Using Residential Proxies to Capture Travel Data and Improve the Competitive Advantage of the Tourism Industry

In today's increasingly fierce digital competition, data has become the core resource for decision-making and operations in the tourism industry. Whether it is flight prices, hotel room prices, or customer preferences and market trends, the collection and analysis of real-time data can not only help companies optimize pricing strategies and improve customer experience, but also provide important basis for formulating marketing strategies and responding to market changes. However, the complex anti-crawler mechanism of tourism websites and the diversity of data formats have brought huge challenges to data collection. This article will explore how to build a powerful real-time network data extraction system to help tourism companies efficiently obtain and analyze key data and achieve all-round business optimization.

Why Does the Tourism Industry Need to Collect Data?

In today's digital age, data has become an important resource for companies to gain competitive advantages. Real-time data collection in the tourism industry can not only improve the operational efficiency of companies, but also provide data support for the long-term development of companies. By collecting and analyzing tourism data, companies can better understand market trends, consumer preferences, and competitor strategies, thereby optimizing services, improving user experience, and ultimately maximizing profits.

• Understanding market demand: The market demand of the tourism industry is affected by many factors, including seasonal changes, holidays, economic environment, etc. Through market research, companies can understand which destinations, activities or services are most popular in a specific period. This helps companies optimize products and services and develop more targeted marketing strategies to meet consumer needs.
• Develop marketing strategies: Tourism companies need to develop strategies for different markets and customer groups. By collecting market share, competitor activities and consumer behavior data, companies can develop more effective marketing strategies, increase brand exposure, and stand out from the competition. For example, by monitoring flight prices and hotel reservations in real time, companies can discover changes in market demand and adjust their marketing strategies.
• Responding to market changes: The tourism market is affected by many external factors, such as weather, political events, and public health crises. Real-time data collection can help companies respond quickly to market changes, adjust operational strategies, and ensure business stability and continuity. For example, during the epidemic, data analysis helped companies understand changes in travel restrictions and customer demand, so that they could make corresponding adjustments.
• Improve customer experience: Data collection can help companies gain a deeper understanding of customer preferences and needs, thereby providing personalized services. By analyzing customer feedback, comments, and behavioral data, companies can identify customer pain points and expectations, improve service quality, and increase customer satisfaction and loyalty.

What Tourism Data Are There?

In the travel industry, channels such as online travel agencies, hotel and airline websites, social media platforms, customer feedback systems, etc. contain a large amount of data, covering everything from market trends to customer experience. Here are some of the main types of travel data:

• Price data: including flight prices, hotel room rates, car rental fees, etc. This type of data is crucial for tourism companies to formulate pricing strategies and market positioning.
• Booking data: including booking volume, booking time, cancellation rate, etc. This type of data helps tourism companies understand market demand, optimize inventory management and improve customer service.
• Customer data: including customer personal information, customer preferences, customer reviews and feedback, etc. This type of data is used to improve customer experience, develop personalized marketing strategies and optimize service quality.
• Competitor data: including competitor prices, market share, marketing strategies, etc. This type of data is used to conduct market competition analysis, adjust own strategies and identify business opportunities.
• Geographic location data: including tourists’ origin, geographical location distribution, etc. This type of data helps tourism companies to segment the market, formulate regional marketing strategies and optimize customer service.
• Market trend data: including destination popularity, seasonal changes, tourist traffic, etc. This type of data helps tourism companies grasp market trends, formulate marketing plans and optimize resource allocation.
• By comprehensively analyzing these data, tourism companies can gain in-depth market insights, optimize operational strategies, and improve customer satisfaction.

What Are the Challenges in Data Collection?

