Mastering data analysis in hotels: a guide for revenue managers.

Although this definition is simple, the complexity resides in the data itself. As a hotel professional, you probably have access to more data than you imagine.

Data is one of your most valuable assets, but – like the oil it is often compared to – if you do not explore, refine and use it effectively, it will not drive your business.

In this article, we present the basic concepts of this very important topic to serve as a starting point for data-driven decision-making practices in your hotel.

Key takeaways

• Hotel data analysis transforms raw information from property management systems, central reservation systems, and customer relationship management platforms into actionable insights that drive revenue optimization and operational efficiency.
• Five types of analysis serve different purposes: descriptive analysis reveals reservation patterns and trends, diagnostic analysis explains the factors driving performance, predictive analysis forecasts demand using market data, prescriptive analysis provides pricing recommendations, and cognitive analysis applies AI to learn from data interactions.
• Effective data analysis in hotels requires robust software solutions that integrate real-time data sources, use refined algorithms, offer user-friendly interfaces, and are designed specifically for hospitality operations, rather than generic commercial tools.
• Revenue managers can leverage guest feedback, online reviews, competitor pricing, and market demand data to optimize pricing strategies, adjust inventory levels, and improve the performance of distribution channels.
• Data analysis allows hotels to enhance guest experience through a deeper understanding of customer preferences, resulting in increased repeat visits, positive reviews, and additional sales opportunities.
• The combination of internal hotel data with external market information provides the most comprehensive foundation for making informed decisions about revenue management and maintaining competitive advantage.

The importance of data analysis in the hotel industry

Generally speaking, managing a business requires knowledge in two areas: strategy and execution. Neither is sufficient alone.

Effective strategies and tactics combine theory and practice, based on empirical evidence. This approach allows for:

A good strategy and the tactics that follow it are based on theory and practice, the latter being founded on empirical evidence. This is how significant adjustments can be made, and this is where data analysis excels.

When done well, data analysis offers significant benefits:

• Valuable information about your guests and their experience : this allows you to improve the experience for future guests. And a better experience means greater chances of repeat visits, word-of-mouth recommendations, higher online revenues, and opportunities for additional sales .
• A deeper understanding of revenue sourcesIt facilitates the identification of opportunities to boost them. What campaigns work? Which don't? Which would work with a larger budget or different segmentation? Which online travel agencies (OTAs) and other distributors should you work with or improve your partnerships with? Data analysis allows answering crucial questions about your revenue sources, guiding strategic decisions for growth.
• Operational analyses : Use data-based insights to optimize processes throughout your property. This leads to greater efficiency, higher profit margins, and better performance on key metrics.

5 types of data analysis (and what they mean for your hotel)

Data comes in different formats and sizes, and the same is true for data analysis. Each of the five types you should be familiar with and put into practice offers different actionable insights about your guests' preferences, the hotel, and the scenario in which you operate.

There are five main types of data analysis particularly relevant for hotel managers:

1. Descriptive Analysis

Using current and historical data, descriptive analysis is the process of identifying and describing trends and relationships.

Although often considered the simplest form of data analysis for not requiring deep investigation, it is particularly useful for communicating changes over time. These trends can pave the way for further analyses and better decision-making.

Descriptive analysis provides information about:

• booking patterns
• Guest demographic data
• Revenue trends

All these factors change over time, but observing how those changes occur allows you to leverage what is working and what isn't, focusing your resources and attention on the former to create more effective marketing campaigns and set appropriate prices.

What data to collect and how to analyze it

Your three main sources for descriptive analysis are:

• Property Management System (PMS)(PMS): where your front desk activities are controlled and recorded.
• Central Reservation System (CRS): the only reliable source of inventory information.
• Customer Relationship Management System (CRM): where you record your marketing efforts and customer data; the more detailed the records, the better the actionable insights you can obtain will be.

You probably already use these systems, but are you leveraging the data they generate? Data enters these systems through:

Consider that data arrives in these systems in three ways:

1. Staff contribution at your property
2. Information provided by the guest (through your channels or OTAs)
3. Or it is configured to be connected from one system to another.

So, review your configuration to see if you are asking the right questions.

When analyzing data, consider thebasic metrics and KPIsthat are essential for revenue management. The more data you have, the more statistically valid they will be, but you can only interpret them correctly if you are aware of confounding variables, such as events, seasonality, and other factors that can explain peaks or dips in your graphs.

2. Diagnostic Analytics

While it overlaps with Type 1, diagnostic analytics is somewhat misleading, since diagnoses are generally associated with problems.

