Hospitality and similar service industries are primarily focused on customer satisfaction, presenting the experiences offered to guests, the atmosphere, the story… Although it seems that artificial intelligence has little to do here, it is already useful and will become increasingly so. It can be used in operational tasks, assist staff in their daily work, and provide key information for strategic decisions and management moves.
Artificial Intelligence (AI) is drastically changing the way we use technology and communicate with it. It has been shown to help us solve complex problems, but it can also completely take over some jobs and reduce or entirely replace human labor where possible. Hospitality and similar service industries are primarily focused on customer satisfaction and presenting the experiences offered to guests: the atmosphere, the story, the new experience, and its appeal.
Can artificial intelligence offer something better than a person who presented a specific service to the guest and personally checked their satisfaction? How will we solve data security issues, privacy for guests and service providers? Are open-source artificial intelligence tools good enough and safe for application in hospitality? These are all open questions and situations that arise with its use.
Let’s first look at the challenges faced in hospitality. Some of the characteristics of this sector include seasonality, increasing guest demands, and growing market competitiveness. Advertising, monitoring, and adapting all forms of advertising, especially on social media and platforms, are extremely important. A skilled workforce that is proficient in foreign languages and excels in online or offline sales methods and tools is needed… How can artificial intelligence help address these challenges, particularly in the area of service personalization, revenue management, the application of chatbots, and more?
Data and Analytics
Since AI enables the analysis of large amounts of guest data, it is natural to use it for predicting trends and personalizing offers. Based on the analysis of available guest data such as activities during their stay, spending, demographic data, preferences, booking and purchase history, it can identify similarities among guests and define groups based on those similarities and patterns using machine learning techniques and clustering. An example is segmentation, such as guests traveling for business or those preferring luxury services.
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Machine learning is often used for guest segmentation or classification, specifically supervised machine learning techniques. Classification algorithms such as logistic regression, random forests, support vector machines (SVM), and neural networks are applied.
Of course, with all the available tools and methods, the most important factor is the dataset on which the analysis and segmentation are performed. The quality, quantity, and comprehensiveness of the data are crucial: the better and more extensive the data, the more accurate and useful the segmentation will be for personalizing the offer.
Personalization of Offers
When we segment guests using some of the mentioned algorithms, marketing campaigns can be adapted, special offers can be designed, activities, restaurants, accommodations can be recommended, personalized recommendations, special menus, and similar can be provided, thus offering a unique experience to each guest. This, of course, ensures maximum guest satisfaction, potentially gaining their long-term loyalty and increasing spending.
For this purpose, a technological tool (recommendation engine) can also be used to create personalized recommendations about accommodations (type of room, view, location, etc.), restaurants and dining (e.g., menu, specialties), activities and attractions (e.g., tours, museums, sporting events), and additional services (spa, fitness, pool, transportation, events, etc.).
Revenue Management
The next important application would certainly be in revenue management, whose main goal, simply put, is to sell the right product to the right customer at the right time and, of course, at the right price (offer the right room to the right customer at the right price, at the right point in time). Essentially, it is about maximizing capacity utilization while achieving the highest possible service price and the lowest costs, all while ensuring satisfied guests.
Artificial intelligence in this segment helps in dynamic pricing, i.e., defining accommodation unit prices based on various factors such as demand, supply, events during a certain period, weather forecasts, season, and other relevant data. It can predict demand trend movements, analyze competitive information, provide insights into the current market state, and suggest competitive prices. It can also automate pricing decisions based on predefined rules and parameters set by users. In this area, machine learning algorithms and predictive analytics techniques are used.
