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Large-scale Price Data Extraction from Hotel Booking Portals

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A popular online travel portal from India, looking for a technological solution for hotels price matching.

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Challenge: Hotel Price Matching Across Portals

The client wanted to build an internal system for hotels price matching using the pricing data extracted from their competitors. Since the target websites had complex and dynamic coding elements and hundreds of thousands of hotel listings, the scraping hotels data demanded an extensive infrastructure and high-end resources. The client didn’t have the technical know-how to go about this and wanted a fully-managed service that can take end-to-end ownership of the data mining process. Another key requirement was that the data must be extracted at a frequency as high as twice a day, which again is resource-intensive.

The Solution

The client shared the detailed requirements including the target sites, crawling frequency, their preferred data delivery format and the data points they wanted to extract from these sites. This use case comes under our site crawling service since the websites in the list had different structuring and design. The client needed the extracted data in JSON format and was ready to use the PromptCloud API to access the extracted data at their end.
As per their instructions, the different target sites had to be crawled at different frequencies, including twice a day, fortnightly and daily. Our team completed the web crawler set up for the three target sites in just five days and the initial set of data files was delivered to the client. About 2.5 million records were delivered to the client during the first webcrawl, solving their hotels price match issue.

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. 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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