Despite the name, this approach is not limited to problem solving; it examines both positive and negative patterns in your hotel's performance.

By understanding what drives positive results, as well as the challenges, diagnostic analysis allows you to refine your strategies, enhance guest services, and optimize operations.

What data to collect and how to analyze it

Since the data used in diagnostic analysis pertains to you and your hotel, you can generally influence what is collected, which may include:

• Guest experience feedback: ensure there is a way to collect this information, whether digitally, through handwritten questionnaires or conversations, but above all, record it in a way that allows for direct comparisons.
• Online reviews: you will need to consult third-party websites for this, but they are easy to find.
• Operational data: consult the section above regarding your PMS and CRS.

All this data provides direct or indirect measures of customer satisfaction or dissatisfaction and the factors that influence it.

Online star ratings are a clear example of quantitative data. However, much of the data is qualitative, so it is useful to categorize the type of most extensive feedback received for meaningful analysis.

3. Predictive Analysis

Predictive analysis expands descriptive analysis by incorporating external data, such as market trends, along with historical information.

This approach provides a more comprehensive forecast of future results.

When collected and analyzed from the correct analytical perspective, these elements allow generating models to accurately predict future demand.

This is important because it allows you to:

• Improve your pricing strategies and adjust your prices dynamically, taking into account competitors' pricing.
• Adjust inventory levels
• Maximize revenue and profitability
• Ensure that your combination of distribution channels is optimized.
• Improve your operations for aspects such as adequate staffing levels.

What data to collect and how to analyze it

In addition to the data you collect for descriptive analysis, you will also need:

• Market and demand data, considering events and seasonality.
• Real-time data on competitor rates
• Exclusive data on occupancy rates and average daily rates in your market segment.

Much of this is commercially available through third-party providers, as in the examples we mentioned above, and can be viewed on their platforms or imported into your own systems for analysis.

We recommend focusing on common KPIs and applying the caveats we discussed above regarding statistical significance.

4. Prescriptive Analytics

Prescriptive analytics provides hotel managers with data-driven recommendations to optimize their decision-making.

Prescriptive analytics heavily depends on vendor solutions because it imports data outside of your control and processes it through algorithms that you cannot visualize. Therefore, do not consider recommendations as absolute truths; you can modify them.

But it is an important part of the process, especially for smaller hotels with reduced revenue management departments.

A classic example is its use to determine prices based on demand data and room rates across your set of competitors.

But the more sophisticated your tool is, the greater variety it can offer for other informed decisions, such as those that can feed a displacement analysis, for example.

What data to collect and how to analyze it

Most data for prescriptive analytics is a combination of internal and external inputs – the real power lies in combining the two.

5. Cognitive Analytics

The most innovative item on this list, cognitive analytics, combines artificial intelligence (AI) and data analysis, leveraging advances in Big Data.

Its goal is to apply human-like intelligence to analytical tasks, integrating various technologies, including:

• Semantics
• Highly refined algorithms
• Deep learning and machine learning (ML)

This approach allows your analytical systems to learn from data interactions and human input, becoming smarter and more effective over time.

Data analysis is based on learning, adjustments, and improvements. Therefore, if you can accelerate this process through AI, you will have a clear competitive advantage in terms of revenue and improved operational efficiency in all aspects of your hotel.

What data to collect and how to analyze it

Cognitive analysis, an emerging technology in the hotel industry, can process unlimited types of data, both external and internal.

But it is evident that the more data you can collect and enter into your own systems, and the more granular this collection is, better prepared will you be to provide these solutions with source material from which sophisticated and reliable insights can be extracted.

When interpreting results, apply the same rules discussed above – but beware of so-called "hallucinations," a phenomenon of generative AI; if something seems suspicious or implausible, consider what data would be needed to produce it.

Remember that inferences can be made from analogous data, but these have their limitations.

Why does data analysis fail without robust software?

The hotel revenue management market is filled with software solutions that claim to offer data analysis support.

Some providers do a great job. Others do not.

The best tools will:

• Use the correct data, usually real-time and large quantity data – remember the importance of Big Data.
• Use highly accurate algorithms to produce meaningful results – these are not publicly accessible, so talk to customer service representatives, question them about their operation, and request a demo or, ideally, a trial period, because the proof is in the results.
• Be easy to use – the best tool in the world is useless if it is frustrating to navigate and consumes your time with all team training and support requests that you need to make.
• Have reasonable prices – but remember that you get what you pay for, so this does not necessarily mean choosing the cheapest solution.
• It should be developed tailor-made for hoteliers, rather than a generic tool.