Machine learning and its importance in hotel revenue management

Hotels are constantly looking for new ways to boost their revenue growth and attract a larger segment of the market. With the right revenue management strategies, you can optimise your prices, segment your market, predict consumer demand, and enhance your guest experience. But you also need the right tools and software so that you can access all the data you need to create these strategies. This is where machine learning can help.

Today’s post is going to explain what machine learning is and why this technology is so valuable for effective hotel revenue management.

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What is machine learning?

Machine learning is an advanced technology that uses algorithms and artificial intelligence to identify hidden patterns and insights in order to predict future trends. The technology has a range of applications. Including image and speech recognition, virtual personal assistants, natural language processing, and data analytics.

In terms of hotel revenue management, machine learning can be used to enhance your revenue management systems in a number of ways. The most common applications are those that relate to data processing and analytics, automation, and personalisation. 

How can machine learning be used in revenue management?

Increasing numbers of hotels are incorporating machine learning in their revenue management tech stack. In fact, AI and machine learning can be deployed in almost every process in your hotel. The main reason it’s so effective is that the technology has an enormous capacity for handling large quantities of data and complex tasks in an efficient and effective manner. This not only provides revenue managers with a wealth of valuable insight, but the right tools also save time and resources that can instead be channelled into revenue growth strategies.  

Let’s explore how exactly you can use machine learning to streamline and enhance your revenue management processes. 

Analyse Big Data to help you understand your market

Data collection and analysis is one of the most important elements of revenue management. Machine learning provides you with three different levels of analysis: descriptive, predictive, and prescriptive analytics.

Let’s start with predictive analytics. This is where you rely on internal (hotel performance) and external data (market trends) to predict future demand.

The right predictive analytics can help you understand and segment your market and forecast and anticipate consumer demand. In the past, all this predictive data would need to be sourced and analysed manually. Which took a great deal of time and effort. Machine learning has stepped things up. By a long mile.

In fact, machine learning can identify all manner of trends in Big Data and drastically enhance the quality of your automated predictive analytics.

For example, in terms of understanding your market, machine learning can help you:

• Identify your key market segments through 360-degree customer views.
• Understand and predict the likes, dislikes, preferences, needs, wants, and expectations of your guests, helping you create optimised guest experiences.
• Predict guest purchasing behaviours based on past behaviour so that you can anticipate demand. And also adjust your pricing strategies accordingly (when they book, how they book, demand trends, revenue per guest, etc.).
• Flag potential risks associated with each guest segment (customers, macro-industrial environments, micro-environments, etc.) and transform them into revenue opportunities.
• Adjust your market predictions in line with external factors such as weather, upcoming events, traffic, etc.

All this business intelligence helps you gain market awareness so that you can keep your guests happy and optimise your pricing strategies. This, in turn, enhances your hotel’s reputation, gives you a competitive edge, and enables you to refocus on the development of sustainable and profitable revenue streams.

Enhanced forecasting so that you can design actionable RM strategies 

As we just discussed, machine learning provides you with three different levels of analysis. 

Let’s explore these levels in a bit more detail:

• Descriptive analytics: the analysis of structured and unstructured data, including internal analytics (hotel performance) and external analytics (competition and market). This helps you to paint a picture of the current status of your business.
• Predictive analytics: additional data that complements your descriptive analysis with external factors that might influence market trends, such as weather, traffic, economics, upcoming events, etc. This helps you better predict future demand.
• Prescriptive analytics: the analysis of both descriptive data (the current status of your business) and predictive data (your demand forecasts) in order to identify (prescribe) effective sales strategies.

Machine learning provides you with valuable prescriptive recommendations that you can use to design actionable strategies that respond to your predicted forecasts. This is essentially what revenue managers have been doing manually for years, but machine learning can speed up the process, make more accurate data-driven predictions, and exponentially enhance your results. And, given that forecasting is an essential part of your role, this is a huge benefit.

In fact, it’s so effective that prescriptive analytics is believed to be the future of hotel revenue management.

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