Understanding Hotel Data Analytics

Hotel data analytics involves analyzing various types of data to extract valuable insights. It encompasses factors such as revenue, occupancy, customer satisfaction, and operational performance. Advanced tools and technologies are used to collect, manage, and interpret this data effectively.

“Big data” is the term used to describe the large volume of structured and unstructured data that a business collects every day. By analyzing big data, businesses can gain insights that lead to better business decisions, beat competitors, learn about their customers in-depth, and grow strategically.

According to experts at The Economist, the world’s most valuable resource is no longer oil – it’s data. “Smartphones and the internet have made data abundant, ubiquitous and far more valuable. Whether you are going for a run, watching TV or even just sitting in traffic, virtually every activity creates a digital trace—more raw material for the data distilleries,” says one report.

The concept of big data goes deeper than simply a large amount of information. Industry analysts use “the Three V’s” to define big data more specifically. They are volume, velocity, and variety. Volume refers to the sheer magnitude of volume that is being collected – an estimated 180 zettabytes in the digital universe by 2025. Velocity covers the pace at which this data is being collected, which increases day by day. Each minute, it’s estimated that email users send 156 million messages. Processing this amount of incoming data is a massive challenge for data scientists and companies alike.

Variety is a factor which covers the form in which data is collected. There are two forms of data collection: structured and unstructured. Structured data can be organized easily in a database and is relatively easy to analyze and store. Unstructured data, however, is difficult to sort and includes emails, social media posts, audio and video files, web pages, and more.

There are other dimensions to big data that data scientists use in their analysis. But, you don’t need to be a data scientist to take advantage of the world’s most valuable resource. All you need are the right data tools in place to help your hotel collect and surface actionable insights.

Collecting and Managing Hotel Data

To leverage data analytics, hotels must identify reliable data sources and implement robust data collection methods. Best practices include utilizing property management systems, online booking platforms, and guest feedback systems. Hotels should also invest in secure data storage and management systems.

Big data is absolutely transformational for businesses in every industry.

“Data are now part of every sector and function of the global economy and, like other essential factors of production such as hard assets and human capital, much of modern economic activity simply could not take place without them,” reports consulting firm McKinsey. “The use of big data — large pools of data that can be brought together — will become the key basis of competition and growth for individual firms, enhancing productivity and creating significant value for the world economy.”

In the retail sector, research has shown that embracing big data can improve a company’s operating margin by 60%. In the US healthcare sector, using big data effectively could reduce costs by 8%. From telecommunications to fitness, banking to manufacturing, big data is improving business operations, customer experience, resource optimization, and supply-chain efficiency. The hospitality industry is no different.

There is an ever-increasing amount of guest data intelligence available to hoteliers – website metrics, call center data, consumer profiles, transactional data, and survey data all offer new data streams from which hoteliers can gain insight. Most hotels and resorts already have a substantial amount of data – they just aren’t sure how to utilize it.

Key Metrics and KPIs in Hotel Data Analytics

Hotel data analytics relies on key metrics and key performance indicators (KPIs) to evaluate performance. Revenue-related metrics, such as Revenue per Available Room (RevPAR) and Average Daily Rate (ADR), are crucial. Additionally, occupancy, demand, customer satisfaction, and operational performance metrics provide insights into hotel operations.

Big data may seem like an all-encompassing term, but there are some pieces of information that are not classified as big data. Big data gets collected throughout the guest journey from marketing, guest experience, business intelligence, and personalization. Generic data that is collected at the market level, such as STR reports, do not qualify as big data, including: 

• Market penetration index: this is a measure of a hotel’s occupancy in comparison to the average market occupancy levels. It’s similar to market share, and while it’s useful information, it’s not considered big data. 

• Average Rate Index: this is the same as the market penetration, but instead of comparing occupancy, we compare rates. A rate greater than one indicates that your hotel is, on average, priced above your competitors. 

• Online review content: reviews of your hotel are not considered big data. 

Hotels that misunderstand the concept of big data fail to reap the benefits of the datapoints at their disposal. This infographic outlines some of the most common pitfalls to businesses that aren’t invested in understanding big data.

Source: McKinsey & Co.

Big data allows your hotel adapt to trends in real time, run targeted marketing campaigns and so much more. Data from market intelligence software Parity+ by RateGain shows relevant demand data pertaining to all the factors impacting current and future demand. This analysis allows hotel owners to see accurate hotel market intelligence that combines pricing, demand, reputation, benchmarking, and on-the-books occupancy. Big data impacts everything from operations to marketing, room pricing to strategic investments. 

Harnessing big data for your property

Big data represents a wealth of intelligence at your disposal, but only if you have the tools to use it. Most of the data you receive will be in the form of unstructured data – which is where a tool like Parity+ by RateGain can help. This big data tool organizes large volumes of data into smaller, manageable insights you can use.

