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Introducing Machine Learning’s Applications for Hotel Operations

So much has been written recently about how artificial intelligence (AI) in its various forms can help hotels run their businesses better by increasing guest engagement while decreasing expenses. Almost all that attention to date has focused on generative AI (genAI) and its potential to help travelers plan and book travel or to provide personalized responses to service guest or employee queries. But because genAI is in its infancy, there are few real-world use cases in hospitality today proving its value. So let’s take a look at an AI that’s already in use, is a building block of genAI, and has already proven its value to hospitality: machine learning.

Some may scoff, but traditional machine learning is exciting! Why? Because it has already proven to create a material revenue benefit on the hospitality industry. Revenue management system providers have included some type of machine learning to automate decisions around pricing and forecasting for some time. Advances in machine learning have created faster, more complex computing capabilities, enabling more robust pricing and forecasting. And as those advances scale, the industry will continue to derive more value from machine learning.

When discussing machine learning, especially relative to pricing and forecasting, we must first talk about predictive and prescriptive analytics. These functions go together like three interconnected gears in the clockwork of hospitality pricing.

Predictive analytics is a branch of advanced analytics that uses historical data, statistical modeling, machine learning techniques, and other algorithms to identify patterns and predict future outcomes or trends. It focuses on “what will happen” in a given scenario, like predicting customer churn, forecasting market trends, or even predicting prices.

Prescriptive analytics goes beyond prediction to ask, What should we do now?, building on the insights from predictive analytics to recommend and optimize actions to achieve desired outcomes. It offers actionable advice, such as what price should be assigned to a specific product or service.

Here’s a good example of the combination of machine learning, predictive analytics, and prescriptive analytics in action: Nor1’s upsell solutions are all based on machine learning that needs certain information to select, price, and present upsell offers, including: (1) defined upsell offers at a hotel (rooms, attributes, or non-room items like early check-in or breakfast); (2) a data set with history of upsell offer performance at that hotel; (3) access to inventory in real-time; and (4) reservation data for a given guest.

With those components in place, an incoming reservation is evaluated in real time; predictions are made as to which offers to present, price, and sort; inventory is checked for availability; and then the selection of offers is made to price and present (with even the order of an offer presentation being in consideration). The offers are presented back to a guest in real-time wherever the guest made the request – booking engine, email, app, mobile web, or even in-person at the front desk.

In short, machine learning provides the foundation, which is the understanding of the data and its relationships. Predictive analytics builds upon this understanding to forecast future developments, and then prescriptive analytics uses these predictions to guide decisions and optimize outcomes. This makes machine learning straightforward and powerful, not to mention great for the bottom line – and guests.

Benefits from machine learning extend beyond revenue management, and many hotels have been using operational systems with machine learning. For example, leveraging guest data, hotels can deliver personalized experiences that cater to individual preferences and past stays. From pre-stocking minibars to recommending scenic high-floor rooms to selling non-alcoholic amenities, machine learning can present targeted hotel offerings with the highest probability of conversion. This eliminates generic accommodations and elevates guest satisfaction by providing offers the guest is more likely to want and actually buy.

Furthermore, predictive maintenance algorithms anticipate potential equipment failures before they occur, preventing inconvenient disruptions like malfunctioning thermostats or worn-out linens, reducing operational costs associated with reactive repairs.

AI-powered chatbots and digital assistants can also provide 24/7 support, such as handling reservations, answering repetitive questions, and offering local recommendations. By freeing human staff from these tasks, these virtual assistants allow them to focus on personalized interactions, further enriching the guest’s journey.

These digital assistants have broader potential to streamline operations and unlock significant cost efficiencies. Repetitive tasks such as housekeeping schedules can be handled by digital systems, enabling staff to focus on guest interactions and higher-value tasks. This not only improves employee satisfaction, but also reduces expenses.

Other types of analytics and machine learning holds promise for hotels. For example, data analysis empowers hotels to anticipate future demand with remarkable accuracy, helping to predict occupancy rates and resource needs, paving the way for optimal staffing, energy management, and resource allocation. Additionally, sentiment analysis tools gain valuable insights from guest reviews and social media feedback. By analyzing this rich data, hotels can proactively address potential issues and identify areas for improvement, creating a guest-centric environment that fosters more positive experiences and brand recognition.

Here are few more areas in hospitality that benefit from the application of machine learning:

  • Fraud detection: Identify and prevent fraudulent bookings and transactions, minimizing financial losses.
  • Enhanced guest safety and security: Analyze security footage and sensor data to detect potential threats and promote guest safety.
  • Elevated staff productivity: Automate repetitive tasks and save staff members from repetitive actions, ultimately saving time to focus on higher-value tasks faster.
  • Improved employee experience: Provide data-driven insights to optimize staff scheduling, training, and career development, leading to higher employee satisfaction and lower churn.

Let’s change direction and address one of the primary value propositions of machine learning – automation. It might seem obvious, but the application of machine learning to the functions listed above means these functions are automated – no human being is involved in the actual task or has a more limited role. The opportunities for reduced expenses and more effective business management are evident.

This doesn’t mean humans are going to be eliminated from our industry. To the contrary, hospitality doesn’t exist without the human touch. But there are mountains of data available to hoteliers about guests, the market, their facilities, staff, etc., and machine learning systems are great at finding and learning from patterns in data. The industry now has a better way to utilize their data, obtain a clearer view into their business, and obtain actionable insights to make improvements.

Machine learning empowers the hospitality industry to personalize guest experiences, optimize operations, and make data-driven decisions, ultimately leading to increased revenue, guest loyalty, and competitive advantages. It’s also a jumping off point for generative AI, which we’ll discuss in a future post.

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