What is hotel analytics?

Hotel analytics is the qualitative and quantitative data insights used to improve marketing strategies, optimize revenue and productivity, and make the best business decisions. It is the process of analyzing guest data to gain a competitive advantage for effective forecasting capabilities, to predict trends, optimize revenue, and improve the overall guest experience. Hotel analytics help hoteliers explore travel trends so that they can model likely outcomes and anticipate similar results in the future. This, in turn, helps them improve decision-making, drive productivity, and increase the predictability of revenue.

The hotel industry collects vast volumes of disparate data that can be leveraged for consumer insights and competitive advantage. However, such data often remains an underappreciated asset. Many people capture loyalty information from the data, but very few progress to deeper analysis.

In the current dynamic technological landscape, where machine learning and artificial intelligence are ubiquitous in nearly every aspect of data gathering and processing, it is necessary to leverage it in the hospitality vertical too.

Analytics is the way forward for the hospitality industry to help hoteliers improve marketing capabilities to attract more guests as well as increase revenue and operational efficiency. Analytics assist hoteliers in the examination of different trends and patterns based on the existing data, and use the data to make future predictions. Hotel analytics are the architect of astute business decisions and the key to building effective marketing strategies.

Different methods of using hotel analytics

Hotel and accommodation data is available in large quantities, be it client data, market data, social media information, or guest stay data. Many sources contribute to this data pool used by hotel chains, hotel companies, and independent agencies like travel agents. All this data first has to be segmented to gain meaningful insights from it using tools like data analytics. First, the data is collected and aggregated from different sources, then segmented, cleansed, and prepared to allow for easier analysis.

Hotel analytics serves many purposes and based on what the hoteliers are looking for, the analysis is divided into different types.

Qualitative hotel data

The data for this process comes from surveys, social media, customer service chats, and physical interactions with guests. It answers qualitative questions like where, how, why, and more.

Quantitative hotel data

The data for this method comes from structured data sources. It is usually taken from the hotel website, channel manager, CRM system, or inventory list.

After these two basic methods are determined, further data analysis is done based on the following methods:

Descriptive hotel analytics

This is the traditional form of analysis that looks at historical trends or patterns based on a large volume of data. It is used for daily hotel operations, especially performance reports.

Predictive hotel analytics

Predictive analytics involves advanced modeling techniques, using data from the past to forecast or predict the likelihood of future events. Predictive analytics is largely based in probabilistic outcomes. Therefore, outcomes are not complete guarantee. However, this approach offers strong guidance for hotel management strategy. For example, using seasonality of past booking trends can help to predict next month’s occupancy and staffing or stock requirements.

Diagnostic analysis

This method uses data as it is to show business results through graphs and other visualizations. It is used to describe what has happened in the past, and offer a reason why breakdowns occurred.

Prescriptive analysis

This technique makes use of predictive analysis to prescribe future actions to be taken. It helps to determine the next-best action to yield the best results for the business, and suggests other strategies to gain market share.

Benefits of hotel analytics

In a dynamic, evolving, and competitive hotel industry, success depends largely on how hotels make use of their data. Effective data collection and analysis methods can give insightful ideas to increase sales and footfall through efficient processes and great marketing.

Enhanced inventory management

There is often a mismatch between the supply and use of items. Either the business is running out of products, or have too much in stock, which is inefficient use of space and can lead to more waste. With effective use of analytics, hotels can keep track of supplies and determine different usage trends. Proper inventory management can help enhance ordering and reduce losses.

Demand prediction

Through data analytics, hoteliers can predict customer behavior patterns and forecast demand more accurately in the future. These insights about their own hotel and competitors can help develop effective pricing models. For instance, revenue management software uses machine learning to understand the best room rate in real-time. It also helps to analyze huge amounts of data from internal and external source to find patterns and anomalies in pricing.

Using predictive analytics for hotel recommendations

Predictive analytics uses a combination of algorithms and machine learning to make forecasts about the future. It is useful to predict which hotel deals consumers might want and might possibly book. It is a great way to understand consumer preferences. By linking predictive analytics with geolocation data, hotels can increase the sale of their recommended deals or offers in real-time. Also, with the continuous use of artificial intelligence and predictive analytics, the hotel industry can provide personalized offers and unique experiences to guests in real-time.

For instance, hotel managers can use analytics to predict when to deliver a pre-ordered room service dinner as the guests enter the hotel’s main lobby or to alert housekeeping to clean a room as soon as a guest leaves. Such geolocation data details give more personalized and luxurious offers and services for a better on-property experience and efficient operations. Additionally, the use of ML-driven alerts for the sending of timely, relevant offers (like upgrades) during guests’ stays only further augment the customer experience to increase loyalty.

Enhanced decision making

Looking for the right type of guests for the hotel is always a major challenge. If this decision is taken properly, it can help in multiple ways. With the help of business analytics, hoteliers can gather information from different sources to understand the demographic of the property’s guests and market to that specific demographic. For instance, if the hotel notices that double occupancy reduces before a particular time period, hoteliers can address this specific demographic and include certain packages that help improve double bookings.