The platform pulls live rate shop data from many data sources, including hotel websites and online travel agencies. Then, Parity+ provides actionable intelligence and insights in a clean user interface that’s accessible to even those who have no data analytics training. IT’s the easiest way to eliminate the busy work of manipulating Excel spreadsheets and gathering competitors’ past, current, and future rates by hand – and more reliable than trying to gather this information yourself. Rate Insight pulls in big data from a full list of leisure and corporate events, as well as online reputation, to paint a comprehensive and real-time picture of your market.

Analyzing and Interpreting Hotel Data

Exploratory data analysis techniques and statistical analysis methods are employed to uncover patterns, trends, and correlations in hotel data. Data visualization and reporting tools help transform complex data into visually compelling reports, making it easier to communicate insights to stakeholders.

Big data is a business imperative for the hospitality industry. But, not all big data is valuable. Let real business needs drive how and when you dive into datasets. Be proactive and align your investment with your needs. Seek out solutions that fit those needs, rather than throwing money at a problem.

The sheer volume, velocity, and variety of big data can be overwhelming for hotel managers. This is where a tool like Parity+ is critical for collecting and harvest high-quality data. Use a platform that can surface actionable insights; these tools present findings in clear and simple ways to hoteliers who rarely lack a data background. Keep it simple and always focus on results when communicating with your team, whether you’re working with a general manager, management company corporate, or owner.

Predictive Analytics in the Hotel Industry

Predictive analytics has transformed the hotel industry, offering valuable insights for revenue management and labor management. Let's delve into the most widely used applications of predictive analytics in these areas.

In revenue management, hotels leverage predictive analytics to optimize various aspects of their operations. By analyzing historical data, market trends, and external factors, predictive models accurately forecast future demand. This enables hotels to fine-tune pricing strategies, adjust inventory levels, and maximize revenue. With dynamic pricing, hotels can dynamically adjust prices based on real-time demand fluctuations and competitor pricing. By offering the right price at the right time, hotels can optimize revenue and maximize profitability. Predictive analytics also assists hotels in channel optimization, determining the most effective distribution channels for their inventory. By analyzing historical data and market trends, hotels can allocate inventory to channels that yield the highest revenue potential. Additionally, predictive models identify guest preferences and behavior patterns, enabling hotels to offer personalized upselling and cross-selling opportunities. By recommending relevant upgrades or additional services, hotels can increase their average revenue per guest.

When it comes to labor management, predictive analytics plays a crucial role in optimizing staffing and improving overall efficiency. By analyzing historical data, occupancy rates, and other factors, predictive models accurately forecast staffing requirements. This empowers hotels to schedule the right number of staff members, ensuring operational efficiency while minimizing labor costs. Furthermore, predictive analytics helps hotels analyze employee performance data to identify patterns or trends. By recognizing high-performing employees, areas for improvement, and training needs, hotels can enhance productivity and customer satisfaction. Predictive analytics also assists hotels in predicting employee turnover. By analyzing data related to employee satisfaction, performance, and tenure, hotels can proactively address retention issues and implement strategies to reduce turnover rates. Lastly, predictive analytics aids in workforce planning, taking into account seasonal fluctuations, events, and other demand drivers. This enables hotels to align staffing levels with anticipated demand, preventing overstaffing or understaffing scenarios.

Predictive analytics empowers hotels to make data-driven decisions in revenue management and labor management. By accurately forecasting demand, optimizing pricing strategies, aligning staffing levels, and analyzing employee performance, hotels can enhance profitability, operational efficiency, and guest satisfaction. With predictive analytics as a guiding force, hotels can stay ahead in a competitive industry and achieve sustainable success.

Improving Guest Experience with Data Analytics

Data analytics has become a game-changer for hotels looking to enhance the guest experience. By leveraging the power of data, hotels can personalize their interactions with guests, anticipate their needs, and deliver exceptional service. Let's explore some of the ways hotels can use data analytics to achieve these goals.

One of the key benefits of data analytics is the ability to personalize and target marketing efforts. By analyzing guest data, such as demographics, preferences, and past behaviors, hotels can create tailored experiences. They can segment guests into distinct groups and craft marketing messages, offers, and recommendations that resonate with each group. This personalized approach not only engages guests on a deeper level but also increases their satisfaction and loyalty.

Data analytics also plays a crucial role in enhancing customer service. By analyzing guest preferences, needs, and expectations, hotels can deliver personalized and anticipatory service. For instance, if a guest has a preference for a specific room type or amenities, hotels can ensure those preferences are met. Moreover, by using data analytics to identify service gaps or areas for improvement, hotels can promptly address any issues and provide exceptional customer service.

