Integrating artificial intelligence (AI) into hotel operations has been touted as a game-changer for increasing guest engagement while decreasing expenses. While much of the attention has focused on generative AI and its potential to personalize responses, there's another type of AI that's already making waves in the hospitality industry: machine learning. As a building block of genAI, traditional machine learning is proving its value by creating a material revenue benefit for hotels.

Machine learning has been used for years in revenue management systems to automate pricing and forecasting decisions. Advances in machine learning have enabled faster, more complex computing capabilities, leading to more robust pricing and forecasting. This technology isn't just about generating predictions; it's also about making recommendations that drive action.

Predictive and Prescriptive Analytics

At its core, machine learning is all about predictive and prescriptive analytics. These functions work together like three interconnected gears in the clockwork of hospitality pricing. Predictive analytics uses historical data, statistical modeling, machine learning techniques, and other algorithms to identify patterns and predict future outcomes or trends. It's focused on "what will happen" in a given scenario.

Prescriptive analytics goes beyond prediction to ask, "What should we do now?" Building on the insights from predictive analytics, it recommends and optimizes actions to achieve desired outcomes. This actionable advice can be as simple as what price to assign to a specific product or service.

Upselling Solutions

Nor1's upsell solutions are a great example of machine learning in action. Their system uses machine learning to select, price, and present upsell offers based on defined upsell options, historical data, real-time inventory availability, and reservation information. This combination of components enables the system to make predictions about which offers to present, price, and sort, taking into account inventory availability and even the order of offer presentation.

Beyond Revenue Management

Machine learning's benefits extend far beyond revenue management. Hotels have been using operational systems with machine learning to deliver personalized experiences that cater to individual preferences and past stays. This can include pre-stocking minibars, recommending scenic high-floor rooms, or selling non-alcoholic amenities. By leveraging guest data, hotels can present targeted hotel offerings with the highest probability of conversion.

Operational Efficiency

Machine learning can also be used for predictive maintenance, anticipating potential equipment failures before they occur. This prevents inconvenient disruptions like malfunctioning thermostats or worn-out linens, reducing operational costs associated with reactive repairs. AI-powered chatbots and digital assistants can provide 24/7 support, freeing human staff to focus on personalized interactions.

Broader Applications

Machine learning holds promise for hotels in many areas beyond revenue management. Data analysis enables hotels to anticipate future demand with remarkable accuracy, helping to predict occupancy rates and resource needs. Sentiment analysis tools gain valuable insights from guest reviews and social media feedback, enabling hotels to proactively address potential issues and identify areas for improvement.

Additional Applications

Machine learning has the potential to benefit hotels in many other ways, including:

  • Fraud detection: Identify and prevent fraudulent bookings and transactions, minimizing financial losses.
  • Enhanced guest safety and security: