Data science has brought new marvelous opportunities to many industries. Along with these possibilities it has also brought constant changes and challenges. Travel and tourism industry is no exception here.
Travel is on its rise nowadays. This may be explained by the fact that it has become affordable to a broader audience. Thus, the target market has changed dramatically by getting more extensive than ever before. It is no more a privilege of the rich and noble. Moreover, travel and tourism have become a worldwide trend.
To satisfy all the needs of the growing number of consumers and process enormous data chunks the data science algorithms are vital. Big data becomes a critical tool as far as airlines, hotels, reservation and booking websites and many others are striving to improve their services every day. Let’s consider several of widespread and efficient data science use cases in the travel industry.
People in some way tend to appreciate travel experience personalization. Customer segmentation entails dividing all your customers according to their preferences and adaptation of the general stack of services to satisfy the needs of every group. Thus, the key idea is to find one solution that would fit all cases. In its turn, personalization is a trick that allows providing a specific service to a particular person. Thus, personalization makes this process deeper.
Personalized marketing and customer segmentation are all about collection users behavioral and metadata, CRM data, geolocation, social media data to unify, process and assume the user’s preferences in the future. For the travel industry this knowledge is essential.
Sentiment analysis is a branch of unsupervised learning aimed at analyzing textual data and recognizing emotional elements in the text. Sentiment analysis allows the company owner or the service provider to learn about the real attitude of the customers towards their brands. Regarding the travel industry, customers reviews play a huge role. Travelers often read reviews posted on various web platforms and websites and make decisions on their basis. That is why a lot of modern booking website offer sentiment analysis as a part of their service package for those travel agencies, hotels and hostels eager to cooperate with.
Some specialists often regard this use case as one of the most efficient and promising. Significant travel and booking web platforms are actively using recommendation engines in their day-to-day work.
These recommendations are often provided by matching the client's wishes and needs with the available offers. Generally speaking, applying the data-powered recommendation engines solutions the travel and tourism companies can offer the rental deals, alternative travel dates, new routes, destinations and attractions based on previous search and preferences. Due to recommendation engines the travel agencies and booking service providers can make suitable offers to all their customers.
Route optimization plays a significant role in the travel and tourism industry. Trip planning, taking into account different destinations, schedules, working hours and distances may be quite challenging. Here comes travel route optimization.
Key objectives of this optimization are as follows:
Thus, travel route optimization largely contributes to customer satisfaction.
Nowadays, travel bots are truly changing the travel industry by providing exceptional assistance in travel arrangements and support for the clients. An AI-powered travel bot can answer questions, save user’s time and money, organize the trip and suggest new places to visit. The 24/7 accessibility mode and support of multiple languages make a travel bot the best possible solution for customers support.
The most important factor to mention here is that these bots are constantly learning, therefore they become smarter and even more helpful every day. Thus, chatbot is capable of solving major travel and tourism tasks. Integration of a bot into your website would prove to be very beneficial. Such companies like JetBlue, Marriott, Ryanair, Hyatt, Hipmunk, Kayak, Booking and many others know this for sure.
In getting competitive advantages the companies seek to use big data with maximum benefit. In making decisions and actions travel and tourism companies largely rely on analytics. Both real-time and predictive analytics have many applications in the travel industry.
One of the most vivid use cases of real-time analytics in travel is tourism analytics. Tourism forecasting models allow predicting travel activity for specific periods and customer segments. Their principal task is to identify long-term and short-term opportunities for new deals. Due to the analysis of the previous clients’ activities, preferences and purchases the companies can predict future opportunities for business expansion.
Predictive analytics finds its implementation in dynamic pricing and fair forecasting. The practices of dynamic pricing and fair forecasting are not new to the travel industry. Every year more and more companies apply this technique to attract as many clients as it is possible.
As we all know the prices are the subject for the continuous changes depending on the season, weather, provider and the availability of places, seats, and rooms. With the help of smart tools, simultaneous monitoring of these price changes on multiple websites becomes possible. Self-learning algorithms are capable to collect historical data and predict future price movements taking into account all the external factors.
For instance, in the hotel industry these algorithms are often used to carry out the following tasks:
Data science is changing the face of the travel industry. It helps travel and tourism businesses to provide unique travel experience and high satisfaction rates, preserving personal touch. In recent years data science has become one of the most promising technologies bringing changes to various industries. It has shifted the way we travel and our attitude toward traveling arrangements. The use cases presented in this article are only the tip of an iceberg. With a vast variety of solutions provided by the application of data science and machine learning, travel business can learn their clients’ needs and preferences to provide them with the best possible services and offers.