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Data Analytics in the Hospitality Industry
Features, Development Steps, and Costs
In data analytics since 1989, ScienceSoft builds custom analytics solutions to help hospitality businesses personalize customer experience, optimize operations, and manage competition.
Contributors
Head of Data Analytics Department, ScienceSoft
Senior Business Analyst and BI Consultant, ScienceSoft
Hospitality analytics is commonly used by hospitality businesses to monitor operational KPIs, foresee customer demand, enable dynamic pricing adjustment and real-time inventory replenishment, and more.
- Custom hospitality data analytics solutions are a popular choice in the industry as they let businesses benefit from tailored features, integration flexibility, and role-specific workflows that are not offered by OOTB software.
- Implementation time: 2–6 months for an MVP.
- Costs: $30,000–$500,000, depending on the solution's complexity. Answer a few questions about your business needs to get a tailored ballpark estimate from our data analytics team.
- ROI: up to 290% with a payback period of seven months.
- Key integrations: a property management system (PMS), a central reservation system (CRS), financial management software, channel distribution software, a CRM, supply chain management software, external sources (a global distribution system (GDS), social media platforms, online review and rating platforms).
Hoteliers Bet on Data and AI to Achieve Their Business Goals
According to a survey by Amadeus Insights, 37% of hospitality businesses think that data-driven techs like artificial intelligence and machine learning will help them personalize customer experience and improve the performance of marketing campaigns.
High-Demand Features for Hospitality Analytics
Facility analytics
Using data from IoT sensors, analytics solutions can monitor the status and utilization of facility assets in real time (e.g., HVAC systems, guest room amenities, kitchen equipment, fire safety and lightning systems) and track the consumption of energy and water resources. The software can trigger alerts on equipment malfunctions and provide insights for optimizing resource usage. Combining sensor data with equipment maintenance logs allows companies to conduct predictive asset maintenance.
By combining data on customer demographics and preferences (e.g., amenities used, length of stay, food preferences), hospitality providers can get detailed customer profiles. Advanced analytics solutions can enable NLP-powered customer sentiment analysis based on customer reviews and provide ML/AI-driven personalization recommendations (e.g., on activity, dining, and loyalty program options).
Supply chain analytics
Tracking SCM KPIs (e.g., on-time delivery rates, supplier lead time, return rate, food cost percentage, stockout rate) lets hospitality businesses assess supplier performance, optimize inventory management, reduce food and beverage waste, and more. With real-time inventory monitoring, you can get alerts on low stock levels or benefit from automated inventory reordering. Companies that manage inventory delivery in house can implement transportation and logistics analytics, including features for optimizing routes and delivery schedules.
With core financial analytics capabilities, companies can track financial KPIs such as daily rate (ADR), revenue per available room (RevPAR), cost per occupied room (CPOR), market penetration index (MPI), flow-through rate. More advanced features include financial modeling and forecasting which let companies test their strategies under various market conditions.
Marketing and sales analytics
With insights into prospects’ preferences and online behavior (e.g., opened emails, clicked offers), companies can create targeted marketing content. Hospitality businesses can track the performance of distribution channels (e.g., booking volume and revenue per channel) and get ML/AI-driven recommendations on optimal distribution channel strategy. Analytics solutions can also enable lead scoring and sales funnel analysis.
Demand forecasting
Analytics solutions can forecast customer demand for rooms and on-site ancillary services by using historical data (e.g., on past occupancy rates, and average length of stay) and real-time feeds, including local events, market trends, and competitor activity. ML/AI-powered analytics can provide recommendations on optimal capacity, inventory level, and staffing based on demand forecasts.
Dynamic pricing optimization
With continuous monitoring of multi-source data (e.g., booking trends, competitor pricing, inventory levels, currency rate fluctuations), ML/AI-powered engines can automatically adjust prices in real time while balancing competitiveness and profitability.
Workforce analytics
Workforce analytics helps to monitoring role-specific KPIs (e.g., average resolution time for customer service agents, service recovery rate for hotel personnel, sales volume for sales agents) and common employee performance metrics such as training completion rates and attendance. You can also get insights into employee satisfaction and engagement to minimize turnover and use forecasting and what-if modeling for optimal workforce allocation.
Fraud detection
ML/AI-powered algorithms continuously monitor customer and employee-related data (e.g., data on bookings, financial transactions, discounts management) and identify patterns that can be indicative of fraud. For example, multiple bookings from the same credit card and repeated last-minute cancellations may be a sign of ghost booking; frequent discounts and other bonuses to the same customers can help detect cases of customer collusion with staff.
