Breaking down travel analytics: turning data into an advantage

The travel world runs on stories — but today, it runs on numbers too. Travel analytics helps businesses translate these numbers into growth opportunities. Hotels, airlines, and tour operators unlock patterns in booking behaviors, turning everyday data into revenue-boosting insights. Data analytics in travel industry brings certainty in an unpredictable market.

This article explores how analytics transforms marketing approaches, revenue tactics, and guest experiences. We'll dive into data sources, spotlight success stories, and outline practical steps for travel businesses ready to embrace the analytical advantage.

‍

Breaking down big data in travel

Ever wonder how that hotel knew exactly when to offer you a discount? Or why do airlines seem to understand your travel patterns better than your own family? The magic behind these experiences is travel data analytics. 52% of travelers expect AI-driven assistance for travel-related inquiries, and your best way out is to respond with smart data analysis.

Travel companies treasure data. When a traveler browses destinations at midnight, abandons a booking, or raves about a hotel on social media, these digital breadcrumbs tell a story. What exactly falls under the tourism data analytics microscope? The landscape is richer than most realize:

• Customer journeys — from initial inspiration to post-trip feedback.
• Booking behaviors — timing patterns, device preferences, and abandoned carts.
• Operational metrics — flight occupancy, hotel capacity, and staffing efficiency.
• Financial indicators — revenue streams, profit margins, and spending patterns.
• External factors — weather events, competitor pricing, and cultural trends.

We will explore these data sources in more detail later. For business travel specifically, analytics digs deeper — tracking company spending patterns, policy compliance rates, and traveler satisfaction scores. The most forward-thinking travel managers monitor average trip costs alongside feedback, identifying opportunities where cost-saving and comfort can coexist.

The beauty of modern travel analytics isn't just in gathering this information — it's in transforming overwhelming data into clear decision paths. When an airline learns that certain routes perform better with adjusted departure times, these insights translate to revenue growth and customer loyalty. And the advantages are much greater — let’s look at them in detail.

Key benefits of tourism analytics

Research shows that travel companies using predictive analytics see a 30% improvement in customer retention and a 20% increase in profitability. Travel industry analytics and tracking unlock competitive advantages for every player in the ecosystem. With research showing that companies excelling in personalization achieve 40% more revenue than their competitors, the stakes couldn't be higher. Let's explore how various stakeholders benefit from data.

• Travel agencies.

Did you know that 71% of travelers now expect personalized recommendations when booking trips? That's a massive opportunity! By analyzing booking patterns, search histories, and past trips, agencies craft personalized itineraries that feel tailor-made. Agencies that analyze customer preferences optimize commission structures, identify high-value partnerships, and direct marketing dollars toward channels generating the highest conversion rates.

• Airlines.

Flight analytics improves everything from route planning to fuel management. Airlines track passenger booking windows, fare sensitivities, and competitor pricing, and analyze passenger demographics to determine which routes might benefit from premium cabin expansions or additional budget options. Operational analytics minimizes aircraft turnaround times and optimizes crew scheduling. Most fascinating, airlines use predictive analytics to identify potential issues before they cause delays or cancellations. 

• Hotels.

Properties analyze check-in patterns, length-of-stay data, and room preferences to optimize staffing and inventory. Hotel industry data analytics examines guest spending to spot revenue opportunities beyond room rates. Review analysis helps pinpoint rooms that consistently receive complaints or praise. Companies using sentiment analysis to improve customer service and respond quickly see a 12% increase in positive reviews! Many properties now implement dynamic pricing strategies that maximize revenue without alienating loyal customers.

• Tour operators and attractions.

Local experience providers gain remarkable insights through visitor analytics. By tracking foot traffic patterns, attractions can adjust opening hours, staffing levels, and entry procedures to accommodate peak periods. For instance, when 48% of Americans planned to travel during the 2023 holiday season (up from 31% in the previous year), smart attractions used this kind of seasonal data to prepare for visitor surges. Many now use heatmapping technology to identify which exhibits or areas draw the most attention, informing layout decisions and helping determine where to place premium experiences or food outlets. 

• Transportation and mobility providers.

With younger generations 30% more likely to book trips just two weeks in advance, mobility providers must respond fast. Companies now analyze weather forecasts alongside historical demand patterns to predict transportation needs. Car rental companies, ride-sharing services, and public transportation systems use travel and expense analytics, to track popular routes, rental duration patterns, and vehicle preferences, anticipating demand around events to ensure availability. GPS data also sees bottlenecks and optimizes route planning. 

