Data analytics is the latest buzzword in hotel technology trends. But many hotels, from large chains to boutique properties, may struggle to figure out the best way to use it.
While some hotels may already know the role data analytics plays in revenue management, they may not see its wider potential. Data analytics can help tackle a number of business challenges, from guest-facing to back-of-house pain points, boosting performance, reputation, and guest loyalty.
Here are nine powerful ways hotels can use data analytics, along with the benefits and challenges that come with it.
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How is data analytics used in the hotel industry?
An important hotel technology trend, data analytics is a process that involves collecting, analyzing, and interpreting data. In a hospitality context, it’s used to help hoteliers make more informed decisions to improve performance, operations, and the guest experience.
Large chains rely heavily on big data—large volumes of data sets collected from sources like guest communications, social media, booking platforms, and website traffic. By using big data, these hotels can uncover patterns (e.g., weekend booking spikes) and trends (e.g., growing demand for contactless check-ins). These insights help drive key decisions, from setting optimal room rates to predicting guest preferences.
The hotel industry uses various types of data analytics. For example, predictive analytics is popular amongst revenue managers because it helps them foresee an unexpected uptick in demand and adjust room rates to attract bookers, increasing conversions, margins, and revenue.
What are the benefits of hotel data analytics?
While current research shows data analytics doesn’t have a direct impact on guest satisfaction, which is too complex to measure, it can benefit hotels in many other ways:
- Enhanced decision-making. Data-driven insights help eliminate biases and guide strategies based on actual behavior and past transactional information to help hotels make more objective decisions.
- Personalized guest experience. By studying historic guest behavior, transactions, and social media trends, hotels can suggest room types, meals, upgrades, and more to suit individual guests, boosting loyalty, and encouraging repeat bookings.
- Streamlined operations. Data analytics makes maintenance, housekeeping, and inventory management more efficient by identifying bottlenecks and optimizing workflows to reduce costs.
- Optimized rates, occupancy, and performance. Hotels can maximize revenue and margins through predictive analysis and dynamic pricing models based on historic bookings, current trends, and market shifts.
- Targeted marketing. Analyzing guest demographics, booking behavior, and preferences means hotels can create targeted marketing campaigns for specific audiences to improve engagement and ROI.
- Reduced carbon footprint. Data insights help identify areas for energy and resource optimization, like energy-efficient lighting and waste reduction strategies to support sustainability goals.
- Improved competitive analysis. Hotels can benchmark their performance against competitors with data analytics by analyzing market trends, pricing strategies, and customer sentiment to gain an edge.
- More accurate forecasting. With data analysis, hotels can predict demand, manage staffing, optimize inventory, and prepare for peak seasons, increasing resource efficiency and boosting profitability.
What are the challenges of hotel data analytics?
While hotels have many reasons to use data analytics, they may also feel deterred by it. Here are common challenges that may cause some hoteliers to be late adopters of this rising hotel tech trend.
- Lack of expertise. Setting up and integrating data analytics can be complex, requiring large hotels to hire data analysts to manage comprehensive data sets. Small hotels won’t need experts but might require onboarding help. Hiring a consultant is one solution. A data analytics software company may also support setting up systems, ensuring data quality, and interpreting results.
- Insufficient data protection. Cyber threats put the security and privacy of customer data at risk. Data collection requires compliance with regulatory standards. While large hotels may need to invest in additional expertise to protect extensive data, smaller hotels can do due diligence to ensure a software’s security and compliance, mitigating risk.
- Inadequate change management. Some managers may resist data analytics adoption out of fear technology will replace humans or that they’ll lose the human touch. Balance is key. For example, revenue managers can override dynamic pricing systems when they have local event knowledge the tech lacks. Front desk staff can use information from guest conversations to enhance personalization tools and offer more tailored experiences.
Despite these challenges, the right tools can empower hotels to embrace data analytics, transforming operations and enhancing the guest experience.
9 use cases of data analytics in hospitality
Data analytics doesn’t start and stop with rate optimization or predictive analysis. It offers diverse use cases for hotels of any size. Here are nine ways hoteliers can use data analytics.
1. Dynamic pricing and demand forecasting
Dynamic pricing and demand forecasting enable hotels to adjust room rates in real time by analyzing various data points, such as historical booking trends, competitor rates, and market demand. Together, they ensure hotels optimize revenue during peak periods and remain competitive during low-demand times.
Tools like IDeaS, a robust revenue management system (RMS), use advanced data analytics to streamline dynamic pricing and demand forecasting.
By integrating historical and current booking data, market trends, and competitor insights, IDeaS automates pricing updates and demand forecasts. It helps hotels make data-driven decisions about inventory management and pricing strategies to boost revenue.
