Travel, the future is in Depp Learning

travel deep learning

There is no doubt that the travel industry is among those most severely impacted by the Covid-19 pandemic. Locations generally impacted by large tourist flows have been deserted for a long time and, in general, tourists have been hesitant in choosing their vacations due to the restrictions still in place. Nonetheless, at a domestic level, recent weeks have seen a growth in bookings that can be considered encouraging. According to ISTAT, the figures speak in fact, for the tourism segment, an index relating to consumers equal to 86.1, against 54.6 last month. The highest value of 2021 six were recorded in February, pre-pandemic when however it had stopped at 65.9. The transport sector, on the other hand, rose from 104.7 last month to 120.3 in May. Here, too, the highest value to date was that of February, when the index stood at 96.

Travel, deep learning as an ally

Overall, the data capture a sector that seems to be regaining confidence in the future, probably also in light of the reopenings and easing of restrictions. This, then, is a crucial time to get back on track and secure an edge over the competition for the months ahead. One sound strategy is to leverage the advanced capabilities and application possibilities of deep learning to define an innovative, data-driven way to reach potential customers.

There are many reasons why deep learning is the best technology approach for this industry.

  1. First, deep learning is by nature suitable for cases where large and consistent datasets are available. In these cases the algorithms can be “exercised” (training phase), to identify patterns of recurring behaviour and suggest actions aimed at intercepting demand, maximizing or predicting it. In the current scenario, deep learning can be used to relate the level of restrictions recorded over time and in different locations to the trend in demand, the trend in the curve of contagions and other impacting variables. Using this information, for example, it is possible to predict demand trends in a given destination and with specific restrictions in place. This information would make it possible to identify potential target clients and intercept them through an optimized price or a package that meets the expected needs or by implementing specific marketing actions.
  2. For some time now, large players in the tourism sector have been using machine learning and deep learning in various fields. For example, Airbnb has implemented algorithms based on the use of neural networks to optimize relevance in searches and increase bookings, or uses machine learning algorithms to suggest solutions to customers based on previous purchase behaviours and customizes suggestions according to this evidence.
  3. At a general level, artificial intelligence can help those working in the tourism industry to achieve three main goals: to identify their potential customers, based on the expected conversion rate; to obtain a deeper knowledge of their customers and identify the elements on which to leverage in terms of pricing to maximize their margins; to optimize investments in advertising and marketing based on the available data. In this way, you can increase conversions by identifying potential customers and offering them the solution that best meets their preferences.

Travel, deep learning advantages

By now, many travel companies have found deep learning to be the key ingredient to their success. Through this approach, you can gain an advantage to intercept more quickly and effectively their potential customers, optimize marketing investments and predict the trend of demand according to the “new” variables impacting this post-pandemic phase.

Using Artificial Intelligence and Machine Learning makes the company extremely reactive in responding to new market needs. Players that can exploit the data at their disposal to create sophisticated artificial intelligence algorithms acquire a competitive advantage that can make their business in perfect timing with the evolution of demand, today more than ever dynamic and complex to predict.

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