Who doesn’t love a vacation?? Taking a brief breather and escaping from our busy lives is a rare treat we wish to reward ourselves with often. But have we ever wondered about what exactly goes on behind the scenes in the tourism industry? How the decisions and operations in the industry are driven?
Being a wide-ranging industry, the tourism industry incorporates various diverse sectors under its umbrella, becoming a pivotal mode of livelihood for each of these sectors. Broadly, these include the transportation sector, the entertainment sector, the accommodation sector, the food, and beverage sector, and all its connected industries. One common aspect which is involved and has laid its paws in each of these sectors is Big Data.
In this blog, we will understand what Big Data is, the areas where it is used, the opportunities it offers as well as the challenges it poses.
Big Data, which is data in large volume, plays a vital role in analyzing the different trends of travelers by comprising the information obtained via various consumer centers and utilizing it to establish a definite marketing approach that can be applied to the target audience.
Big Data technologies which include Hadoop, MongoDB (You can take a look at our blog on Hadoop vs MangoDB to get a better understanding of the topic), Rainstar, and cloud-based analytics provide sufficient space for data storage. These technologies display the information gathered from a large range of sources in an organized and structured manner that aids the businesses involved in the travel and tourism industry to take prompt decisions in accordance with the varying demands of the consumers.
One useful method is Website data scraping. In this process, the information collected from the various websites is converted into a raw data format such as ‘.csv’ or a text file which is then fed into models for the analysis of the data. With this the data scraped from public websites can be used to collect insights and information for the creation of a product portfolio to be released into the market.
Another useful method of the same would be Social Media Analytics. Here the views and opinions of the general customers regarding a particular brand of the company are gauged and analyzed.
You can also sneak a peek at our blog on 7 tools of Marketing Analytics
Big Data is being effectively utilized in a variety of areas in tourism. These include:
Travel bots: A recently introduced feature, travel bots are chatbots that provide either automated customer services on websites of travel companies or operate through messaging platforms such as Facebook Messenger to interact with travelers and assist them in their bookings.
Powered by Artificial Intelligence, supported by multiple languages, and accessible 24/7 the Travel bots are capable of answering queries, working efficiently and saving time and money of the user, organising the trip and even of offering suggestions.
An example of a travel bot is KAYAK which is available on Amazon Echo, iMessage and Messenger. KAYAK combined with Alexa Skills helps users in tracking flights, booking hotels and searching for holiday opportunities.
Uses of Big Data in Tourism
Personalized Marketing: With the target audience being so diverse and mixed the proper use of Big Data becomes crucial. Big Data assists in comprehending the demographics of the target audience along with the geographical, behavioural and psychographic factors involved in order to identify marketing opportunities as well as construct a fitting marketing strategy. This allows for more targeted and personalised promotional content to be circulated.
Boosting Customer Experience: The proper utilization of Big Data assists in enhancing the experience of the customers. Data procured from the customers of their sentiments regarding that particular brand range between conversations on social media reviews uploaded online as well as the data obtained of services used. This information gives clarity on which services are used the most, which get used rarely, and which ones are likely to be sought. Thus this data allows companies to comprehend which areas they need to invest in as well as which new services they can choose to introduce.
Optimization of Route: Planning of a trip while taking into consideration a variety of factors like the destinations, the schedule of the traveller, his/her working hours as well considering the distance can get testing. This is where Route Optimization comes into play. Its main aim becomes minimizing the cost and distance of travelling as well as managing the time of the journey effectively in order to fully gratify the customer.
Predictive analytics: The price-fixing is done taking into account a variety of factors such as the conditions of the weather, season, availability of places, rooms and seats.
Speaking of weather conditions, you can also sneak a peek at our blog on how weather forecasting uses big data analytics.
Self-learning algorithms become a tool for accumulating and compiling data from the past, taking the external factors into consideration in order to predict the price fluctuations in the future.
