Historical data is the backbone of hotel sales benchmarking, helping hotels make smarter decisions based on past performance. By analyzing trends in metrics like occupancy, ADR (Average Daily Rate), and RevPAR (Revenue per Available Room), hotels can:
- Identify trends: Spot seasonal patterns and long-term growth or decline.
- Improve forecasting: Use past data to predict future demand and revenue.
- Refine strategies: Adjust pricing, staffing, and marketing based on proven insights.
- Compare performance: Benchmark against competitors and industry standards.
Key Challenges:
- Data silos: Isolated data across departments limits insights.
- Unusual events: Events like COVID-19 disrupt historical trends.
- Data quality: Inconsistent or incomplete data skews benchmarks.
Solutions:
- Standardize data collection and formatting.
- Use detailed data for deeper insights.
- Leverage automation tools to integrate historical and real-time data.
Combining historical data with modern tools helps hotels boost revenue, improve efficiency, and stay competitive in the market.
Hotel Benchmarking with STR: Exploring Performance Beyond Top-line Data
Common Problems with Real-Time Hotel Sales Benchmarking
When it comes to blending historical data with real-time metrics, hospitality professionals face a host of challenges. These issues can disrupt decision-making and compromise the accuracy of benchmarking efforts.
Data Silos and Inconsistent Formats
A major roadblock in hotel sales benchmarking is the existence of data silos – isolated data repositories controlled by different departments. These silos make it difficult to integrate historical and real-time metrics seamlessly. In fact, siloed data can lead to revenue losses of up to 30% annually, and 33% of finance teams report difficulties accessing or consolidating such data.
Adding to the complexity are inconsistent data formats. For instance, one system might store Average Daily Rate (ADR) data in one format, while another uses a completely different structure. This forces analysts to spend time reformatting data instead of deriving actionable insights. These inefficiencies not only waste valuable time but also increase the risk of errors, further eroding the reliability of benchmarks.
How Unusual Events Affect Historical Trends
Unpredictable events can wreak havoc on years of carefully collected historical data, making it unreliable for benchmarking. A stark example is the COVID-19 pandemic, which dramatically disrupted the hospitality industry.
The pandemic’s effects were staggering: U.S. hotels lost over $46 billion since February 2020, and 4.8 million hospitality and leisure jobs were eliminated during that time. Occupancy rates in U.S. hotels dropped below 20% in late 2020, while daily room occupancy, ADR, and RevPAR fell by approximately 74%, 47%, and 86%, respectively. Between March and May 2020 alone, the industry saw revenue losses exceeding $30 billion.
Such extraordinary events distort historical trends, making it harder to rely on past data for current decision-making. Even beyond these events, maintaining high-quality, comparable data remains essential for accurate benchmarks.
Problems with Data Quality and Comparability
Even when data silos are addressed and extraordinary events are considered, challenges with data quality and comparability persist. Accurate and consistent data is the backbone of reliable benchmarking, but achieving this is easier said than done. As Caroline Tarre, Customer Success Manager at Benchmarking Alliance, aptly put it:
"Making business choices without solid data is like driving in the dark without headlights on."
One significant issue is data decay. B2B contact data, for instance, degrades at an average rate of 22.5% annually. For hotels tracking guest preferences, corporate accounts, or market segments, this natural decay can lead to outdated and unreliable benchmarks.
Inconsistent reporting adds another layer of complexity. Overly detailed or inconsistent metrics make it difficult to measure performance accurately. Moreover, selecting appropriate competitive sets is a constant struggle. Comparing a luxury resort to a limited-service airport hotel, for example, offers little value. Factors like market segment, location, size, amenities, and target audience all play a role in determining meaningful benchmarks, but finding genuinely comparable properties is no small task.
Incomplete datasets further undermine benchmarking efforts. When some properties in a competitive set provide detailed data while others share only limited metrics, the resulting benchmarks become skewed and unreliable. This problem becomes even more pronounced when combining historical data with real-time metrics, as data completeness often varies by time period.
These challenges underscore the importance of robust data integration and quality control to ensure reliable real-time benchmarks. Without addressing these issues, benchmarking efforts risk being fundamentally flawed.