Forward-thinking businesses no longer view analytics as optional — it's the essential foundation upon which all strategic decisions are built. With businesses that deliver personalization seeing a 10-15% revenue uplift, can you afford to ignore the power of data? The question is clear. But to use it wisely, you need to understand its main sources.

‍

Understanding sources of travel data analytics: internal vs external data 

Success in travel analytics depends on having the right data at your fingertips. Today's travel businesses need both the information they generate themselves and insights from the wider market to make truly informed decisions. Travel data management has become essential for companies that want to understand their customers, optimize operations, and stay ahead of competitors. Let's explore where this valuable data comes from and how it drives better business outcomes.

Internal data sources

Everything that happens within your business creates valuable data points that can transform your operations and customer experience. Your most valuable internal sources include:

• Booking platforms and apps show exactly how travelers interact with your offerings.
• Transaction records reveal spending patterns and booking behaviors.
• Customer feedback and survey responses highlight satisfaction levels.
• Website and mobile analytics track how users explore and engage.
• Loyalty program interactions identify your most valuable customers.

These information streams are powerful because they're uniquely yours. Your booking engine captures exactly when customers abandon searches. Your CRM system tracks how quickly guests respond to specific promotions. Your operational systems record check-in times and service requests. This detailed internal data vs external data comparison shows why proprietary information forms the foundation of any strong analytics strategy — it reveals the direct relationship between your business decisions and customer responses.

External data sources

The broader travel landscape provides essential context that explains why certain trends appear in your internal data and helps you anticipate what's coming next. Smart travel companies regularly tap into:

• Social media sentiment and travel review platforms show emerging trends.
• Weather forecasts and seasonal patterns help predict demand shifts.
• Economic indicators and currency fluctuations affect travel decisions.
• Competitive pricing intelligence from distribution channels helps improve your rates.
• Local events calendars and tourism statistics signal potential demand spikes.

The travel data analyst who masters these external sources gains tremendous advantages. When a major conference announces dates in your city, you can adjust pricing strategies months ahead. When competitors introduce new service offerings, you can respond before losing market share. When economic conditions shift in source markets, you can recalibrate marketing efforts toward more promising regions.

This integration of internal vs external secondary data delivers insights neither source could provide alone. Hotels blending their occupancy patterns with local event calendars optimize staffing and amenity offerings. Tour operators analyzing past purchase behavior alongside social media trends develop experiences that perfectly match emerging customer interests.

Breaking down the types of travel data

From the moment a traveler dreams about a destination to their post-trip review, countless data points are created. Understanding these different types of data is crucial for any business wanting to harness the full potential of analytics in travel industry. Let's break down the key data categories that power today's travel intelligence systems.

• Booking data.

Booking data is the backbone of travel analytics. It captures the who, what, when, and where of every reservation. This information reveals booking windows (how far in advance travelers plan), popular travel dates, and destination trends. Analyzing this data helps businesses spot emerging patterns, like an unexpected surge in bookings to a previously overlooked destination or a shift in how far ahead travelers are planning their trips. 

• Customer feedback.

Customer feedback comes through formal surveys, online reviews, social media comments, and direct communications. This perspective reveals what guests truly value and what issues they face. Feedback data helps identify common pain points and unexpected delights. Smart companies analyze feedback at scale, using sentiment analysis to process thousands of reviews and identify patterns that would be impossible to spot manually.

• Transaction history.

Following the money tells a compelling story. Transaction data reveals price sensitivity, popular add-ons, and spending patterns across different traveler segments. Airlines use transaction history to identify which routes command premium pricing and which are more price-sensitive, and hotels analyze which room types deliver the highest profit margins. Transaction data also helps identify fraud — protecting both the business and legitimate customers.

• Travel history.

A traveler's past choices predict their future preferences. This information helps create truly personalized recommendations — if a customer always books beachfront properties, they'll likely appreciate similar options. Travel agencies use it to anticipate when customers might be planning their next trip, reaching out with perfectly timed offers. Also, airlines identify which passengers might be interested in upgrading to premium cabins or adding flexibility options.

• Behavioral data.

Behavioral data encompasses website navigation patterns, search queries, app usage, and responses to marketing campaigns. This information helps identify which features travelers value most and which create friction. How is it applied? OTAs analyze search patterns to understand which filters matter most to travelers, and hotels examine which room features travelers zoom in on in photos to better highlight what matters in marketing materials.

• External trends.