For example, IDeaS enables hotels to optimize their business mix by targeting more profitable guest segments and focusing on room-type and rate-code-specific pricing. Its machine-learning capabilities continuously adapt pricing recommendations based on real-time booking behaviors and customer insights.
Other tools, like Wheelhouse, combine data-driven and rule-based strategies to customize dynamic pricing. It analyzes billions of data points daily, including local demand and competitor rates, to deliver precise pricing recommendations.
Wheelhouse also takes into account local events, like conferences or holidays that may impact demand, allowing hotels to adjust pricing accordingly. It also enables hotels to tailor pricing strategies based on their risk tolerance and market goals, while implementing changes quickly.
2. Guest experience personalization
With data spread across multiple sources—customer relationship management (CRM), property management systems (PMS), and social media—compiling and analyzing guest insights manually to personalize experiences is time-consuming and error-prone. This can lead to missed upselling and loyalty opportunities and guests feeling misunderstood.
Using data analytics, hotels can centralize guest data, uncover actionable insights, and deliver customized experiences.
For instance, hotels can analyze historic booking data, social media mentions, and past interactions to offer personalized messaging, upsells, and services.
This might include recommending a spa package based on a guest’s prior preferences or setting up a room with extra-soft pillows for returning visitors who have requested them before.
Hotels can understand individual preferences and create tailored offers with the guest experience platform, Like Magic, that groups data into a single, unified profile, automatically eliminating duplicates and inaccuracies. With Like Magic, a hotel could recognize a frequent early check-in request and proactively offer this service as a perk for loyalty.
Similarly, guest experience platforms like Duve offer guests a dedicated app where hotels can tap into behavioral analytics and deliver tailored offers via push notifications. For example, if a guest interacts with the hotel’s spa offerings in the app, the tool can analyze their behavior and tailor a package based on these insights.
3. Feedback analysis
Since feedback can come in all forms—in-house surveys, OTA reviews, and social media comments—collecting, analyzing, and interpreting the data can be resource-intensive and prone to human bias and mistakes.
Instead, data analytics does the heavy lifting. Hotels can gauge guest sentiment and glean the areas for improvement with more accuracy in less time.
With Boom, a PMS powered by AI, hotels use automation to request feedback and collect and analyze the data. Boom’s sentiment analysis tool includes post-arrival check-up messages to gauge guest satisfaction and during-stay surveys to spot friction points and delight drivers.
This immediate feedback collection and analysis enables hotels to identify and fix potential issues and double down on what works, enhancing the stay while guests are still in-house.
For example, an analysis of during-stay survey data can reveal multiple guest complaints about sloppy room cleaning, particularly excessive dustiness. Hotels can act immediately, deploying extra cleaning while studying housekeeping processes to improve the service.
On the flip side, feedback analysis may find that several guests enjoyed a local tour proposed by the digital concierge app. The hotel can create tailored upsells based on the data, offering mini tours with local guides.
4. Predictive maintenance
Predictive maintenance analyzes data patterns, so hotels can determine when machinery—such as HVAC systems—requires maintenance. This prevents costly downtime and improves operational efficiency while extending the lifespan of equipment.
With solutions like Optii, hotels can get actionable insights into the cost and performance of machinery. This allows hoteliers to manage assets more effectively. For example, Optii can predict elevator maintenance needs, ensuring timely repairs and optimal scheduling.
By tracking and documenting each asset’s life cycle, Optii also helps hotels optimize costs by comparing the total cost of ownership across different asset classes. This data-driven approach empowers management to make smarter investment decisions, prolong asset life, and maintain smooth operations across the property.
5. Targeted marketing
With data analytics, hotels can know which marketing channels, media, and messaging resonate with each guest segment. These insights allow the hotel to double down on specific strategies and replicate results to increase conversion rates, optimize marketing spend, and boost ROI.
The CRM solution, Bookboost, enables hotels to create tailored email content that engages the guest. Bookboost uses segmentation analysis to ensure hotels craft personalized content for each guest group.
For example, hotels can offer business guests packages that include private meeting rooms, while families receive complimentary breakfasts to help them perceive a cost-saving opportunity.
To understand guest preferences, hoteliers can try SHR, a smart hotel technology solution that combines CRM with data analytics. SHR helps hotels avoid guest data duplication to enhance guest insights. This means hotels create personalized offers that are more accurate, based on preferences and past interactions.
Another hospitality tech firm, Cendyn, offers both a CRM and a customer data platform (CDP). They enable hotels to optimize guest data management, crafting more targeted marketing campaigns and improving sales.