How Historical Data Improves Real-Time Benchmarking
Historical data plays a key role in refining real-time benchmarking, turning past performance into actionable insights that help businesses make smarter decisions. When analyzed effectively, it bridges the gap between raw metrics and meaningful strategies.
Finding Trends and Seasonal Patterns
Historical data is invaluable for spotting trends and seasonal patterns that might not be obvious in everyday operations. By diving into past booking data and layering it with factors like weather trends, local events, and customer feedback, hotels can accurately identify their peak and off-peak periods.
This goes beyond simply tracking occupancy rates. For example, analyzing booking trends during summer events can guide promotional campaigns. Peak seasons often align with holidays, festivals, and local happenings, while slower periods call for strategic discounts and targeted offers. In fact, hotels leveraging advanced analytics have reported a 15% boost in bookings during traditionally slow months, thanks to these tailored efforts.
Ricardo Vladimiro, Data Science Lead at Miniclip, highlights the importance of such analysis:
"Time series analysis allows both descriptive and predictive analytics. Many industries, mine included, have very noisy time-based datasets and many dashboards filled with time series data. Being able to separate trend, seasonality and error and then predict where will be in x units of time is very powerful from a decision-making point of view."
Adding Context to Key Performance Metrics
Metrics like occupancy, ADR (Average Daily Rate), and RevPAR (Revenue Per Available Room) only tell part of the story without historical context. Looking at past data helps uncover whether these figures indicate growth, stagnation, or decline.
Take a 65% occupancy rate in July, for instance. On its own, it might seem acceptable. But if historical data shows an 80% occupancy for the same month over the past three years, this year’s performance signals a concerning drop. On the flip side, if past averages hovered around 55%, a 65% rate would represent strong growth. This kind of insight is critical for accurate benchmarking, especially since nearly 75% of hoteliers rely on data analytics to guide their decisions. Historical data also allows for performance breakdowns by room type, booking channel, or market segment, offering more targeted insights.
Such contextual understanding doesn’t just clarify the present – it lays the groundwork for future strategies.
Using Past Data for Future Sales Planning
Historical data isn’t just about understanding the past; it’s a powerful tool for planning ahead. By analyzing previous performance trends, hotels can fine-tune pricing strategies, staffing levels, and marketing efforts with precision .
The forecasting accuracy made possible through historical data is impressive. For example, LARC’s U.S. RevPAR model achieved an R-squared of 98.7%, with a standard error of just 2.7%, in back-tests going back to 2000. This level of precision helps hotels allocate resources effectively, based on anticipated demand.
Revenue forecasting also provides a holistic view of a hotel’s expected performance, covering revenue, expenses, and profits. Such insights allow businesses to set realistic goals and refine their strategies. For example, LARC projects U.S. RevPAR to rise by 2.7% in 2025, reaching $102.29, with Hotel EBITDA increasing by 1.1% and Hotel Values by 3%. These projections, rooted in historical trends, help hotels prepare for the future with confidence.
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Solutions and Best Practices for Using Historical Data
Using historical data for hotel sales benchmarking requires a structured approach to tackle common challenges like data silos, inconsistencies, and quality issues. The key is to implement strategies that ensure reliable comparisons and meaningful insights from past performance.
Standardizing Data Collection and Formatting
Consistency is the backbone of effective benchmarking. Without standardized data collection and formatting, comparing performance across different periods becomes a guessing game. As Rebecca Boyle from Thynk puts it:
"Data standardization is becoming increasingly important for hotels to make data-driven decisions… Data standardization will help to streamline your data and save you time and resources in the long term."
Start by conducting regular data audits – annually, if possible – to spot inconsistencies in how data is recorded, stored, and categorized. For example, review room type classifications and revenue reporting methods to ensure they align across time periods. Providing clear guidelines and training for staff, from front desk agents to revenue analysts, is crucial to maintaining data quality over the long term.
Adopting the FAIR principle – making data Findable, Accessible, Interoperable, and Reusable – can also help ensure that your data remains useful and comprehensible for future teams.
