Mastering Hotel Data Analytics: A Guide for Revenue Managers

While this definition is straightforward, the complexity lies in the data itself. As a hotelier, you likely have access to more data than you realize.

Data is one of your most valuable assets, but – like the oil with which it’s so often compared – if you don’t drill into it, refine it and put it to good use, it won’t fuel your work.

In this blog post, we outline the basics of this all-important topic to provide a springboard into data-driven decision-making practices at your hotel.

The importance of data analytics in the hospitality industry

In the broadest terms, running a business requires expertise in two areas: strategy and implementation. Neither are sufficient alone.

Effective strategies and tactics combine theory with practice, informed by empirical evidence. This approach enables:

Good strategy and the tactics that follow draw on theory and practice, the latter being informed by empirical evidence. This is how you can make meaningful adjustments, and it’s where data analytics comes into its own.

Done well, data analytics offers significant benefits:

• Valuable insights into your guests and the guest experience: this empowers you to improve the experience for future guests. And a better experience means higher chances of repeat visits, word-of-mouth recommendations, better online revenues, and opportunities for upselling.

• A deeper understanding of revenue streams: this makes it easier to identify opportunities to drive revenue. Which campaigns work? Which don’t? Which would work with more budget or different targeting? Which online travel agents (OTAs) and other distributors should you work with or refine your partnerships with? Data analytics enables you to answer crucial questions about your revenue streams, guiding strategic decisions for growth.

• Operational insights: Leverage data-driven insights to streamline processes across your property This leads to improved efficiency, higher profit margins, and enhanced performance across key metrics.

5 types of data analytics (+ what they mean for your hotel)

Data comes in different shapes and sizes, and so too does data analytics. Each of the five types with which you should be familiar and put into practice offers different actionable insights into your guest preferences, hotel and the landscape in which you operate.

There are five key types of data analytics particularly relevant for hoteliers:

1. Descriptive analytics

Using current and historical data, descriptive analysis is the process of identifying and describing trends and relationships. Though often considered the simplest form of data analysis because it doesn’t involve deep digging, it’s nonetheless particularly useful for communicating change over time. These trends can pave the path for further analysis and better decision-making.

Descriptive analytics provides insights into:

• Booking patterns

• Guest demographics

• Revenue trends

All of these change over time but seeing how they change means you can capitalize on what’s working and what isn’t, and focus your resources and attention on the former to build better marketing campaigns and set the right prices.

What data to collect and how to analyze it

Your three primary sources for descriptive analysis are:

• Property management system (PMS): where your front office activities are controlled and recorded

• Central reservation system (CRS): the single source of truth for inventory management

• Customer relationship management system (CRM): where you record your marketing efforts and customer data; the more granular it is, the better actionable insights you can derive

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

Consider that data comes into these systems in three ways:

1. Staff input at your property

2. Guest-provided information (via your channels or OTAs)

3. Or it’s wired up to be fed from one system to another.

So review your set-up to see if you’re asking the right questions.

In analyzing the data, consider the basic metrics and KPIs that are at the heart of revenue management. The more data you have, the more statistically valid it will be, but you can only make sense of it if you’re aware of confounding variables such as events, seasonality and other factors that might explain peaks or troughs in your graphs.

2. Diagnostic analytics

Overlapping with Type 1, diagnostic analytics is a slightly misleading term, as diagnoses are usually associated with problems. Despite its name, this approach isn't limited to problem-solving; it examines both positive and negative patterns in your hotel's performance. By understanding what drives successful outcomes as well as challenges, diagnostic analytics allows you to refine your strategies, enhance guest services, and streamline operations.

What data to collect and how to analyze it

Because the data used in diagnostic analysis is about you and your hotel, you can often influence what you capture, which can include:

• Feedback on guest experience: make sure there’s a way to gather this, whether digitally, in handwritten questionnaires or from conversations, but most importantly, record it, and record it in a way that like-for-like comparisons can be made.

• Online reviews: you’ll need to check third-party websites for this but these are easy to find.

• Operational data: see the section above on your PMS and CRS.

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

Online review star ratings are a clear example of quantitative data. But much of the data is qualitative, so it helps if you can categorize the type of long-form feedback you receive for meaningful analysis.

3. Predictive analytics

Predictive analytics expands on descriptive analytics by incorporating external data, such as market trends, alongside historical information.

This approach provides a more comprehensive forecast of future outcomes.

When drawn together and viewed through the right analytical prism, you can generate models to accurately forecast future demand.

This is important because it enables you to:

• Fine-tune your pricing strategies and dynamically adjust your prices, making sure you factor in competitor prices

• Adjust inventory levels

• Maximize revenue and profitability

• Ensure that your mix of distribution channels is optimized

• Hone your operations for the likes of appropriate staffing levels

What data to collect and how to analyze it

In addition to the sort of data you gather for descriptive analysis, you’ll also need:

• Market and demand data, factoring in events and seasonality

• Real-time data on your competitor rates

• Proprietary data on occupancy rates and ADR in your market segment

Much of this is available commercially from third-party suppliers, as per the examples we link to above, and it can be viewed on their platforms or brought into your own systems for analysis.

We recommend focusing on common KPIs and to apply the caveats we discuss above relating to statistical significance.

4. Prescriptive analytics

Prescriptive analytics provides hoteliers with data-informed recommendations for the optimization of their decision-making.

Prescriptive analytics is heavily reliant on vendors’ solutions, as it brings in data from outside your control and feeds it through algorithms that you can’t see, so don’t take its recommendations as gospel; you can override them. But it’s an important part of the mix, especially for smaller hotels with small revenue management departments.

A bread-and-butter example is its use for determining prices based on demand data and on room rates in your competitive set.

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

What data to collect and how to analyze it

Most of the data for prescriptive analytics is a combination of internal and external inputs - the true power lies in the combination of the two.

5. Cognitive analytics

The most cutting-edge item in this list, cognitive analytics combines artificial intelligence (AI) and data analytics, and it capitalizes on advances in Big Data.

It aims to apply human-like intelligence to analytical tasks by integrating various technologies, including:

• Semantics

• Highly refined algorithms

• Deep learning and machine learning (ML)

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

All data analytics is about learning, radjusting and improving, so if you can accelerate this through AI, you have a clear competitive advantage in terms of revenue and improvements in operational efficiency across the board at your hotel.

What data to collect and how to analyze it

Cognitive analytics, an emerging technology in hospitality, can process unlimited types of data, both external and interna. But it should go without saying that the more you can gather and enter into your own systems, and the more granular it is, the better placed you’ll be to equip these solutions with source material from which to derive sophisticated and reliable insights.

When interpreting results apply the same rules we discuss above – but beware so-called ‘hallucinations’, a phenomenon of generative AI; if something looks suspicious or implausible, consider what data would have been needed to produce it. Keep in mind that inferences can be made from analogous data but these only go so far.

Why data analytics fails without robust software

The hotel revenue management market is awash with software solutions that claim to support data analytics.

Some providers do a great job. Some don’t. So choose wisely.

The best tools will:

• Draw from the right data, often real-time data, and lots of it – remember the importance of Big Data

• Use finely-tuned algorithms to produce meaningful output – these are out of public view, so speak to customer representatives and interrogate them about how they work, and get a demo or ideally a trial, as the proof is in the pudding

• Be easy to use – the best tool in the world is useless if it’s frustrating to navigate and drains your time with all the staff training and support desk queries you have to make

• Be reasonably priced – but you get what you pay for, so this doesn’t necessarily mean choosing the cheapest solution

• Be custom-built for hoteliers, rather than a generic tool.

Keep all these pointers front of mind, because data analytics is only as good as the tools you use at your property.

Uncover better insights with market intelligence tools

As hotel industry experts, we at Lighthouse understand the critical role of effective data analytics. Our comprehensive suite of solutions supports revenue managers in their data analysis efforts:

• Business Intelligence – forecasts, reports, assessing your metrics: this is the platform to understand your hotel and drive strategy

• Market Insight – spot and analyze demand patterns and get a view of trends before your competitors

• A range of other tools, including Rate Insight, that can also help manage bookings, assess competitors’ prices and forecast revenue.

Ready to unlock the full potential of your data?

Explore how Lighthouse's suite of tools can transform your analytics capabilities and drive your property's success. Let's discuss how our solutions can meet your unique needs.