Client
Social Travel Engine
E-mail: sales@promptcloud.com
Challenge
Solution
Challenge
The client was looking to build one of the largest review database by aggregating scattered reviews on hotels and destinations across multiple sources. They had tried few solutions around web page crawling but issues started creeping in as data scaled, given they needed new data regularly.
Also the number of sources were increasingly exponentially on the web and so was the data. Additionally, they wanted reviews from all countries in all languages and the author profiles, images, etc. from the web pages and decided to scrape hotel data with PromptCloud.
Also the number of sources were increasingly exponentially on the web and so was the data. Additionally, they wanted reviews from all countries in all languages and the author profiles, images, etc. from the web pages and decided to scrape hotel data with PromptCloud.
Solution
All historical data from each source was extracted in parallel with incremental data as reviews were published. Data was de-duped before delivery so only new data got uploaded. Machine learning techniques were employed for adaptive crawling thereby crawling the more active pages more often than others. Site list was dynamically modified based on client requirements. Over 20 million structured records were delivered in a period of 2 months.
Benefits of Scraping Hotels Data
- The client got easy and straightforward access to about 1 million price points daily from their industry
- Since our system sent out notifications on new data extracted, the client had the flexibility of importing new files into their system only when new data was available
- Productivity increased since the data team could work on other projects. The client expanded into other verticals
- Low turnaround time of data improved the ability to market client’s services and capabilities
- Value addition from the project was 50 times the spend
- Data quality levels had increased alarmingly without any time investment from the team
- A cost savings of about 37% was achieved by the client by not having to set up an in-house crawling team
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