This kind of data analytics also determines the kind of services guests want or need. It is a necessary tool to take decisions on enhancing customers’ offerings and services. Using data analytics as their main strategy, hoteliers can streamline procedures, look at emerging risks, identify guest trends, and build on mechanisms to get feedback. This means that hotel analytics gives hoteliers a competitive advantage when building a brand image.

Proactive monitoring of customer issues

The hospitality industry has many opportunities for negative customer feedback, just like any other industry. Some are anticipated, while most of them come out of nowhere. Those hotels who are prepared for all kinds of situations are the ones that can survive and sustain themselves in a highly competitive world.

Using a competent hotel management system, the leadership team in a hotel can see the issues at hand and come up with quick solutions to resolve the crisis before becomes worse. Early identification of problems using analytics and reporting systems also helps anticipate issues and plan for the future. Hotels can apply it across different aspects of the hotel industry, including; food and beverage, transportation, security, and employee handling.

Pricing management using machine learning

Pricing management is one of the crucial functions of any hotel, with practices such as dynamic pricing becoming a mainstay in the hospitality sector. Using machine learning, hotels can optimize pricing with better research, accurate prediction based on market demand, availability of rooms, location, and willingness to pay.

With the help of data analytics and machine learning, hotels can give customized pricing to different guests, much like some retail e-commerce vendors offer products with personalized pricing.

Top use cases for hotel analytics

There are so many benefits from linking data analytics into all hotel processes.

Customer segmentation

Hotel analytics is used to identify unique cluster groups of guests and conduct different value segmentation for each group. The system can also examine the current customer base within each cluster group and further establish unique groups. For instance, a hotel might have a group of business customers who consistently come to the hotel for one night stays, and some others who stay for longer. With the help of data analytics, these groups can be further segmented into smaller clusters based on affinity and behavioral factors. This, in turn, helps hoteliers to offer customized services to those repeat guests.

Customer profile

Analyzing and building customer profiles is crucial for a hotel to determine which market segments are the most profitable for them. It can also determine which set of guests are likely to respond to promotional offers and marketing as well as separate out different demographics of guests for targeted marketing.

Since customer profiling is such an integral requirement of the hotel business, it needs to be accurate. This can be done with the help of an in-depth analysis of guest demographics and lifestyle characteristics. Hotels can use qualitative and quantitative data like income level, family status, cultural background, interests and hobbies, and age in order to evaluate customer demographics.

Customer association

Using advanced data analytics, hoteliers can see associations between different events and bundle them together in special guest offers as well as for upselling certain items. For instance, a guest buying the house specialty in a restaurant is also likely to buy a glass of a limited wine along with the top-selling dessert. These paired relationships can help bundle menu items into one meal so that it is easier for the guest to order, and at the same time, ensure customer satisfaction. Menu design can also be changed to display combinations together for guests to order. These data associations are a great way to upsell customers and bundle offers together for guest convenience.

Expense management

Running a hotel is has several cost factors. A lot of money is spent every year on the upkeep and maintenance of a hotel, along with the running costs of employee salaries, advertising and marketing budgets, utility bills, and more. Here is where data analytics can help. It helps forecast occupancy rates and based on that hotels can plan on hiring staff, complete renovations in off-peak season, and save money on utilities.

Deciding on room tariff

Hotels have been using yield management for quite some time, offering different room rates to different customers. This has proven to be widely successful too. Today, hotels use big data for revenue management and can offer customized prices and rooms to their guests. In fact, many hotel chains use big data analytics to forecast the optimal room tariff for rooms to fill faster. It is done using revenue management algorithms as the algorithm can handle data faster and combines different data sets for more accurate results.

Management of hotel booking channels

A hotel receives bookings from a variety of sources including online travel agents, website bookings, their own apps, and direct bookings. Data analytics can help determine how many bookings are received from each of the channels, which yields the most bookings, and which ones are not as popular. This analysis is critical in creating the right marketing strategy to boost the hotel bookings on all channels.

Leveraging hotel analytics to optimize business performance

With increasing competition in the hotel industry, staying competitive and coninuting to increase profits requires advanced data analytics. Gone are the days when word-of-mouth publicity and good service were enough to build a good brand image. In today’s technology-driven environment, it is essential to use technology to stay ahead of competitors.

Hotel data analytics is one such tool that hoteliers can leverage to create extra value. It goes beyond just facts and figures using built-in business intelligence tools to forecast trends. Harnessing the power of data analytics and emerging technologies for improvements in guest experience, optimized pricing options, and enhanced operational efficiencies brings the hotel to the next level. Investing in reliable data analytics and reporting systems that are well-managed and built by industry experts will help a hotelier save time and money and, at the same time, improve performance and productivity. Adding powerful analytics will make an everlasting impact on your business.

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