Collecting data in the travel industry, companies often face a number of challenges that can limit the availability and quality of data. Here are some common challenges:

• Anti-bot measures: Many travel websites implement complex anti-bot techniques to prevent data from being scraped by automated tools. These techniques include IP blocking, JavaScript challenges, dynamic content loading, captchas, etc. These protections make automated scraping difficult and may result in scraping requests being blocked or failing.
• Diversity of data formats and structures: Different travel websites may use different data formats and page structures, which increases the complexity of data crawling and integration. Some websites may use custom HTML tags or complex JavaScript scripts to display data, making parsing and extracting data require special adaptation and processing.
• Data update frequency: Data in the travel industry (such as flight prices, hotel room rates) often changes frequently. In order to maintain data accuracy, real-time or near-real-time data capture and update strategies must be implemented. This requires the capture system to have efficient scheduling and data synchronization capabilities to cope with rapidly changing market dynamics.
• Big data processing: The amount of tourism data is usually very large, including flight, hotel, car rental and other information. This data requires efficient processing and analysis capabilities, involving challenges in storage, computing and data transmission. Processing large-scale data sets requires strong technical infrastructure and data processing capabilities.
• Dynamic content loading: Many modern travel websites use dynamic content loading technologies (such as AJAX, JavaScript) to update page content. The initial page load may not show complete data, which requires the crawler to be able to handle dynamically loaded data to ensure that the latest information is obtained.

Building a Real-Time Network Data Extraction System

In order to effectively deal with the above challenges, tourism companies need to build a powerful and flexible real-time network data extraction system. The core architecture of this system includes the following steps:

Step 1: Create a URL template

First, companies need to create URL templates for each target website. These templates will help crawlers generate query links to obtain specific data. For example, when crawling flight data from a travel website, the URL template may contain variables such as departure and destination, date range, etc.

from datetime import datetime, timedelta

# Set the departure and destination
origin = "NYC"
destination = "LAX"

# Set the date range
start_date = datetime(2024, 9, 1)
end_date = datetime(2024, 9, 7)
delta = timedelta(days=1)

# Generate URL list
urls = []
current_date = start_date
while current_date <= end_date:
formatted_date = current_date.strftime("%Y-%m-%d")
url = url_template.format(origin=origin, destination=destination, date=formatted_date)
urls.append(url)
current_date += delta

# Output the generated URL
for url in urls:
print(url)

Step 2: Configure residential proxy

After setting up the URL template, using a residential proxy for web crawling can not only improve crawling efficiency, but also help you circumvent the anti-crawler mechanism of the target website, thereby obtaining more comprehensive and accurate data. For example, use 911 Proxy to obtain the IP and port, and use the requests library in Python to configure a residential proxy.

import requests

# Proxy server IP and port
proxy = "http://username:[e-mail]:12345"

# Setting up the proxy
proxies = {
"http": proxy,
"https": proxy,
}

# Make a request with a proxy
url = "https://example-travel-site.com/flights?from=NYC&to=LAX&date=2024-09-01"
response = requests.get(url, proxies=proxies)
response.raise_for_status()

# Print the returned HTML content
print(response.text)

Step 3: Data capture and analysis

To improve the success rate of crawling, you can rotate the IP address and use Selenium to simulate browser behavior to circumvent the website's anti-crawling mechanism. After obtaining the HTML content of the page, you can use the BeautifulSoup library to parse the page and extract useful flight information from it.

from selenium import webdriver
from selenium.webdriver.common.proxy import Proxy, ProxyType

# Configure browser options
options = webdriver.ChromeOptions()
options.add_argument('--proxy-server=%s' % proxy.http_proxy)

# Start the browser
driver = webdriver.Chrome(options=options)

# Visit the target website and grab flight data
driver.get("https://example-travel-site.com/flights?from=NYC&to=LAX&date=2024-09-01")

# Get page content
html_content = driver.page_source
print(html_content)

# Close the browser
driver.quit()

from bs4 import BeautifulSoup

soup = BeautifulSoup(response.text, 'html.parser')

# Example: Extracting flight information
flights = soup.find_all('div', class_='flight-info')
for flight in flights:
flight_number = flight.find('span', class_='flight-number').text
departure_time = flight.find('span', class_='departure-time').text
arrival_time = flight.find('span', class_='arrival-time').text
price = flight.find('span', class_='price').text
print(f"Flight {flight_number}: {departure_time} - {arrival_time} | Price: {price}")

Summarize

By building a comprehensive real-time network data extraction system, enterprises can fully grasp market dynamics, optimize operational decisions, and thus maintain competitive advantages. With a powerful network data extraction system and flexible residential agents, travel companies can more effectively obtain market insights, Improve operational efficiency and ultimately maximize profits. Through the discussion in this article, we hope to help tourism companies better understand and apply these technologies, so as to achieve long-term business development.