Breaking into the Hospitality Industry as a Data Analyst

The hospitality industry has not historically been a tech-first segment. Going as far as to be one of the slowest adopters of new tech, years behind the aviation sector, which has lead many breakthroughs used in hospitality today. But this is rapidly changing. Today, data and analytics are becoming essential in hospitality, opening up exciting opportunities for analysts to make a tangible impact. With 77% of hoteliers now viewing data analytics as a priority.

Their mains goals being to boost guest loyalty and enhance the guest experience. Similarly, nearly 90% of hospitality businesses now believe analytics will improve their growth prospects. For a data analyst, this means you can help an industry modernise and be part of its digital transformation; an incredible opportunity. Through data-driven insights, hotels can learn more about guest behaviour and preferences, allowing them to personalise offerings, improve service quality, streamline operations, and ultimately optimise both top and bottom line.

Opportunities for Data Analysts in Hospitality

The push toward analytics in hospitality is creating numerous roles for data professionals. As teams struggle to parse the vast amounts of data collected. Providing opportunities even for those coming from other sectors. Hotels and travel companies are investing in data capabilities to stay competitive amid industry upheavals and evolving customer expectations. Over the last couple of years job postings for data analysts, engineers and scientists in hospitality have surged, and organisations are eager for talent who can turn their troves of data into actionable strategies. It is a sector where your work can still directly influence business decisions; from reducing costs to delighting guests.

Guest information flows in from many sources (booking systems, guest surveys, online reviews, social media, mobile apps, etc.), and a skilled analyst can “crunch the numbers” to uncover trends that drive better decisions. For example, analysts can help prevent guest cancellations, optimise pricing and inventory, forecast demand, and increase revenue per booking. They analyse metrics that compare a hotel’s performance against its market and suggest adjustments to improve the hotel’s standing. Data analysts also dive into customer feedback and marketing data to identify what drives guest satisfaction and which campaigns yield ROI, thereby guiding managers on where to invest effort and budget. In an industry where small improvements in occupancy or rate can translate to big revenue gains, these analytical contributions are highly valued.

As a data analyst, you wouldn’t just contribute to revenue, although that is a key aspect of the job. Hotels are also using analytics to elevate service. Data insights can help personalise guest experiences, streamline operations, and improve efficiency. For instance, by studying booking patterns and guest demographics, a hotel might optimise staffing levels or tailor its amenities to match guest preferences. You could be key to making a guests stay an experience they will remember for a lifetime. Which is especially true in the higher segments. You will also enable restaurants to analyse point-of-sale and customer feedback data to refine menus or reduce waste. All these improvements translate to competitive advantage both in revenue and guest satisfaction. 

Master the Key Hospitality KPIs

Breaking into hospitality analytics requires a deep understanding of the industry’s key performance indicators (KPIs). I have personally taken over from an analyst who was clearly very competent in the tech space, but who lacked the hospitality back ground. Which lead to very complicated and roundabout solutions for simple KPI calculations. The metrics below are the language of success in hotels and restaurants, and it is key to be fluent in them to provide meaningful insights. Some of the most important hospitality KPIs include:

• Occupancy Rate: The percentage of available rooms (or seats, in restaurants) that are filled over a given period. High occupancy is good, but if it’s too high it could signal underpricing (selling out too easily) or strain on operations. Successful revenue management seeks the optimal occupancy that maximises revenue while maintaining service quality.

• ADR (Average Daily Rate): The average rate paid per room (or per occupied unit) in a given time frame. If you sold 100 room-nights for a total of £10,000, your ADR is £100. A higher ADR means guests are paying more on average, but it must be balanced with occupancy. Raising rates too high can lower occupancy, which has a larger effect on the top line revenue. ADR is often looked at alongside occupancy to assess if pricing strategy is effective.

• RevPAR (Revenue Per Available Room): A critical metric in hotels, RevPAR blends occupancy and ADR to measure revenue generated per available room. It’s calculated as total room revenue divided by total rooms available (or as a short cut ADR × Occupancy). Hotel experts often say that if you only track one metric, track RevPAR, because it encapsulates the impact of both rate and volume on revenue. A rising RevPAR generally indicates improving performance. For example: if a hotel has 100 rooms, an 80% occupancy at $125 ADR gives RevPAR $100; boosting RevPAR could involve higher occupancy, higher rates, or both.