The world beyond your business dramatically impacts travel decisions. External trend data includes weather patterns, economic indicators, cultural events, and health situations. This information provides crucial context for interpreting booking pattern changes. Tour operators correlate weather forecasts with booking patterns to optimize marketing spend, and airlines analyze currency fluctuations to predict shifts in international travel demand — showing that travel and leisure analytics become more powerful when combined with these external factors. 

By connecting booking patterns with customer feedback, transaction history with behavioral data, and internal metrics with external trends, travel businesses gain the 360-degree perspective needed to make truly informed decisions. 

‍

Understanding seasonality and demand forecasting in travel

Let's face it — travel isn't a steady business. It comes in waves throughout the year, creating predictable patterns that savvy businesses can tap into. Just like predictive analytics in retail industry analyzes when the shoppers will need winter boots due to weather patterns and social media frenzy, travel data analytics studies different variables to predict demand. 

Breaking down seasonality: Peak vs off-peak periods compared

Tourism is a game of timing. Tourism analytics reveal striking patterns in travel demand, highlighting the contrasts between bustling peak seasons and quieter off-peak periods. Let’s explore the differences taking the example of the European 2023-2024 tourism seasons. 

• Peak periods.

When peak season hits, destinations become activity hubs. Hotels fill up, prices climb, and tourist spots overflow. High-demand windows line up with great weather, school breaks, and popular events. For instance, one in four European trips happens during July or August alone. Even more illustrative? Europeans spend a third of annual tourism nights during these two summer months. This creates a double-edged sword for travel businesses — amazing revenue opportunities paired with the challenge of handling crowds without compromising service quality.

• Off-peak periods.

Then comes the quiet season — slower months when tourism dips. January typically marks the low point of the year — August tourism volume can be 2.4 times higher than January's, and when measuring nights spent, the difference jumps to 3.9 times! This downturn hits domestic travel harder than international journeys, though business travelers help fill some gaps by traveling most during June and September. Travel providers use these periods to renovate facilities, train staff, and roll out promotions to entice travelers with low prices and seasonal experiences.

Not all travel follows the same seasonal patterns, though. Longer vacations (4+ nights) cluster between June and September, while quick getaways spread more evenly throughout the year. Business travelers march to their drum, actually preferring to travel before and after the main holiday rushes — a valuable insight for hotels and airlines to smooth out their demand curves.

How providers win demand fluctuations with predictive tourism analytics

Big data in travel industry allows travel companies to dig into massive datasets spanning years of occupancy rates, pricing trends, and external factors like weather events and economic indicators. How does it happen in real hotel chains and travel businesses?

Take the Dorchester Collection's approach — their Metis platform monitors social media and online reviews to spot shifting customer preferences. Their experience shows how data can identify problems (like low loyalty among Paris luxury hotels) while human insight develops solutions (like repositioning Plaza Athénée as the fashion-focused "Haute Couture hotel").

Travel and expense data analytics reveal patterns that help fine-tune offerings for different customer segments throughout the year. Luxury brands like Waldorf Astoria and Ritz-Carlton discovered their guests are more likely to be singles than families with children. The Ritz-Carlton caters to smaller households, while Waldorf Astoria sees more families. These insights enable targeted experiences — from restaurant concepts to entertainment options.

What's becoming clear is that navigating seasonality requires both smart data analysis and creative human response. While algorithms excel at spotting patterns across datasets, great strategies come from combining insights with real-world observation and market knowledge.

‍

Travel analytics winning use cases

We started looking at some great examples already. Let's dive deeper into how travel companies are putting analytics to work in their day-to-day operations. Here are four fascinating ways analytics is transforming how travel businesses operate.

• Revenue management.

What’s travel industry predictive analytics in action? Revenue management teams use algorithms to set just the right price at just the right time for each seat, room, or rental car. Machine learning models spot subtle shifts in booking patterns, allowing them to adjust prices automatically. These systems don't just look at historical data either — they factor in everything from upcoming events and holidays to competitor pricing and even weather forecasts. The impact? Companies using advanced revenue management see up to 17% revenue increases.

• Sentiment analysis.

What are travelers saying about their experiences? Analytic tools analyze thousands of online reviews, social media posts, and feedback forms using natural language processing. Is a three-star review quite positive, but mentioning one specific issue? Is a positive comment hiding frustration about a service? Sentiment analysis catches these nuances. How is it used? A hotel chain might discover that guests across multiple properties mention uncomfortable pillows, prompting a company-wide bedding upgrade to improve your services and drive more loyalty. 

