The travel industry is now a part of the bandwagon of the various other industries that use big data analytics in their regular operations. People generate a lot of data in all their activities and decisions with activities ranging from searching for holiday destinations to buying rucksacks online. Even while traveling across the globe, people tend to leave a lot of trail on the internet.
While people generate a lot of direct data on the open-source internet voluntarily or involuntarily, there are several other unconventional data sources that the travel industry can use to make better decisions. These data-backed insights and analytics-driven decisions can help the travel industry grow and develop. Firms in this industry have started leveraging predictive analytics to predict upcoming travel trends and fix existing issues.
Big Data in Tourism: How Big Data analytics is impacting travel and tourism?
Big Data analytics is being used by the travel and transportation companies all around the world. For instance, airline operators use analytics to not only understand a passengers’ purchasing and travel patterns but also aggregated behavior insights about particular demographics. It can reflect on customers’ choice of travel destinations and times. Techniques like web scraping and social media listening can help us analyze the electronic Word-of-Mouth (eWOM) of various services and products in the industry. Highly scalable and enterprise-grade web data extraction platforms can be established and supported by companies like Datahut to scrape this information from multiple platforms and collate it in one structured data-set.
This will help companies get a thorough picture of the market, competitive strategies and the brand performance from one source of Big Data. While explaining the industry performance, on the whole, Big Data analytics also helps airline and railway companies in managing their revenue and strategic pricing operations. This enables them to maximize their income opportunities and design the best travel experiences for passengers. Big Data insights can also help transportation service providers improve their network connectivity according to the market demand. Product offerings, sales campaigns, and product improvement are some domains where analytics can be used in transportation and travel companies.
Big data analytics can also aid the hospitality department. Hotel chains are already using Big Data insights to create customized and relevant discount packages, add-on services and incentive coupons based on a customers’ travel patterns. Moreover, the tourism boards in a few countries are also turning towards Big Data applications to understand tourism flows and discover more investment opportunities in their country. In 2014, the Cuban government started using an analytics software to process the data generated by visitors on various social networks. They did so to quickly identify existing problems at the government-run hotels and tourist facilities.
International hotel chains like Starwood Hotels and Resorts have started integrating analytics for dynamic pricing to increase revenue and profit. For instance, the hotel chain has systems in the hotels that combine information like macroeconomic factors and local events to offer accommodation at competitive prices. Technology adoption and constant innovation have made it possible to provide real-time insights into a customers’ perception of the services, the overall health of the business and competitive performance in the market.
Using customer reviews and sentiments from social media comments can provide insights on what can be improved in the existing services or what they really appreciate. This would help the hotels market themselves better and attract more business. Furthermore, predictive analytics using statistical algorithms and machine-learning techniques allows business to extract and analyze information from newspapers, websites or videos to help operators track the latest developments in the industry and its environment.
How can firms build a data analytics muscle?
While firms can start understanding the importance of big data in tourism and start adopting data analytics in its regular operations overnight, it is important to first create a data-driven outlook for the organization. The travel industry needs to acknowledge the fact that data can provide statistical and factual support for their decisions. It can also help organizations gain insights into what the pain points are in the current scenario. Thus, an organization in the travel industry should start gathering different forms of data to start analyzing various aspects of their operations.
Website data scrapping plays a significant role as a source of information in the market share analysis and competitive pricing researches in the travel industry. Data scraped from public websites can be used to gain insights into the product portfolio being offered by the market. This will help travel and tourism companies to optimize their product assortment to increase revenue and profits.
Companies like Datahut offer web scraping services to diverse industries at competitive prices. The process of web scraping converts information provided on various websites into usable raw data format like ‘.csv’ and text file which can then be fed into models for data analytics and predictive analysis. Firms interested to get a competitive advantage should use web scraping as a tool to keep updating their product portfolio, offer competitive prices and stay ahead in the market.
Social Media analytics is another branch of analytics that helps organizations gather insights into the general customer sentiment about their respective brands and also about the current market trends. According to a report published by Amadeus in 2016, around 90 percent of US travelers with a smartphone use social media and reviews services to express their sentiment about travel, hospitality and holiday experiences. TripAdvisor has an average footfall of over 390 million unique visitors and 435 million reviews. This is a large pool of valuable customer data along with predictive analytics and machine learning can help brands revamp their market perception, hold their competitive advantage and even create recommendation engines for various product and service offering to the customers.
Some Success Stories from the Travel Industry
With the advancement and rapid development of technology, all domains and industrial verticals are using competitive and niche technologies in their operations. Market players in the travel industry have also joined the group of firms adopting these state-of-the-art technologies. Internet of Things (IoT) is one of the tools that most industries are trying to adopt and use for various business problems. In the travel and tourism industry, KAYAK sets the example for using big data innovation and spearheads the technological revolution.
With the use of predictive analytics modeling and IoT in the form of Alexa Skills, KAYAK has set a new benchmark for other companies in the travel industry. Users can use a simple voice query like, “Alexa, ask KAYAK where can I go on vacation in October for $1000” to get a list of viable holiday options with the necessary constraints applied. KAYAK integrated with Alexa Skills helps users to track flights, book hotels and search for holiday options. KAYAK is trying to improve this functionality by designing personalized recommendations with higher accuracy.
Airlines are increasingly tapping the unveiled potential of Big Data analytics to optimize their processes and improve personalized customer experience. United Airlines shows a “collect and analyze” approach to their data. The company tracks customer behavior using real-time data with more than 150 variables, including individual and general historical data. This large dataset forms the input for detailed customer segmentation and adaptive UX/UI designs in real time according to the category a particular user belongs to.
As an essential part of the travel industry, airports should also integrate analytics into their processes. Here, Big Data Analytics can be used to predict and optimize a large number of problems, like load distribution during peak hours, fraud and malpractice detection, route allocation or even intelligent reporting on the airports’ general performance. Amsterdam’s Schiphol Airport has reportedly fared well when it comes to customer satisfaction. The Schiphol Group, which operates the airport has invested in data science packages and a team of analysts fluent in R and Python to analyze, report and visualize the constant influx of data. They then use heat-maps to gain insights on how travelers travel through the airport, even calculating how far they tend to stray from their departure gate.
Some other examples show how insights from Big Data in tourism has helped various organizations in the travel and tourism industry.
- Wynn Las Vegas has integrated Amazon Echo speakers in all their rooms.
- Safeco Field Suits has taken it a notch higher and uses it to guide guests on what to do in the city during their stay. Using the facial recognition technology, Lemon Tree Hotel in Deli has enhanced security. This system captures facial images from the CCTV cameras in and around the hotel and compares them to existing images in the database, to flag any suspicions.
- While Radisson Blu Edwardian Hotel in London uses a chatbot named Edward, Las Vegas Hotel Cosmopolitan has Rose to act as a virtual concierge that uses Artificial Intelligence to answer any queries and provide round-the-day support.
Due to the growth of analytics in the industry, Amazon plans to launch a customized version of Alexa for the hospitality industry in 2018. These pursuits and developments are reflective on the growing analytical trends of the travel and tourism industry. The other organizations in the industry thus, should adopt, improvise and use analytical tools to make more informed decisions.