Real-time guest feedback analysis is another valuable application of data analytics. Hotels can monitor and analyze guest feedback from online reviews, social media mentions, and guest surveys in real-time. This allows hotels to gain insights into guest satisfaction levels and identify areas that require attention. By proactively responding to guest concerns, hotels can demonstrate their commitment to guest satisfaction and foster positive experiences.

Data analytics also helps hotels optimize the guest journey. By analyzing guest behavior at various touchpoints, such as booking, check-in, and check-out, hotels can identify pain points and areas of friction. Armed with this knowledge, hotels can streamline processes, offer self-service options, and ensure a seamless and hassle-free experience for guests.

Data analytics uncovers revenue-generating opportunities while improving the guest experience. By analyzing guest preferences, purchase history, and behavior patterns, hotels can offer personalized recommendations for additional services, amenities, or experiences. This not only enhances the guest experience but also drives incremental revenue for the hotel.

Hotels with loyalty programs can optimize their offerings through data analytics. By analyzing member data, hotels can gain insights into guest behavior within the program. This enables them to identify trends, preferences, and opportunities for enhancing the loyalty program experience. Personalized offers, tailored rewards, and exclusive benefits based on guest preferences and engagement can further strengthen guest loyalty.

Data analytics empowers hotels to create personalized experiences, anticipate guest needs, and optimize every aspect of the guest journey. By leveraging data-driven insights, hotels can provide exceptional customer service, identify areas for improvement, and create memorable experiences that foster guest loyalty and advocacy. With data analytics as a guiding force, hotels can stay ahead of the competition and deliver unparalleled guest experiences.

Leveraging Competitive Insights through Data Analytics

Hotels can leverage data analytics to uncover valuable competitive insights that can inform their strategic decisions and drive their success in the market. Here are some ways hotels can use data analytics to gain a competitive edge:

1. Benchmarking and Competitive Analysis: Data analytics enables hotels to benchmark their performance against industry standards and competitors. By comparing key metrics such as revenue per available room (RevPAR), average daily rate (ADR), and occupancy rates, hotels can identify areas where they are outperforming or lagging behind their competitors. This analysis provides insights into pricing strategies, market positioning, and overall competitiveness.

2. Market Trends and Demand Analysis: Data analytics allows hotels to monitor and analyze market trends and shifts in demand patterns. By analyzing historical data, market indicators, and external factors, hotels can identify emerging trends and changing customer preferences. This information helps hotels stay ahead of the curve, adjust their offerings, and capitalize on new opportunities before their competitors.

3. Pricing and Revenue Management: Data analytics plays a crucial role in pricing and revenue management strategies. Hotels can analyze market data, demand forecasts, and competitor pricing to optimize their pricing strategies. By identifying pricing gaps and opportunities, hotels can adjust their rates to maximize revenue and gain a competitive advantage.

4. Reputation Management and Guest Reviews: Data analytics enables hotels to analyze guest reviews and online feedback to understand their reputation in the market. By monitoring sentiments, identifying areas of strength and improvement, and tracking guest satisfaction levels, hotels can make data-driven decisions to enhance their guest experience and outshine their competitors.

5. Competitive Positioning and Marketing Strategies: Data analytics provides insights into competitors' positioning, marketing strategies, and customer acquisition channels. By analyzing competitor data, hotels can identify gaps in the market, differentiate their offerings, and refine their marketing efforts to target specific customer segments effectively.

6. Distribution Channel Analysis: Data analytics helps hotels evaluate the performance of different distribution channels. By analyzing data on bookings, revenue, and channel costs, hotels can identify the most effective channels for their target market. This allows them to optimize their distribution strategy and allocate resources to channels that yield the highest returns, gaining a competitive advantage in the distribution landscape.

7. Predictive Analytics for Competitive Intelligence: Predictive analytics models can be used to forecast competitor performance, anticipate market trends, and identify potential disruptions. By leveraging predictive analytics, hotels can make data-driven decisions and develop proactive strategies to stay ahead of their competitors.

Data analytics empowers hotels to uncover competitive insights, identify market trends, optimize pricing strategies, and refine their marketing efforts. By leveraging data-driven intelligence, hotels can make informed decisions, adapt to changing market dynamics, and gain a competitive edge in the industry.

Data Privacy and Security Considerations

Data security and privacy concerns pose significant challenges to the hotel industry, given the vast amounts of personal and financial information they handle. Hotel owners must take proactive steps to mitigate these risks and protect guest data. Let's explore some of the biggest concerns and the measures hotel owners can implement to address them effectively.

Data breaches are a major worry for hotels, with hackers constantly seeking to exploit vulnerabilities and gain unauthorized access to sensitive information. To combat this threat, hotel owners should prioritize robust data security measures. This includes implementing strong encryption protocols, secure network infrastructure, and firewalls to safeguard guest data. Regular updates and patching of systems are vital to address known vulnerabilities promptly. Conducting regular security audits and vulnerability assessments helps identify and rectify any weaknesses in the hotel's systems and infrastructure. Equally important is educating employees on security practices, including password management and how to recognize and respond to phishing attempts.