• Personalizing customer experience.

By analyzing previous bookings, browsing behavior, and demographics, companies create accurate customer profiles. For instance, business travel analytics is especially sophisticated, with separate models for frequent business travelers. They've learned these groups have different needs — consistency, efficiency, and loyalty rewards. The most advanced companies go beyond simple recommendations to create entirely personalized customer journeys. This level of personalization creates both immediate satisfaction and long-term loyalty. 

• Risk management.

The travel industry faces unique challenges when it comes to risk. Travel operational analytics helps identify, assess, and mitigate these risks before they impact customers. Predictive models flag unusual booking patterns that indicate fraud. Moreover, airlines use these algorithms to predict which flights are most likely to experience delays due to weather, aircraft routing, and crew scheduling constraints. Risk management analytics also helps with more mundane but equally important tasks like maintenance scheduling and inventory management. 

In an industry where a single disruption can affect thousands of travelers, these tools have become essential for maintaining customer satisfaction and controlling costs.

Real-life examples of successful travel analytics implementation

Let's explore some innovative applications of travel data analytics that have helped some globally known travel companies operate better and serve their customers more successfully.

• American Airlines used analytics to predict flight delays hours before they occur. By analyzing weather patterns, aircraft positioning, crew scheduling, and historical delay data, they developed a system that forecasts delays with 80% accuracy. This allows them to adjust schedules, reallocate resources, and help passengers — significantly reducing costs and improving customer satisfaction scores to 77% in just one year.
• JetBlue Airways used analytics to implement a dynamic pricing model that goes beyond simple supply and demand. Their system analyzes competitor pricing, search patterns, booking windows, and social media sentiment to optimize ticket prices. The result? A 17.5% increase in revenue per available seat mile.
• Marriott International deployed sensors and travel data analytics across their properties to optimize energy consumption. The system tracks occupancy, weather conditions, and schedules to adjust heating, cooling, and lighting systems. This initiative reduced carbon intensity by 30% and water intensity by 15%.
• Hertz developed a predictive maintenance system using travel transportation analytics that monitors vehicle performance data across their fleet. By analyzing engine diagnostics, driving behavior, and environmental conditions, they identify potential mechanical issues before they cause breakdowns. This approach reduced roadside assistance calls throughout 10,300 locations worldwide.
• Expedia implemented analytics and travel API integration to create a hyper-personalized recommendation engine. By analyzing search and booking events, user preferences, and behavior patterns, their system delivers tailored travel suggestions that have increased conversion rates to an average of 3.5%. 
• TripActions boosted business travel with corporate travel data analytics with spending insights and a compliance tracking platform. It analyzes booking and spending patterns, negotiated rates, and market pricing to identify savings opportunities and provide personalized booking options that balance employee preferences with company policies. 

From operational efficiencies to personalized customer experiences, companies that effectively harness data are gaining significant competitive advantages. 

‍

Top travel analytics solutions compared

In today's travel industry, having the right analytical capabilities makes the difference between thriving and surviving. Modern travel analytics tools offer great insights into customer behavior, efficiency, and market trends hidden in mountains of unstructured data. Let's explore some of the most powerful solutions available today.

• Teradata is a powerful platform for transportation enterprises. What makes this solution valuable is its deployment options — public cloud, hybrid environments, or on-premises. The platform's capabilities enable you to prepare data using in-database functions of various complexities. Airlines and railway operators also benefit from Teradata's ability to handle massive transactional workloads across departments and regions.

• When it comes to understanding traveler behavior patterns, Amadeus Travel Intelligence delivers value to OTAs. Simply having a beautiful travel website development isn't enough — you need to know how customers interact with your offerings and predict their needs. This platform spots seasonal search patterns, and the generated insights help improve SEO effectiveness, identify potential partners, and see expansion directions.
• TravelPerk distinguishes itself by its flexible dashboard that breaks down data by person, transport type, booking method, and recoverable VAT. Finance teams appreciate cost breakdowns. The platform even includes sustainability features through GreenPerk carbon footprint reporting.

• Travel analytics companies like DataToBiz specialize in managed analytics for hotels, resorts, and vacation property owners. They create centralized data warehouses that integrate with various data sources. What's particularly valuable is their demand forecasting and price optimization capability. As a certified partner for tech giants like Microsoft, Google, and AWS, they ensure robust implementation and ongoing support.
• Managing travel expenses efficiently requires specialized travel expense analytics capabilities, which is precisely what TravelSpends delivers. Their Tripnomics product captures travel patterns and optimizes pricing with real-time validation for price changes. The platform's continuous monitoring capabilities ensure managers maintain complete control over the process while adhering to industry regulations. 