Using Detailed Data for Better Insights
Diving into detailed historical data reveals trends and opportunities that aggregate figures often miss. By segmenting data – such as by room type, booking channel, or guest segment – hotels can pinpoint areas where they excel and identify where they need improvement.
Benchmarking against your own historical performance is particularly effective. Research shows that organizations that benchmark broadly outperform others by a factor of 2.4. George Polyard, VP of Customer Experience at ComOps, highlights the value of this approach:
"Where there is a lot of value is comparing your performance to your own past performance. It removes all of that ambiguity and controversy within benchmarking."
Using Tools for Easy Data Access
Once your data is standardized and detailed insights are extracted, the next step is to leverage tools that simplify analysis. Modern hotel sales automation platforms combine historical data with real-time insights, presenting everything in easy-to-navigate dashboards. This streamlines decision-making and saves time.
Jeff Hinkle, VP of Revenue Management at Stonebridge Companies, explains the benefits of such tools:
"Business Intelligence’s live insights and automated reports have transformed the revenue manager role from being a report puller to being a strategist that actively shapes our properties’ performance."
Integration is key – connecting property management systems, CRM platforms, and financial tools ensures smooth data flow while reducing manual effort. David Gill, Director of Revenue Management at Silverado Resort and Spa, shares:
"Business Intelligence gives you all the critical factors for revenue positioning in one platform. We can see operational hard dollar improvements in time. I have a revenue analyst who no longer has to put two hours into building a report."
Advanced features like intuitive search and multi-user access further enhance usability. Platforms such as M1 Intel’s Matrix seamlessly integrate historical and current data, offering collaborative dashboards that empower sales teams to make informed, data-driven decisions across their properties.
How Hotel Sales Automation Platforms Help with Benchmarking
Modern hotel sales automation platforms simplify the integration of historical and real-time data, providing sales teams with immediate insights. By merging past performance with current operations, these platforms give hotels a complete view to make informed decisions and boost results.
Combining Historical Data with Current Data
These platforms eliminate the need for manual data handling by automatically integrating historical and real-time data, significantly reducing errors from managing multiple systems. This seamless process enables sales teams to make decisions based on accurate, up-to-date information. In fact, 73% of hotel executives rank real-time analytics as "extremely important" to their competitive strategies.
Take Marriott International as an example. By implementing real-time analytics across its properties, Marriott achieved a 22% reduction in check-in times, a 17% improvement in staff productivity, a 31% boost in guest satisfaction scores, and $287 million in additional revenue through personalized upselling. This was made possible by combining historical guest preferences with real-time booking trends to craft tailored offers.
The technical backbone of this integration is an API-first architecture. Middleware solutions connect older systems, ensuring that historical data aligns seamlessly with current performance metrics.
Features That Support Data-Based Decisions
Hotel sales automation platforms transform historical data into actionable insights through key features:
- Advanced search tools, like those in M1 Intel’s Matrix platform, allow sales teams to quickly find relevant accounts, contacts, or opportunities from vast historical records.
- Kanban-style interfaces visually map sales pipelines, helping managers spot deviations from successful past trends and adjust strategies.
- Multi-user access ensures teams can simultaneously collaborate on historical insights. For instance, a sales coordinator identifying patterns in past group bookings can instantly share findings with revenue and general managers.
These platforms also support portfolio-wide reporting, enabling operators to analyze performance across multiple locations. By comparing key metrics from individual properties, management can replicate successful strategies across the portfolio.
Supporting Teamwork Across Hotel Portfolios
Sales automation platforms go beyond individual features to enhance collaboration across properties. For multi-property groups, centralized data repositories make historical insights accessible to all locations while preserving property-specific details.
- Centralized guest profiles enable cross-selling opportunities. For instance, if a frequent business traveler at one property inquires about a wedding venue, the system can suggest sister properties known for hosting weddings.
- Shared email templates ensure consistent brand messaging. Properties can review past campaign performance, identify effective approaches, and adapt them for their specific needs.
Personalized emails, for example, achieve 26% higher open rates and generate six times more transactions. Additionally, repeat guests spend 67% more than new ones. Sharing data on guest preferences and successful communication strategies benefits the entire portfolio.
