Summer is approaching, bringing with it a new holiday season for the tourism sector that promises to follow the trends of 2024. According to the 2024/2025 Winter Season Report prepared by ObservaTUR, the National Outbound Tourism Observatory, the tourism sector in Spain continues to evolve—not only towards a more sustainable model, but also one focused on offering increasingly personalized and technology-driven experiences.

In addition to the rise of responsible tourism, the report identifies three major additional trends: the return of early bookings, the rise of immersive technological experiences, and the growing personalization of travel. In the latter two areas, Decision AI—a branch of Artificial Intelligence that enhances decision-making—can play a key role, as it allows tourism companies to better understand their customers, anticipate their preferences, and offer more accurately personalized services.

Some of the advantages that Decision AI can bring to the tourism sector include:

• Personalization and enhancement of customer offerings, not only through proposals tailored to each traveler’s preferences, budget, and context, but also through the ability to anticipate their needs or potential issues before they are even expressed.
  
• Increased profitability, through the optimization of itineraries and resources, which can also lead to greater operational efficiency.
  
• Improved decision-making, by analyzing demand trends, seasonality, and other variables. This enables more effective planning of products, pricing, and promotional strategies, as well as a faster response to unexpected events.

But… what exactly is a problem in the tourism sector?

Beyond the challenge of personalization, the tourism industry also faces other types of problems that could be addressed using predictive models: How much demand can we expect? And what will tourist behavior look like in terms of consumption and needs?

How much demand can we expect?

This refers to the estimation of the number of tourists who will visit a destination, city, museum, or landmark during a specific future period. The main characteristics of this type of prediction include:

• Prediction horizon: which can be daily, weekly, or seasonal.
  
• Data sources: which may include historical visitor records, online searches, bookings, or weather data, among others.
  

One of the major challenges in this area is identifying and combining multiple data sources and variables to build robust models capable of forecasting demand with a high degree of accuracy. This type of prediction is crucial for anticipating tourist flows and planning resources in advance, as well as essential services such as transportation, accommodation, or security.

What will tourist behavior look like in terms of consumption and needs?

This focuses on predicting the future preferences or decisions of a specific tourist or a defined customer segment. The goal is to offer a high level of personalization in proposals and services, based on data such as historical consumption patterns, past bookings, or individual preferences.

In this case, the main challenge lies in generating relevant and accurate real-time recommendations without compromising user privacy.

What methods are used?

The specialized literature includes a variety of models, such as:

• ARIMA and SARIMA: classical statistical models used to analyze and forecast time series.
  
• Neural networks applied to time series, such as RNNs (Recurrent Neural Networks), LSTM (Long Short-Term Memory), or Time Series Transformers, which capture patterns in sequential data.
  
• Classification models, used to identify tourist patterns or behaviors, such as the likelihood of visiting a destination, canceling a booking, or choosing a specific activity—valuable for enhancing travel personalization.
  

In addition to these, many other approaches exist to address such problems. In fact, hybrid models have been developed that combine heuristic rules with other AI techniques. A heuristic is an algorithm that follows a set of defined rules or criteria to reach an approximate solution to a problem, especially useful when a fast and efficient response is needed.

Predictive models based on artificial intelligence make it possible to anticipate patterns of tourist behavior. This not only helps with demand forecasting but also with resource and route optimization. Based on these predictions, intelligent systems can be implemented to recommend more efficient itineraries according to variables such as opening hours, expected visitor flows, and customer preferences. This allows tourism companies not only to provide a better experience but also to more efficiently distribute operational load and make data-driven decisions.

How can I apply this to my business?

While we’ve discussed certain types of problems in general terms, the reality is that most of them are not directly applicable to a specific business issue. Every company—and each business model within the tourism sector—has its own particularities, objectives, and limitations that must be taken into account.

That’s why, when applying Decision AI in real-world operations, it is essential to incorporate the specific constraints of the business, as well as the key variables that influence decision-making. These may include factors such as resource availability, local regulations, or even commercial agreements. In this context, models must be designed with flexibility, meaning they should be adaptable to different operational scenarios and capable of adjusting as data evolves or environmental conditions change.

How can we help solve the problem?

For all these reasons, it is essential to approach this type of project by ensuring both adaptation to the client’s specific context and the effectiveness of the final solution. This approach makes it possible to build useful, scalable models that are aligned with business objectives. The main phases are:

• Problem definition: we clearly identify what needs to be solved and what level of accuracy or performance is expected from the model.
  
• Requirements gathering: we analyze the available data sources, their potential limitations, and how they can be combined with the operational and strategic constraints of the business.
  
• Selection of the appropriate theoretical model: we choose the most suitable approach based on the nature of the problem. This may involve time series statistical models, deep learning models, or hybrid approaches that combine various techniques.
  
• Development of the predictive algorithm: we build the model and progressively improve it, validating results at each iteration to ensure robustness.
  
• Integration into existing systems: once validated, the model is incorporated into the client’s technological platforms, ensuring it works seamlessly in day-to-day operations.
  
• Continuous evaluation and iterative improvement: we monitor performance, review potential errors, incorporate new variables, and continuously optimize its functioning.

This process allows us not only to solve the initial problem, but also to create a solution that evolves alongside the needs of the business.

Applying this type of technology to specific problems in the tourism sector has a real impact on business operations. Some of these impacts include:

• Increased revenue: by anticipating periods of high and low demand, we can determine optimal pricing strategies to maximize revenue and improve profitability.
  
• Higher occupancy rates: closely related to the above, by identifying and anticipating low-demand periods, we can adapt our commercial offers, adjust pricing, and tailor marketing efforts to boost occupancy.
  
• Improved customer experience: by understanding customers’ preferences and personal tastes, we can tailor our offerings accordingly.
  
• Cost savings: by predicting demand, we can optimize the use of resources and reduce costs related to consumables, staffing, and transportation.

Like many other industries, the tourism sector is undergoing a transformation toward more sustainable and intelligent tourism. This is where Decision AI can become a key technology for the sector’s growth.

If you’d like to learn more about how to implement AI models that help you make better decisions, contact us for a personalized consultation session.