• For companies struggling with data processing and visualization, SolveXia reduces manual work. Their no-code automation platform transforms travel data into actionable insights with robust governance and auditing capabilities. The most compelling aspect of their solution is the efficiency gain — reducing data errors by up to 90%. Their business intelligence services also make it easy to share reports across departments.

With so many powerful travel analytics solutions available, selecting the right one depends on your specific business needs, technical capabilities, and growth strategy. The most successful implementations occur when companies align their analytical capabilities with clear business objectives rather than collecting data for its own sake. 

AI assistants for travel booking and predictive analytics

Data isn’t alone when it comes to giving you an advantage. Wait till you try out AI for tourism analytics! Let's explore some key AI tools available for travel agencies and tour operators, focusing on their capabilities and limitations to give you a balanced perspective.

• Trengo offers a platform that centralizes interactions across multiple channels. The AI chatbots handle routine inquiries and booking processes, freeing up your staff for more complex tasks. Unified inbox and analytics are quite useful, but smaller agencies might find the pricing structure somewhat steep since smart AI features come as add-ons.

• Salesforce Travel CRM with Einstein AI provides robust personalization and travel preference prediction capabilities. It integrates well with Global Distribution Systems, which is particularly valuable for comprehensive travel data analytics. However, be prepared for a large investment — financially and in terms of the learning curve. 
• Global consulting firm Accenture approaches travel analytics from a post-pandemic recovery perspective. They focus on helping travel agencies reinvent their processes. What makes their approach particularly effective is how they combine deep industry expertise with customized AI tools that unlock the full potential of travel data.
• Zendesk AI helps manage customer interactions across channels with AI-powered responses. Its analytics provide good insights into customer service performance. The pricing structure (starting from €149/month plus €50/month per agent for AI features) can be complex, and staff will need training to use the platform effectively.

• Sprinklr AI is designed for agencies needing enterprise-level capabilities for customer service and marketing. While it offers comprehensive functionality, the implementation process is complex, and it's generally not cost-effective for smaller operations.

• Sabre Travel AI combines travel expertise with Google Cloud's infrastructure to enhance various aspects of travel operations. For airlines, it helps optimize retailing strategies through powerful airline data analytics capabilities. Travel agencies benefit from its Lodging AI that suggests properties with higher booking probability. The platform also scales well to support high shopping volumes.
• PredictX focuses on travel analytics across five domains: sourcing, operations, risk and compliance, sustainability, and engagement. With pre-built connectors to over 200 data sources, it can provide a comprehensive view of travel activities. Its machine learning models help validate and cleanse data.

Ready to harness the power of analytics for your travel business? Let's explore how to integrate these travel analytics solutions into your day-to-day operations to maximize your revenue, eliminate error, and get into the essence of what your customers want — not today, not tomorrow, but perhaps, months and even years in advance.

‍

A guide to implementing travel data analytics

The journey toward data-driven decision-making doesn't happen overnight. It requires thoughtful planning, right expertise, and a strategic approach to extracting insights from your travel data. Here are the main steps to approaching travel analytics.

• Start by building a team that combines data science expertise with travel industry knowledge. Look for professionals who understand the technical aspects of data analysis and the nuances of travel operations. Don't underestimate the importance of finding team members who can effectively communicate insights to non-technical stakeholders and translate travel-specific challenges into analytical frameworks.
• Establish the challenges you're trying to solve. Are you looking to optimize pricing, improve customer satisfaction, reduce operational costs, or identify new market opportunities? Work with department heads to establish measurable KPIs that align with broader business goals. This clarity ensures your tourism analytics efforts deliver tangible value rather than just interesting but ultimately unused insights.
• Identify data sources. Travel businesses have data scattered across multiple systems. Creating a unified data environment is essential for comprehensive analysis. Map your data ecosystem, identify gaps, and develop a plan for regular data collection that maintains quality and consistency while respecting privacy regulations.
• Select analytical tools to handle a great data variety. Consider platforms that offer machine learning capabilities for predictive analytics, natural language processing for sentiment analysis, and good visualization features. Work with the data science team to evaluate options based on your needs rather than choosing the most popular tools.
• Develop clear processes for turning analytical insights into operational changes. This might involve creating automated alerts for pricing opportunities, dashboards for operational teams, or regular insight briefings for executive decision-makers. Focus on making data insights accessible and actionable for everyone in your organization.
• Establish clear data governance policies — data quality standards, access controls, retention policies, and compliance procedures. Strong governance builds trust with customers increasingly concerned about how their data is used.

