OTA Reviews Scraping Automation into Snowflake

Client Overview

The client is a hospitality and travel data company that analyzes guest reviews to support:

• Reputation management
• Competitive benchmarking
• Customer sentiment analysis
• Hotel performance analytics

Reviews were spread across multiple Online Travel Agencies (OTAs), making it difficult to analyze trends in a unified way.

Business Challenges

Fragmented Review Sources

• Reviews scattered across multiple OTAs
• Different rating scales (5-point, 10-point)
• Inconsistent review metadata

Data Duplication

• Same reviews appearing across platforms
• No reliable deduplication logic

Property Mapping Issues

• Each OTA used different property IDs
• No standardized hotel identifier

Snowflake Integration

• Needed clean, structured data
• Required incremental updates and historical backfills

Actowiz Solutions Architecture

OTA Extraction Layer

• OTA-specific scrapers
• Incremental review fetching
• Support for historical backfills

Normalization Layer

• Unified review schema
• Rating scale normalization
• Language and trip-type tagging

Entity Resolution

• OTA property ID → internal hotel ID mapping
• Name, location, and URL-based matching
• Confidence scoring

Snowflake Data Load

• Staging tables
• Deduplication using hash keys
• Merge and upsert logic

Monitoring and Quality Control

• Field completeness checks
• Volume anomaly alerts
• Scrape failure notifications

Business Impact

• Centralized OTA reviews in Snowflake
• Faster sentiment and trend analysis
• Reduced manual data collection
• Improved decision-making for hotel partners

Conclusion

By automating review scraping and delivering a clean Snowflake-ready dataset, Actowiz Solutions helped the client transform fragmented OTA reviews into a powerful analytics asset.