Forget about Minority Report and its sexy gesture interface - predicting the future is very different from what you see in the movies.
In reality, it’s all about finding patterns in a vast amount of data. Applying the right statistical models allows you to gain insights from the information at your disposal. The hidden patterns unveiled by the process makes it possible to make predictions. This is what we call predictive analytics. This is how the retail industry is able to predict what customers buy according to the time of the month or other items they have just purchased.
In the travel industry, predictive analytics has many uses. The incredibly large amount of data, combined with predictive modelling, unlocks a realm of possibilities for airlines, airports,travel agencies... and of course the travellers.
Here are a few examples using predictive analytics components:
Recommender systems for travel products (e.g., hotels, flights, ancillary services)
Video, image, and voice recognition systems for travel purposes
Click and conversion optimisation for travel products and online-advertising campaigns
Social media analysis (e.g., sentiment analysis and profiling)
Alerting and monitoring
The travel industry generates huge volume of data. For example Amadeus process more than 1 billion transactions per day in one its data centres. New aircraft have close to 6,000 sensors generating more than 2 Tb per day. Obviously this data cannot be analysed by human beings. Using supervised machine learning algorithms, known defects canbe anticipated when a combination of factors are observed much like how a set of symptoms helps doctors diagnose a particular disease (with some probability). On the other hand,unsupervised learning algorithms have helped detect anomalies to generate alerts when some data observation become suspiciously rare.
Have a look at5 more examples of predictive analytics in travel industry and let us know what you think by leaving a comment below orsending a Tweet .
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