platform hotel Rating and Reviews into a centralized, insight-ready data.
BotScraper helped the client enhance decision-making in operations, marketing, and client experience by conquering significant technological constraints, ensuring near - 100% data accuracy, and providing high-value business insights.
BotScraper demonstrated in a rapidly evolving digital environment, how scalable scraping intelligence can directly improve business success and help gain a competitive edge.
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