Compliance with data protection regulations is another crucial concern. Hotels must adhere to laws such as the General Data Protection Regulation (GDPR) in the European Union and the California Consumer Privacy Act (CCPA) in the United States. This requires hotel owners to gain a thorough understanding of the regulations applicable to their operations and ensure their data practices align with legal requirements. Obtaining explicit consent from guests for data collection, providing transparent privacy policies, and implementing data retention and deletion practices in compliance with regulations are essential steps to safeguard guest privacy.

Insider threats pose an ongoing risk to data security. Employees or contractors with access to guest data can potentially misuse or mishandle the information. To mitigate this risk, hotel owners should limit access privileges, granting employees access only to the data and systems necessary for their roles. Regular monitoring and auditing of system access, along with the implementation of audit logs, help identify and address unauthorized or suspicious activities. Employee training and awareness programs are crucial to educate staff about the importance of data security and privacy, as well as the risks associated with insider threats. Confidentiality agreements can be implemented to ensure employees understand their responsibilities in protecting guest data and the consequences of breaching those responsibilities.

Data security and privacy concerns require vigilant measures from hotel owners. By implementing robust data security practices, ensuring compliance with regulations, addressing insider threats through access control and employee awareness programs, and fostering a culture of data security, hotels can protect guest data and maintain their trust. Regular security audits, employee training, and staying informed about evolving security practices are essential for hotels to effectively safeguard data and prioritize guest privacy.

Challenges and Future Trends in Hotel Data Analytics

Hoteliers face several significant challenges when it comes to collecting data, uncovering insights within that data, and acting upon those insights. Here are some of the biggest challenges they currently encounter:

1. Data Fragmentation: Hotels often collect data from various sources and systems, including property management systems, online booking platforms, guest feedback systems, and social media. However, this data is often fragmented and stored in different formats, making it challenging to consolidate and analyze effectively. Hoteliers struggle to integrate and harmonize data from disparate sources to gain a comprehensive view of their operations and guests.

2. Data Quality and Accuracy: Ensuring the quality and accuracy of data is a persistent challenge. Hotels may encounter issues such as duplicate entries, incomplete records, or outdated information. Inaccurate or unreliable data can lead to flawed insights and incorrect decision-making. Maintaining data hygiene, implementing data validation processes, and regular data cleansing are necessary steps to address this challenge.

3. Limited Data Analysis Resources: Hoteliers often face resource constraints, such as a shortage of skilled data analysts or dedicated analytics teams. Analyzing large volumes of data requires expertise in data manipulation, statistical analysis, and data visualization. Hoteliers may struggle to allocate sufficient resources and expertise to derive meaningful insights from their data.

4. Lack of Data Integration and Systems Compatibility: Integrating data from different systems and platforms can be complex, particularly if the systems are not designed to work seamlessly together. Hoteliers may face challenges in connecting and harmonizing data from property management systems, customer relationship management systems, and other data sources. This lack of integration hinders the ability to gain holistic insights from the data.

5. Data Security and Privacy Concerns: While data collection is essential for analysis, hoteliers must navigate the complex landscape of data security and privacy regulations. Compliance with regulations such as the GDPR and CCPA adds complexity and requires robust data protection measures. Hoteliers need to strike a balance between collecting and utilizing guest data while respecting privacy rights and maintaining data security.

6. Actionability of Insights: Even when meaningful insights are uncovered, translating those insights into actionable strategies can be a challenge. Hoteliers need to bridge the gap between data analysis and operational decision-making. Transforming insights into concrete actions may require organizational alignment, changes in processes, or investments in new technologies, which can be a complex undertaking.

7. Change Management and Cultural Shift: Implementing a data-driven culture within an organization requires a mindset shift and a change in established practices. Hoteliers may encounter resistance to change or lack of understanding among employees regarding the benefits of data-driven decision-making. Overcoming this challenge involves effective change management strategies, communication, and training to foster a data-driven mindset at all levels of the organization.

To overcome these challenges, hoteliers can consider several strategies. This includes investing in data integration solutions and platforms that facilitate data consolidation and harmonization. Implementing data governance practices and data quality management processes helps ensure data accuracy and reliability. Collaborating with third-party analytics providers or hiring skilled data analysts can enhance the ability to uncover insights and derive actionable recommendations. Additionally, promoting a data-driven culture and providing adequate training and resources to employees fosters a collective understanding and utilization of data-driven insights for decision-making.

By addressing these challenges and embracing data-driven practices, hoteliers can unlock the full potential of their data, uncover valuable insights, and take proactive actions to optimize operations, enhance the guest experience, and drive business success.