Some duties which this analytics is assisting with are ensuring that there is uniformity in the details provided on official websites of organization and the third-party providers for booking, in cutting down on discounts for the days where special promotion is not needed, and in increasing the rates of the weekend.
Airline: Big Data enables the airline operators to develop an understanding of the behavioural patterns of passengers, to pinpoint the travel mode and time they prefer, to develop an understanding of the overall performance of the industry, widen their network connectivity and also to assist in the handling of revenue.
The Big Data helps in determining the pricing approach to be incorporated and in identifying the various emerging trends in the industry to decide the appropriate response.
Hotel chains: Big Data has proved to play a large role in the improvement of hotel services. It is used by hotel chains in order to create customised packages, to offer a variety of travel-specific discounts and for supplying add on services.
One of the many examples which sheds light on the application of Big Data analytics in hotels is the international hotel chain of Starwood Hotels and Resorts that applied the analytics of Big Data by combining the information on macroeconomic factors and local events to establish a strong pricing strategy for raising the revenues.
Tourism Boards: Big Data is also being integrated by tourism boards to understand the industry’s performance and to highlight the areas where further investment can be made in the field.
Big Data while holding endless scope and opportunities for the Tourism Industry, also poses some challenges in its operations particularly when it comes to the storing, handling, securing and ownership of the data. Some of these challenges are listed below:
Privacy and Security: Data of private customers often gets passed on for research, polling or a variety of purposes from one organisation to another without the knowledge and consent of its users leading to a breach in the privacy of the customer which heightens the plausibility of misuse of personal data. Big Data is also susceptible to hackers and cyber attacks with security control not always being sufficient thus posing a question on the confidentiality and integrity of Big Data.
Data Ownership: The ownership of the Big Data poses a crucial, complex challenge. The European Union Law remains ambiguous and unclear regarding the same.
It is generally assumed that both the website owner and the user own the data. Thus Data Ownership becomes a complex issue which poses dilemmas regarding the control and accuracy of the data for organisations as well as legislators.
Challenges faced with Big Data
Data Handling: The existing databases while being large and complex, cannot handle such a large degree of data. While relational databases which are utilized by most tourism companies divide the big data into rows and columns to organize it, the same approach is difficult to use with the different data formats.
This enhances the need for more advanced tools for processing and storing and more advanced software to deal with big data. Proper handling of the data also requires skilled and trained personnel having expertise in the areas of applied mathematics, algorithms as well as in Big Data processing platforms, and numerous complex tasks.
Data storage: There are two kinds of data collected by the storage company - structured and unstructured, for example, different types of data stored in SQL and NoSQL databases. While the structured data, being easier to collect and mounting for 25% of the total data is procured from websites, blogs, and property management software utilized in hotels, unstructured data is more complicated.
It includes the data obtained from social media sites like Facebook and Twitter, user-generated content forums like Yelp and Trip Adviser, emails, photos, videos, and any other content affecting the online reputation and goodwill of the company. Combining and analyzing this data becomes another challenge faced by tourism companies.
This article talks about Big Data and its significance in the Tourism Industry. It sheds light on what Big Data is and highlights a few of its technologies. It discusses the application of Big Data in Tourism, talks about the various tourism sectors which incorporate it and lastly, it mentions a few of the challenges posed by the application of Big Data in the Tourism Industry.
6 Major Branches of Artificial Intelligence (AI)READ MORE
Reliance Jio and JioMart: Marketing Strategy, SWOT Analysis, and Working EcosystemREAD MORE
Top 10 Big Data TechnologiesREAD MORE
8 Most Popular Business Analysis Techniques used by Business AnalystREAD MORE
Deep Learning - Overview, Practical Examples, Popular AlgorithmsREAD MORE
7 types of regression techniques you should know in Machine LearningREAD MORE
7 Types of Activation Functions in Neural NetworkREAD MORE
What Are Recommendation Systems in Machine Learning?READ MORE
Introduction to Time Series Analysis in Machine learningREAD MORE
How Does Linear And Logistic Regression Work In Machine Learning?READ MORE