• RGI (Revenue Generation Index): Also known as RevPAR Index (RPI), this KPI gauges your hotel’s revenue performance relative to the competition. RGI is calculated by dividing your hotel’s RevPAR by the average RevPAR of your competitive set. An RGI of 1.0 means you’re getting your fair share of the market revenue; above 1.0 means you’re outperforming competitors, while below 1.0 indicates underperformance. RGI is the key benchmark metric for hotels in competitive markets, and mastering it is crucial for analysts in revenue management.

• Other Financial KPIs: Hospitality businesses also monitor metrics like TRevPAR (Total RevPAR), which includes all revenue (not just rooms), and GOPPAR (Gross Operating Profit Per Available Room), which factors in operational costs to measure profitability per room. These give a fuller picture of performance beyond just top-line revenue and can help you stand out as an analyst. We have linked a relevant post below. 

  
  

Understanding these KPIs and how they interact is non-negotiable for a hospitality analyst. Studies show RevPAR is the most widely used metric for hotel revenue performance (preferred by 77% of revenue managers), followed by RGI (used by ~48%). My best tip to break in and be credible? You should be comfortable explaining and analysing what drives RevPAR and RGI in various scenarios. Practice calculating them, learn what influences them (seasonality, events, pricing, etc.), and be ready to discuss strategies to improve them. 

Build Your Hospitality Domain Knowledge

While knowing the KPIs is important, understanding how the business works will set you apart. Hospitality has its own rhythms and challenges that may differ from other industries you’ve previously worked in. Take time to educate yourself on the fundamentals of hotel and restaurant operations, revenue management practices, and industry trends. This context will enable you to turn data into insights that make sense on the ground.

How I would start? Recognise that hospitality is a data-rich environment, but data comes from many disparate sources. This will save you a world of pain down the line. A hotel produces data from reservations, property management systems (PMS), point-of-sale transactions, guest loyalty programs, online travel agencies (OTAs), social media, guest reviews, and more. Part of your role may involve wrangling these into a usable form. Many hotels struggle with siloed legacy systems. With 45% of hotels say fragmented tech and data prevent a unified view of their business. So an analyst who can integrate and analyse data across systems is extremely valuable. Be prepared to encounter messy data and to spend time on data cleaning and combining datasets (e.g. linking reservations with customer feedback). And do not take anything at face value. Spend your first weeks, confirming the basic KPI’s across platforms. Most RMS’s will deviate from the PMS, from the Channel Manager, and so on. One rule of thumb that I have always found helpful is that the PMS it usually right. Before you start you can familiarise yourself with common hospitality software (for example, Opera or other PMS, restaurant POS systems, etc.) and how data is exported from them. Understanding where the data originates will help you judge its quality and limitations (e.g. manual entry errors, timing of updates, etc.).

I would also study hospitality business drivers. Seasonality, weather, local events, and holidays can dramatically affect demand for hotels and restaurants. You can usually predict a coastal hotel’s performance in line with the weather forecast. An interesting trend here is that a warm winter week will equal an increase in summer bookings. Revenue management is a core discipline in hotels that revolves around forecasting demand and dynamically adjusting prices and inventory. As an analyst, you should grasp the basics of revenue management strategy. Think of things like segmentation (business vs leisure travellers, weekday vs weekend patterns), channel mix (direct bookings vs OTAs), and the concept of the competitive set. Revenue managers are some of the heaviest users of data in the industry. They rely on both internal data (e.g. historical occupancy, advance bookings on the books) and external market data (e.g. competitor rates and local demand trends) to make decisions. By understanding their mindset and metrics, you can provide analyses that support pricing decisions, demand forecasts, distribution strategy, and most importantly top line revenue. For instance, you might analyse booking pace (pickup) to alert if a particular date is filling slower than last year, prompting a marketing push or a price adjustment. Or you might examine how an event (like a big concert or conference) lifts demand and suggest optimised pricing for that period.

Also, get familiar with how success is measured beyond the numbers. Guest satisfaction and service quality are paramount in hospitality and they now generate data too (think review scores, Net Promoter Score, social media sentiment). A good analyst doesn’t focus solely on revenue (although you will probably be situate in the commercial team); they also monitor customer experience metrics and find insights there, which is usually delegated to the operations teams. For example, analysing guest reviews might reveal pain points that, if solved, could boost ratings and thus attract more business (raising RevPAR). If you want to succeed in this role, you will likely collaborate with operations managers to interpret feedback data and recommend improvements. By connecting the dots between guest sentiment and financial outcomes (e.g. how a drop in ratings can lead to lower bookings), you position yourself as a well-rounded analyst who understands that happy guests equals better business.