6. Labor optimization
Hotel data analytics helps hotels optimize labor and operations by using data to forecast staffing needs and improve scheduling and productivity. For example, data analytics can predict peak occupancy periods, allowing hotels to schedule the right number of front desk and housekeeping staff to meet guest demands without overstaffing.
An operations management platform like Actabl can help hotels boost staff and operational efficiency. Actabl provides hotels with a business intelligence tool that delivers performance and staffing insights and recommendations.
For example, hotel managers will have greater visibility into real-time hotel performance metrics, like occupancy rates and revenue per available room (RevPAR), giving them the insights needed to make more informed staffing decisions. Actabl also analyzes open shifts so managers can organize staff to fill gaps while assessing overtime risks to reduce costs.
Actabl’s data analysis can even benchmark wages across a hotel’s portfolio of properties and against the market, enabling the creation of more competitive offers to attract and retain talent.
7. Amenities and supplies management
Data analytics can help hotels know how to optimize supplier relationships and inventory management (from towels and food items to cleaning products) so hotels can avoid waste and prevent shortages.
To do this, hospitality tech tools like Operto estimate laundry needs, even across multiple properties, while the housekeeping tech solution, Flexkeeping, relies on data analytics to track and manage housekeeping inventory.
Flexkeeping’s real-time consumption data analysis helps hotels avoid in-room amenities scarcity, like towels and soap, or supplies like disinfectants and detergents.
8. Energy management
Hotels can rely on data analytics for more efficient energy control to save resources, enhance comfort, and prevent damage while ensuring guest safety.
With real-time data on energy consumption, room occupancy, and environmental conditions, hotels can identify inefficiencies and opportunities. For example, hotel tech like Operto integrates with a hotel’s PMS to analyze and automatically adjust heating and cooling based on factors such as occupancy, saving significant costs.
The smart device and data analytics tool, Minut, monitors and analyzes cigarette smoke, occupancy, noise, temperature, and humidity in hotel areas. By notifying hotels immediately of unhealthy environment levels, Minut helps hotels ensure comfort while keeping guests safe.
9. Competitive analysis
Data analytics can help hotels gain an edge on rival properties by providing insights into competitor strategies.
Boom offers hoteliers visual benchmarking comparisons to see how they stack up against competitors and industry standards. Its data-driven insights are particularly useful for understanding where hotels can outperform the competition.
Hotels can also get real-time rate parity analysis and insights with SiteMinder’s business intelligence tool. SiteMinder enables hotels to know when it makes sense to adjust pricing, making their offers more enticing against the competition.
This analysis includes like-for-like rate comparisons and cross-channel analysis, reducing disparity to boost customer trust. SiteMinder also offers performance reports that help hotels identify trends to optimize rate strategies over time.
Smarter hospitality with data analytics
Data analytics offers solutions to a variety of challenges for hotels. From supply management, dynamic pricing, and feedback analysis to competitive analysis, targeted marketing, and predictive maintenance, hotels of all sizes can embrace its diverse capabilities.
While there are many benefits of data analytics—enhanced decision-making, optimized operations, and improved guest personalization—challenges remain. Expertise gaps, data security concerns, and resistance to change can slow adoption. But, with the right tools and strategies, hotels can overcome these hurdles to enhance efficiency, profitability, and the guest experience.
Hoteliers who embrace data analytics tools can stay ahead of market trends as they build stronger guest relationships and create lasting competitive advantages.
Are you a hospitality tech startup looking to build your brand’s reputation through strategic PR? Drop us a line.
Hotel data analytics FAQS
What is hotel data analytics?
Hotel data analytics involves collecting, analyzing, and interpreting data to improve hotel operations, guest experiences, and revenue. It removes manual analysis and human bias to identify patterns and trends, optimize pricing, and make more effective decisions.
Do hotels need a data analyst?
Hiring a data analyst depends on a hotel’s size, their data volume, and the complexity involved. A hotel chain might need an entire team of data analysts to handle large amounts of data spread across different properties, regions, and software. But a single boutique hotel can probably manage a data analytics tool on their own, without needing a specialist.
How can you leverage data analytics in the hospitality industry?
Data analytics in the hospitality industry can optimize pricing, improve marketing strategies, personalize guest experiences, forecast demand, and enhance operational efficiency. It can also aid in energy management, staffing optimization, and understanding how to outperform the competition.
What are the most important hotel data analytics KPIs to track?
Since data analytics offers a variety of use cases, the KPIs to track are numerous. For example, key KPIs include average daily rate (ADR), occupancy rate, Revenue per Available Room (RevPAR), customer acquisition cost (CAC), and guest satisfaction score.
