Consumer choices have markedly changed in the modern era, shifting significantly to modern digital means from conventional ticketing outlets. Smartphones have revolutionized the convenience of travel, enabling individuals to plan and book entire travel journeys effectively with a few clicks. Of course, this digital transformation goes beyond just ticketing, asserting a massive change in consumer behavior towards a more streamlined, technology-driven travel planning and booking process.

In 2022, online sales channels dominated the world travel and tourism market, contributing over two-thirds of the estimated $475 million. It is further predicted that by 2030, the market will have surpassed $1trillion. In conjunction, the realms of service excellence and big data have seen a gradual rise. Therefore, these online bookings serve as rich sources of data collection and analytics.

Big data and analytics in travel

Data analytics play a huge role in the highly competitive travel industry. Travel companies are looking to extensive data, including passenger preferences, booking patterns, and more, to optimize their businesses. Big data can predict and provide exactly what the client requires, as well as identify areas for improvement.

Travel companies can use data analytics to look at buying patterns in real-time, offer dynamic pricing alternatives, augment customer service, and anticipate issues by drawing the right inferences from the analysis. Agencies can also offer personalized packages and tailor-made services to increase client loyalty.

In the travel industry, particularly, data analytics offers an opportunity for providers to shift from a reactive to a more responsive approach. Businesses can project and reshape their operations based on analysis, especially at a time when customers are calling for more efficient and customized services. In a competitive world, however, travel companies must seize every opportunity they find to stay ahead of the race.

Benefits of data analytics

• Improved customer experience
• Projections and insights
• Better pricing structures
• Enhanced operational efficiency
• Risk Management

Forward-thinking organizations in the travel sector use data science, machine learning, and analytics constructively, even to create custom software that infers for them.

TOP 4 Travel Analytics Use Cases

Tools to forecast fares

The costs of lodging and travel vary according to the chosen supplier. However, keeping track of these manually can be a tedious undertaking. As a result, the travel industry is always on the lookout for intelligent solutions that can track and promptly notify users about fresh discount availability. For instance, a sophisticated forecasting technology accessing vast amounts of past pricing data can provide projections after conducting a thorough analysis. Future price changes may be predicted based on demand, seasonal patterns, airline promotions, and other variables.

Managing Disruptions

It takes a lot of advance planning and feedback, particularly from customers, to overcome issues that might arise during travel. Disruption management, on the other hand, solves actual problems on the go - it resolves problems encountered by travelers during their journey. The tool can be primarily employed in business and corporate travel where prompt task execution is necessary. For instance, the likelihood of experiencing a volcanic eruption or hurricane is extremely low - however, there is a real possibility of travel interruption due to these natural causes, where flights may be canceled or delayed.

In the realm of travel business operations, being stranded when one should be en route to a destination poses a considerable risk. The potential repercussions extend beyond mere inconvenience, translating into substantial financial losses and detrimental effects on the overall organizational performance.

Customized offers for loyal customers

The tourism and hospitality industries are known for their loyalty programs. Statistics indicate that in 2016, there was a 13.1% increase in hotel loyalty program members or an addition of around 344 million members. Travel companies can apply all the historical data from loyalty programs to AI-based customization, which will help memberships and allegiances rise.

Besides, to reduce attrition rates, travel companies must prioritize their most valuable clients and members within their loyalty programs. This is also another area where machine learning can be extremely helpful. It might not be enough to provide customers with offers based on past behaviors and metadata. It is the accuracy of forecasts that matters, where historical and real-time data must be combined to make predictability and offer management extremely easy

Sentiment analysis

Sentiment analysis is a novel and effective method of determining precisely what your customer desires. - you can get to know this by conducting review analyses of posts and travelogues to identify their true requirements. Most of the tourists share their trip experiences on social media and provide reviews of services using smartphones. There are several examples of such data available online on other popular platforms.

Brands must take the initiative to examine this vast collection of insightful data to enhance their offerings. All brand-related reviews can be analyzed using computational power and underlying machine-learning approaches.

Big data in travel - Looking ahead

One of the most exciting areas in travel is data science and analytics, which has the potential to revolutionize the travel industry. There are various types of data available, such as customer feedback, social media posts, geospatial data, and other external forms. While challenges to data privacy cannot be ignored, governance is crucial in overcoming these hurdles to bestow effective change management.