Having said all this, please don’t be intimidated if you lack direct hotel experience. No one starts in a position being able to cover all these aspects. Start with the basics; what drives the most revenue and expand from there. You can also build domain expertise through self-learning and curiosity. Read hospitality industry blogs and reports, follow news on hotel performance trends, and consider joining professional associations (like HSMAI – Hospitality Sales & Marketing Association International). There are online courses and certificate programs (for example, in Hotel Revenue Management or Analytics) that can accelerate your learning, though I don’t believe that this is necessary per se. Even spending a weekend reading hotel case studies or the annual reports of big hotel chains can teach you common terms and concerns. The goal is to be able to think like a hotelier when looking at the data. When you can combine your analytical mindset with an insider’s understanding of why certain numbers matter, you’ll stand out as someone who can translate data into actionable hospitality strategy.

Hone the Right Skills and Tools

I am not going to sugar coat it; many hotel companies still heavily rely on Excel. Whilst it is good to have solid technical skills, be prepared to dive back into the basics. I even know of a respectable hotel chain that has a data warehouse, but that doesn’t use it. The good news is that the core data skills you’ve developed in other industries are highly transferable, especially in a growing company that may be looking to make the next step inn their tech journey. To succeed as a hospitality data analyst (and to get hired in the first place), make sure you can demonstrate the following key skills:

• Analytics & Visualization Tools: Stakeholders, like GMs, revenue directors, and marketing managers often prefer insights delivered in a clear, visual format. Experience creating interactive dashboards and reports using BI tools like Power BI and Tableau will serve you well. You might build a dashboard tracking daily occupancy, ADR, RevPAR against targets, or a dashboard showing guest satisfaction trends alongside revenue. A clean visualisation helps non-technical people grasp the story the data is telling. Additionally, knowledge of Excel for quick analysis or reporting is still very much valued in hospitality; many hotel managers live and breathe Excel reports. Being the analyst who can automate an onerous spreadsheet or add forecasting formulas can immediately add value. If you have skills in more advanced analytics (like predictive modelling or machine learning), that’s a bonus. Some larger hotel companies or hospitality tech firms do seek these for forecasting demand or optimising pricing. But even then, those models need to be explained in simple terms. 

• Communication & Data Storytelling: It will not be enough to just find insights. You must communicate them effectively to decision-makers. Many hospitality professionals do not have a data background, so one of your superpowers should be translating complex analysis into clear, actionable recommendations. Focus on developing your data storytelling: use narratives and visuals to explain why a metric is up or down and what action should be taken. Framing insights in a solution-oriented way helps demonstrate your business acumen and allows you to share in the overall business success. This will also make you an invaluable member to the team and give you greater visibility to those higher up. Strong written and verbal communication skills will also help you collaborate with cross-functional teams and ensure your analytical findings actually drive decision-making.

• Business Acumen & Critical Thinking: Beyond tech, successful data analysts have a nose for business implications. This means always asking: How does this analysis connect to revenue, cost, or guest satisfaction? Something me CEO drilled into me; always ask what the business question is and evaluate yourself if you believe it is the most valuable thing you can do with your time. If you’re analysing a new dataset, consider what the most relevant questions are. For instance, given a spreadsheet of monthly booking counts, an analyst with acumen might derive seasonality patterns or identify that a drop in bookings coincided with a competitor’s new promotion. Practice breaking down problems and exploring the “why” behind the numbers (e.g. why did occupancy dip in Q3? Was there a renovation, new competitor, economic factor?). This critical mindset will help you pinpoint insights that matter and avoid getting lost in analysis for analysis’ sake. Many companies will value your ability to prioritise impactful analysis and to understand the operational context of data.

• Adaptability & Learning Mindset: The tools and data landscape in hospitality are continually evolving (read: catching up). From new property management systems to AI-driven guest personalisation platforms. To break in and keep growing, you should show that you’re an avid learner. Perhaps you’ve taken an online course on hotel analytics or taught yourself a visualisation tool to map out tourism data. Highlighting these in your portfolio or interviews can demonstrate your initiative. Once on the job, be ready to learn the specific systems your company uses (e.g. a specific revenue management system or CRM). The faster you can ramp up on new tools, the more you’ll stand out. Keep an eye on industry trends too. Many hotels are currently exploring real-time analytics and predictive models for forecasting; being conversant in these emerging trends (even at a high level) can be impressive.

