How can the travel industry benefit from data scraping
Python, Web scraping

How can the travel industry benefit from data scraping

The travel industry is a major service sector in most countries these days. It is also a major employment and revenue provider. This demands a lot of constant innovation and maintenance. The travel industry is a dynamic industry where the needs and preferences of a customer change every moment. The market players in this field need to keep up with the trends in the industry, the choices of the customers and even on the details of their own historical performance to perform better as time progresses. Thus, as you would presume, the companies working in the travel sector need a lot of data from multiple sources and a pipeline to assess and use that data for insights and recommendations. One of the most popularly adopted methods for gathering these kinds of data is data scraping from the Web and other alternate sources. Data scraping is an integral part of most industries and businesses that follow a data-driven culture. The travel industry has also benefitted from the same in more ways than one. Let’s talk about a few of these benefits. Before that, let us talk about what web scraping can help in and how you can use it efficiently in the travel and hospitality industry.

What are the uses of Web Scraping in the Travel Industry?

Data is vital to solving the important problems that the travel industry is facing currently. Data scraping can help you understand the industry and market trends on travel fares, updated travel itineraries and destinations, new flight and transportation carriers and even details on flight delays, airports and train station updates among other details. You can also scrape data on customer feedback, customer preferences and even their sentiment. This data is often publicly available on most travel sites and aggregators. Such data can also be extracted from social media websites.

As we mentioned in our earlier posts, this data can help travel agencies, hotels, travel aggregators and service providers provide the best possible options to the customers. It will also help boost travel and tourism activities by improving the overall sentiment of the customers. This data can serve a variety of purposessimple listings, price comparison of various airlines, for descriptive and inquisitive analytics like market share analytics, analyze the patterns of travellers and their preferences to help design services that boost revenue and income for the business. Let us now look at a simple framework which we can use to scrape data from a travel service aggregator- MakeMyTrip.

Framework of a typical data scraper that can be used by the Travel Industry.

We will talk about the algorithm that could scrape flight details from MakeMyTrip. If you want to scrape the details of all flights plying between multiple combinations of a given list of cities on a particular range of dates, you first need to initialize these details. A simple code to do so is:

script1-1
Python script to create a list of city codes and dates in string format

Once we have the city codes and the dates’ list in the form of an array, we now need to loop through these to form the complete URL (or website link) to pull the information from. We do this through a loop. It has a neat logic that maps every city to a different city in the list provided by you and then loops through each date in the list. These string values are then appended to the base template of the target URL or the site that you want to extract information from. This can be done so:

script2
Script to complete the URL string

Once you have the complete URL, there are 5 basic steps:

  1. Open the webpage defined by the URL
  2. Once the information has started loading on this webpage, find the document body and save its HTML structure. This will have all the information you need.
  3. Close the link
  4. Parse the extracted information in an organized and more structured manner such that you can access the tags and elements in the webpage.
  5. Extract the required tags to get the appropriate values.
  6. Extract this data and save it in a structured file format- like a CSV or a text file.

These steps have been executed using a piece of simple Python code.

script3
Script to access the URL and parse the copied information from there

There are several platforms and libraries that can do this task for you in no time. We used Selenium and BeautifulSoup in Python for the same. We pulled the following details:

  • Airlines Name
  • Flight Code
  • Arrival City and Time
  • Departure City and Time
  • Flight Duration
  • Flight Cost.

We then stored this information in the CSV format.

script4
Script to extract and store information in a CSV file

Advantages of Data Scraping for the Travel Industry

Now that we have covered how to extract information from a given target site, let us see how data scraping is advantageous.

  1. Web scraping is a quicker process when compared to other conventional methods of collecting data from sources like the Internet.
  2. You can extract information in any desired structured format like CSV, XML or Excel files. You can also upload it to databases like SQL.
  3. Most of the data extracted through web scraping can be used directly without much additional processing. The quality and veracity of this data make it suitable for multiple business problems.
  4. The accuracy of scraping data in an automated algorithmic manner is higher as it is not subjected to human error and ambiguity.

It is also imperative we talk about a few sample problem statements and case studies in the travel industry, that can use data that is scraped from multiple web sources.

Sample problem statements that use scraped data in the Travel Industry

  1. Comparative Price Analysis– You can extract prices of various airlines, hotels, bus services or any other travel and hospitality service that is relevant to your business. Once you have this data, you should analyze your prices against the general market pricing and design recommendations of tuning the prices according to your competitors’ trends.
  2. Market Share Analysis– A lot of travel companies use data scraping to conduct a market share analysis and assess their brand against the competitors. They use sales and profits data and other macroeconomic data. Some of the big travel firms use scraped data to learn about their income and their drivers.
  3. Design effective Marketing Strategies– Some companies scrape data from multiple customer feedback forums and social media websites to learn about the general sentiment of a customer about a service, offer or a product. These insights can then be used to design effective marketing strategies that target customers according to their preferences or improve service and product offerings to meet customer demands.
  4. Predict performance/occupancy of hotels/flights in a given season– If you are running a hotel chain or a travel aggregator/ service provider, you might want to predict how much traffic you should expect in a certain period. This will help you allocate resources and design marketing strategies accordingly. You can use historical data on the number of reservations and successful itineraries for the same.

You can also use scraped data to set up a proper database that can connect to your dashboards and your analytical pipelines for all further purposes. There are multiple other problems that scraped data can help you solve.

Conclusion

While data scraping can help you solve various problems, you need to be careful about the logistic issues. You should be careful about gathering the necessary information in a legal manner with all required permissions. There are certain aspects that make data scraping a legal process. You should comply with all of those.

Data scraping has been beneficial to the travel industry in a lot of ways. There are a lot of professional data scraping firms that offer dedicated data scraping services. We at Datahut have helped several businesses across various industries to gather data and solve vital business problems with the same. We have a transparent process that provides you with data in your preferred manner.

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