Implementing analytics can be challenging. Consider a scenario: A regional tour operator wants to predict demand patterns to optimize pricing. They have years of booking data but it's stored across different systems, and customer feedback is in email threads. After months of struggling to integrate data sources, they've invested significant resources with little to show for it. 

This is where specialized travel technology consulting can be useful. Travel analytics companies help navigate complex data integration issues, recommend appropriate technology solutions, and provide implementation support tailored to travel business needs. 

How COAX bridges your data and improvement

Following the discussion on the challenges you might face, it's worth exploring how COAX helps address these complexities. Our teams specialize in custom software development for travel businesses of all sizes. We work across the entire spectrum of travel technology needs — from targeted analytics features to building platforms from scratch. Our flexible approach ensures we deliver what your business needs. We excel at integrating travel APIs and connecting your systems with GDSs and OTAs while setting architectures that power your analytics capabilities. 

Whether you need to integrate travel analytics tools, leverage big data in travel for better decision-making, or modernize legacy systems, we approach each project with careful attention. In our transport booking platform for Driven Connect, we implemented comprehensive analytics and reporting features that provided data visualization within the dashboard, enabling operators and organizations to track performance metrics and make informed decisions.

Also, for businesses exploring AI development and integration, we recommend starting with focused pilot projects that demonstrate value quickly before expanding to address additional business areas. 

We believe technology should be accessible and valuable for your daily operations, which is why we create intuitive interfaces and provide comprehensive training for your team. From predictive booking analytics for tour operators to data-driven pricing models for hotels and real-time customer insight dashboards for agencies, our goal is to deliver solutions that drive meaningful business results while respecting your unique constraints and opportunities. 

‍

Tourism analytics: recapping your opportunities

Travel analytics isn't just about numbers — it's about understanding the causes and correlations behind every booking, click, and review. As we've seen, travel companies are using data to predict customer desires, optimize pricing, and create truly personalized experiences. Whether you're a boutique hotel or a major airline, embracing analytics can transform your business from reactive to proactive. 

The future of travel belongs to those who can blend powerful data insights with genuine human understanding, creating experiences that delight travelers while boosting the bottom line.

‍

FAQ

What are the challenges of implementing travel and leisure analytics?

Some of the main implementation challenges include:

• High technology infrastructure costs.
• A lack of qualified analytics staff.
• Data fragmentation across booking platforms and legacy systems.
• Poor data quality that impairs decision-making.  
• Resistance to change.
• The need for real-time processing.
• Seasonal data volatility.
• Cross-platform integration complexity.
• Challenges with ROI measurement.

How do privacy regulations affect travel data management and analysis?

Travel businesses must adhere to GDPR, CCPA, and other similar laws, which require consent, data minimization, and secure storage. As O'Connor explains, travel businesses gather large amounts of customer data related to bookings and loyalty programs. The regulations require transparency of data use, purpose limitation, strong security practices, and an ethical approach to handling booking information, IDs, and payment information.

Which tourism segments benefit most from analytics in the travel industry?

Hotels and airlines can get the best ROI when they optimize their dynamic pricing and manage fixed costs. Dynamic pricing is particularly important for tour operators because they can benefit from forecasting seasonal demand. As Dolnicar's research confirms, market segmentation allows tourism destinations to gain a competitive advantage through developing product offerings for select groups, targeted marketing tailored for the selected segment, and greater customer satisfaction.

Do I need data scientists on staff to implement travel analytics?

Not necessarily. Many businesses thrive by developing partnerships with specialized providers or working off industry-specific platforms with built-in capabilities. For instance, COAX has a vast expertise to realize a full array of travel analytics solutions, and is certified with ISO/IEC 27001:2022 for risk management and security, and ISO 9001 for process management. This means we provide quality products and secure sensitive information and documents regarding booking, payment, and customer IDs.

How can small businesses compete with larger companies on travel analytics?

Small businesses take advantage of industry-specific platforms made for smaller operations, analysis of niche customer bases, and quick implementation free from bureaucratic restrictions.  By enabling advanced travel demand modeling, social media geoanalytics, and user-generated content insights, Xiang and Fesenmaier show how analytics in smart tourism enable smaller operators to compete through specialized market understanding and flexible decision-making.