One success story comes from Bay View Collection, which used historical data to target guest segments with an email campaign promoting discounted rates during a slow season. The result? Nearly $10,000 in direct bookings.
Courtney Driessen, Lodging and Revenue Manager at Fireside Resort Jackson Hole, highlights the value of historical context:
"Cloudbeds, we have historical data that we can use to look forward. We’re able to be more involved in the market than we were previously. It’s such a powerful thing to have at my fingertips."
Platforms like M1 Intel’s Matrix support this collaborative approach with brand-agnostic tools, allowing management companies to maintain consistent benchmarking practices across multiple brands. Moreover, their full data ownership model ensures properties retain control over their historical information while benefiting from shared insights. This balance helps hotel groups maximize the value of their collective data, driving smarter benchmarking decisions through comprehensive integration.
Conclusion: The Importance of Historical Data in Hotel Sales Benchmarking
Historical data plays a key role in hotel sales benchmarking, serving as the foundation for smarter decision-making and driving revenue growth. By examining past performance, hotels gain a clearer picture of what works, helping them evaluate current strategies and set achievable goals based on established trends rather than speculation.
In today’s competitive hospitality landscape, where growth can be modest and challenges are ever-present, relying on data-driven insights has become more important than ever. Combining historical data with real-time analytics allows hotels to spot seasonal trends, adapt to shifting market demands, and set realistic performance benchmarks aligned with their unique market conditions.
The value of historical data increases significantly when paired with automation tools like M1 Intel’s Matrix. These tools simplify data collection and access, enabling sales teams to make faster, more informed decisions. Centralizing historical insights also encourages collaboration across hotel portfolios. Management teams can replicate successful strategies, address underperforming areas, and allocate resources more effectively by leveraging patterns and trends that are already proven.
Analyzing key metrics such as occupancy, ADR, and RevPAR against historical data sharpens the accuracy of benchmarking efforts. Additionally, effective use of historical data can enhance operational efficiency. With labor costs accounting for 30%-40% of total hotel expenses, even small improvements in decision-making can lead to significant cost savings and revenue increases.
Hotels that view historical data as a strategic tool and integrate it into their sales processes create a benchmarking framework that not only improves performance but also boosts guest satisfaction and financial outcomes. This approach underscores the importance of using historical insights to guide strategic, data-driven decisions in the hotel industry.
FAQs
How can hotels break down data silos to enhance their sales benchmarking?
Hotels can tackle the challenge of data silos by implementing integrated data management systems that bring together information from multiple sources. Solutions like cloud data warehouses or enterprise resource planning (ERP) systems can centralize data, providing a unified, reliable source for analysis and decision-making.
Equally important is promoting collaboration across departments. By encouraging regular data sharing, hosting cross-department meetings, and aligning on shared strategic goals, hotels can ensure that everyone is working in sync. These efforts give hotels a clearer picture of their operations, enabling more precise and impactful sales benchmarking.
How do major disruptions, like the COVID-19 pandemic, affect the use of historical data for hotel sales benchmarking?
Major events like the COVID-19 pandemic can throw a wrench into hotel sales benchmarking by making historical data less dependable. Such disruptions often cause unpredictable changes in demand, leaving past trends less relevant for predicting future outcomes.
To navigate these challenges, hotels must embrace more agile strategies. Using real-time data alongside dynamic forecasting tools allows businesses to better handle fluctuations and maintain accurate benchmarks, even during unpredictable periods. This approach is key to making smarter decisions and staying ahead in an ever-evolving industry.
How can hotels maintain high-quality historical data for accurate sales benchmarking?
To achieve precise sales benchmarking, hotels need to prioritize maintaining reliable historical data through effective data management practices. This means regularly cleaning data to eliminate errors and inconsistencies, as well as conducting periodic checks to ensure everything is accurate and complete.
Bringing together data from multiple trustworthy sources can also help create a more consistent and well-rounded perspective. On top of that, encouraging a data-focused mindset within the team – where accuracy takes precedence over sheer volume – can significantly enhance the dependability of historical data, leading to smarter decision-making.