• Data Manipulation & SQL: It would be remiss not to add these in the conversation, though I would argue that this is going to be the least of your worries. Hotel and travel companies do often deal with large, relational datasets (think millions of booking records or transaction logs). Being proficient in SQL for querying databases is often a baseline requirement. You should be comfortable extracting and joining data from multiple tables (e.g. reservations, customer profiles, financial data). Familiarity with data processing in Excel or using Python/R for data cleaning can be very useful, especially given that some hospitality data might arrive in CSVs or Excel exports that need additional wrangling. The ability to handle “messy” real-world data (with missing values, outliers, etc.) is crucial.

  
  

A Forbes analysis of data analyst roles found that beyond crunching numbers, top analysts excel in business understanding, communication, and stakeholder management. This is especially true in hospitality, where the ultimate goal is not just to analyse data, but to drive smarter decisions and communicate effectively with people whose hearts are not in data, but in guest experience. If you can query a database, build a dashboard, and then clearly tell the hotel’s leadership what the data means and what they should do, you’ll be invaluable.

Breaking In: Tips to Launch Your Hospitality Data Career

Targeting the hospitality sector as a data analyst might feel like a leap if you come from a different background, but there are concrete steps to make the transition smoother:

• Leverage Your Transferable Skills: This is a given. Emphasise the data skills you already have that apply to hospitality. A database is a database, regardless of where you work. But work these skills into a narrative. Maybe you analysed sales trends in retail or optimised operations in manufacturing. Those analytical techniques (trend analysis, forecasting, optimization, etc.) are very relevant to hotels and restaurants. Domain knowledge can be learned; demonstrating proven analytical impact gives you a strong foundation.

• Get Familiar with Hospitality Data Sets: If you have no direct hospitality experience, consider doing a small project on your own to build familiarity. For example, there are public datasets available on Kaggle (like hotel booking demand datasets, online review data, or tourism statistics) that you can analyse and showcase. You might create a case study where you analyse what factors drive hotel booking cancellations, or build a sample RevPAR dashboard for a hypothetical hotel using dummy data. This not only teaches you industry nuances (seasonality, lead times, etc.) but also gives you something to talk about with potential employers to prove your interest in the field.

• Network and Learn from Insiders: Networking can greatly ease your entry. Hospitality is a people’s business. Having someone you know in the industry will go a long way. Join hospitality analytics communities or forums online. LinkedIn is a good place to follow hospitality analytics professionals, hotel revenue managers, or groups focused on hotel technology. Engage by asking thoughtful questions or commenting on industry discussions. People often appreciate curiosity and might offer advice or even job leads. Attending industry conferences or webinars (many are virtual these days) can also expose you to the latest challenges and solutions in hospitality tech. Organisations like HSMAI and Lighthouse host events and webinars on revenue optimisation and analytics. Golden opportunities to learn and make connections.

• Show Passion for Hospitality, Not Just Data: Finally, when approaching the hospitality industry, let your passion for the domain shine through. Hospitality is fundamentally about people and experiences. If you can convey that you’re excited about helping create great guest experiences or driving growth for hotels through data, you’ll connect more with hospitality hiring managers. They’ll see you as someone who wants to be part of their world, not just a generic analyst. In interviews, you might mention a particular hotel stay or travel experience that inspired you, and how you realised data could make it even better. Combine that genuine interest with examples of your analytical skills, and you’ll leave a memorable impression.

  
  

Hotels are looking to analytics to decide everything from pricing rooms on New Year’s Eve to personalising a guest’s welcome amenities. Restaurants are mining data to refine menus and reduce wait times. And travel companies are leveraging AI to recommend the perfect holiday package. The hospitality sector is at a point where data analytics can be truly transformative, and fresh perspectives are welcome. Which makes it such an exciting time to be in hospitality data because your contributions can shape memorable customer experiences and healthier profits. By mastering hospitality KPIs, building domain know-how, sharpening your technical and storytelling skills, and actively engaging with the industry, you position yourself to break in and thrive. Remember that as an analyst, your ultimate value in hospitality comes from using data to tell the story of the business. Highlighting what’s going well, diagnosing issues, and illuminating opportunities for improvement.