Artificial Intelligence and Machine Learning-Revolutionizing Traveling
There is a prevalent behavioral trend among travelers when it comes to choosing the right airline or travel agency—going for the cheapest option. This option remains the same irrespective of the duration of layovers and available seating. According to a research, travelers fall under the segments of digital natives, family travelers, youngsters, middle-aged and senior citizens. Although digital natives and youngsters comprise less than a quarter of the traveling population, they have been identified as those with highest Internet activity.
Working with Online Travel Agencies (OTA), a data science team gathered the data of interaction with travelers and tracking their activity on the basis of destination exploration, the spots clicked, chosen data of travel, and even the parameters used to choose service providers. This was done using a user behavior tracking (UBT) app which merges data that can be used by data scientists to create algorithms and models for prediction. Machine learning plays a critical role in this task by understanding the likeliness of a customer to buy and providing options relevant to their budget and location preferences.
The idea of travel personalization takes a deeper dive when focusing on the non-economical buyers, where cheaper rates are immaterial. The travel suggestions may range from preferences of layover durations, meals, and hotel chains. The value-add options are the key deciding factors for these travelers, who are predominantly business travelers. More than three-fourths of the OTA’s believe that the ability to personalize the search results makes it easy to reach customer goals.
Metadata can prove to be helpful to make valid assumptions about the users, like the websites that led them, the device, and the browser that was used. The complete power of AI with regard to traveling can be unleashed once the balance between user’s privacy and the data collected is struck.
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