How a Travel Startup Powered Its Booking Intelligence System with Grepsr’s Real-Time Hotel Data Extraction

In the travel industry, booking data is the pulse that reveals how markets move. It captures the patterns of demand, competition, and consumer intent like who’s booking, where, when, and at what price. This information fuels dynamic pricing, helps forecast occupancy, and enables travel platforms and hotels to anticipate market shifts rather than react to them.

Without booking data, every decision such as rate adjustments, promotions, or inventory management, would run on guesswork. With it, travel companies can see the entire demand curve in motion and respond in real time.

That’s where hotel data extraction becomes mission-critical. The web already hosts this information across platforms like Booking.com, but it’s fragmented and constantly changing. Automating its collection at scale turns raw listings into structured, actionable intelligence to power competitive benchmarking, revenue optimization, and smarter traveler experiences.

In short, travel intelligence begins with data visibility, and visibility starts with precise, reliable hotel data extraction. 

About Client

A travel technology startup set out to redesign how travelers discover hotels and how hotels manage direct bookings. Their vision was to build a dual-benefit system – a platform that worked for both ends of the booking chain.

For travelers, the goal was simplicity: make it effortless to find the right hotel at the right price by comparing rates across destinations in one unified interface. For hotels, the platform promised a data-driven advantage by helping them increase direct bookings, avoid OTA (Online Travel Agency) dependency, and manage inventory and pricing more effectively.

To prove their model, the startup initially gathered hotel rate data manually for selected destinations. These small proof-of-concept datasets convinced early hotel and travel-partner stakeholders that reliable price intelligence could reshape how room rates were positioned online. But manual collection couldn’t scale globally.

As their services expanded to worldwide hotel coverage and real-time pricing visibility, they turned to Grepsr to automate and scale the hotel data extraction process.

Their requirements

The client’s data needs were shared in two different but interconnected projects. Both addressed a critical layer of their travel intelligence system.

Requirement 1: Dynamic Pricing Data

This was their first and major requirement to automate the extraction of current and future hotel room rates from a certain booking website.

The goal was to build a comprehensive rate intelligence dataset that reflected real-time pricing fluctuations and allowed trend analysis.

The key details:

• Extract room rates up to 90 days ahead from the date of each crawl.
• Capture related fields such as hotel name, hotel ID, location, stay date, and rating.
• Frequency: bi-weekly crawls for complete global coverage.
• Initially limited to 20 major cities, it later expanded to 400+ cities and their hotels.

The purpose of this was to enable the client’s platform to monitor price changes across markets, benchmark competitor rates, and feed accurate pricing data into their recommendation and analytics systems.  

Requirement 2: Static Metadata and Hotel Attributes

Their second requirement focused on collecting the rest of the hotel information. These details remain consistent over time and form the foundation of hotel listings. 

Key details:

• Extract static fields such as hotel name, address, country, state, city, room types, images, and reviews.
• Download and share hotel and room images as separate, mapped files.
• Update periodically or on demand when content refreshes are required.

The purpose of doing this was to maintain a clean, structured hotel catalog enriched with verified metadata and visuals, ensuring that the client’s database remained synchronized with live listings. 

The challenges

Nonetheless, the projects were not as simple as they seemed when the client sent their aforementioned requests.  

1. The first obstacle was large-scale global hotel data extraction. We had to extract the hotel prices for every major destination worldwide. The problem was that the booking site did not offer a direct and structured list of cities or hotels, which made full coverage technically complex. A single large crawl, in this case, increased the runtime and also delayed data delivery. 

2. Next was the data discrepancies between projects. The initial project of collecting pricing data and metadata was growing at different rates. The hotels that we newly added to the pricing dataset did not have corresponding metadata and images. This mismatch further disrupted the client’s integrated data model to fuel their intelligence system. 

3. Additionally, the high volume of hotel data extraction and delivery was tough and heavy on our infrastructure. There were records of hotels in millions and the image files in large sizes, which required efficient organization, verification and delivery. At this scale, it was nearly impossible to perform manual QA of such datasets and complete delivery with large files and records was impractical. 

4. The final nail in the coffin for Grepsr was maintaining the data freshness because hotels frequently change their rates as per season and demand, so historical, stale data we store in our database becomes rapidly useless for a client that wants real-time data. On the bright side, the static metadata were quite stable but they still needed timely refreshes to ensure the accuracy. 

The solutions 

Like how every cloud has a silver lining, we did come up with solutions and workarounds for each problem we faced. 

First, to deal with the large-scale global hotel data extraction, we set up structured data grouping and parallel execution. Our team scraped all city names and hotel counts from the booking website’s sitemap. 

Then, the cities were categorized into different groups and each of them had a dedicated crawler and report. This parallel structure helped in reducing the crawl time and maintained a steady flow of data to the client’s system without straining our infrastructure. 

Next, to solve the second problem of data discrepancies, we built a dataset synchronization between projects. Grepsr ran automated quality checks and audits to detect if the hotels present in one project, extracting prices, might be missing in the other one, extracting details. 

We also initiated a second crawl to fill any missing metadata fields to ensure that both datasets are aligned seamlessly. 

The third issue with high-volume extraction and delivery was taken care of by data chunking and scheduled reporting. As the name suggests, data chunking is the process of breaking large datasets into smaller, more manageable segments to improve processing efficiency, memory usage, and scalability. 

So our data pipeline went through restructuring to output segmented datasets instead of single massive files. Each chunk further underwent independent QA and validation, improving our accuracy and reliability. 

Finally, for maintaining data freshness, we established dynamic and on-demand refresh cycles. For the pricing data project, we scheduled bi-weekly crawls which would guarantee real-time rate visibility. Whereas, the content and hotel detail data extraction was done on an on-demand basis, in which updates were done only when it was necessary to optimize cost and efficiency. 

The final impact

Our efforts turned what began as a limited proof of concept into a global travel intelligence infrastructure for the travel tech startup. 

Here’s how the strategic automation of hotel rate and content extraction delivered transformative results for the client’s business:

• Data accuracy and reliability: The automation of the data extraction process ensured that hotel rates and metadata were accurate, real-time and aligned across both projects. This allowed the client to deliver more reliable insights to their partners and customers.
• Scalable global coverage: Scaling from 20 cities to more than 400 cities meant the client could expand their platform’s reach without the limitations of manual data collection. This enabled them to offer hotel pricing and content across destinations worldwide, opening up new revenue streams and market opportunities.
• Real-time pricing intelligence: The client’s platform now had the ability to provide real-time dynamic pricing data for hotels worldwide. This allowed hotels to adjust rates based on competitor data, market demand, and trends. With more competitive pricing intelligence, their partners could optimize their own offerings. 
• Streamlined operations and reduced costs: We helped them reduce the manual workload significantly, freeing up resources for more strategic tasks. The bi-weekly updates and on-demand content refresh cycles also ensured that the client’s internal operations were streamlined and costs were minimized.
• Long-term strategic growth: The successful implementation of these data-driven systems positioned the client for future growth and expansion. They could now scale their platform to handle even larger volumes of data and enter new markets more effectively.

Hence, by harnessing real-time data for smarter decision-making, they gained a competitive edge in the market, boosted operational efficiency, and ultimately scaled their platform globally with confidence.

Ready to Revolutionize Your Hotel Data Strategy?

Just like our client, you can harness the power of automated hotel rate and content extraction to gain real-time pricing insights, optimize hotel listings, and scale globally with ease. 

Whether you’re looking to boost direct bookings, offer competitive pricing, or streamline operations, Grepsr’s automated web scraping solutions can take your platform to the next level!

Contact us today!