Key Integrations for Data Analytics in Hospitality
Since most hospitality businesses rely on PMS as the main operational software and data consolidation hub, it makes sense to connect the analytics solution to it for direct data intake. Other operational and back-office software (a CRM, SCM software, channel management software) can be part of the PMS or be connected to it, depending on each business' IT environment.
A property management system (PMS) + a central reservation system
- To forecast customer demand and plan resources accordingly.
- To create detailed customer profiles and get insights for service personalization.
- To optimize the usage of assets and staff allocation.
- To conduct predictive asset maintenance.
- To avoid over- and understocking and minimize supply waste.
- To detect financial fraud.
Financial management software
E.g., revenue management software, accounting software.
- To enable cost and revenue attribution.
- To find cost optimization opportunities.
- To enable financial forecasting and what-if modeling.
External data sources
E.g., social media and review platforms, GDS.
- To automatically adjust pricing and offering in response to competitor and customer activity.
- To understand customer sentiment towards the provided services.
Hospitality Analytics Development: Key Steps and Best Practices
Hospitality analytics development is needed when a business requires tailored analytics capabilities, integration with custom-built or legacy software, and role-specific dashboards that are not offered by ready-made solutions. With 35 years of experience in data analytics and an extensive portfolio of projects for hospitality, ScienceSoft describes key steps for implementing hotel industry data analytics.
1.
Business analysis and requirement engineering
At this stage, business analysts conduct Q&A sessions and workshops with the company’s stakeholders to understand the company’s operational specifics (e.g., software used, booking processes) and the goals to achieve with analytics. The gathered details are then analyzed and documented as software requirements. During this stage business analysts also determine software compliance regulations for the future solution such as GDPR, CCPA, PCI DSS.
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2.
Technical design
The solution architect determines the major architectural components and integrations. The expert compares the available techs and advises those that satisfy performance, scalability, latency, and other requirements at the best cost-to-benefit ratio. For example, to implement real-time big data analytics for hospitality (e.g., for real-time analysis of customer preferences and offering personalization) the architect may consider AWS Lambda or Google Cloud Dataflow. AWS Lambda may be a more cost-efficient choice as it incurs charges only when an event occurs (e.g., a guest provides information that triggers a personalization action). This helps minimize costs during off-peak times when few personalization actions are required. In contrast, Google Cloud Dataflow continuously processes data streams and charges per used storage and processing capacity, which means that the company will pay the same amount for high- and low-traffic periods.
To power historical analytics, data engineers develop a data model. To cut development costs, using an existing model as a basis may be a good option, for example, from a property management system. The engineer defines the key entities (e.g., a hotel reservation) along with their attributes (e.g., check-in and check-out times, room type, customer ID) and the relationships between them (e.g., a hotel reservation can be related to payment transactions and certain hotel amenities).
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3.
UX/UI design
UX/UI designers create dashboard UIs and user app workflows tailored to the needs of different user roles. For example, when maintenance managers receive a real-time alert on asset malfunction, it’s convenient to have an interactive pop-up with the issue details (e.g., equipment ID and location, estimated resolution time, responsible employee). Unlike operational specialists, upper executives are more likely to require mostly static interfaces that provide a 360-degree view of company performance.
The experts also focus on achieving smooth user adoption. For example, they can create hover-over tooltips with brief feature explanations on each dashboard. One of the best practices ScienceSoft’s UI designers recommend is auditing the software that’s already in use in the company and incorporating the familiar colors, widgets, and elements into the new solution.
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4.
Development, testing, and QA
In many cases, it is best when development and testing are conducted in parallel. This approach improves cross-team collaboration, helps detect issues before they become major problems, and minimizes defects in production. To speed up development, experts may use cloud services such as Microsoft Azure Synapse Analytics or Microsoft Fabric, Amazon Redshift, Google BigQuery. They offer convenient managed services and ready-made components, which results in 2-20x faster development time.
ScienceSoft also recommends implementing DevOps practices and going for feasible QA automation. According to our experience, such an approach helps reduce project costs by up to 78%. After the solution is deployed, the team performs post-launch activities such as solution performance monitoring and bug fixing (in case some are detected after deployment).
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What makes ScienceSoft different
We achieve project success no matter what
ScienceSoft does not pass mere project administration off as project management, which, unfortunately, often happens on the market. We practice real project management, achieving project success for our clients no matter what.
How Much Does It Cost to Develop a Hospitality Analytics Solution?
The cost of hospitality analytics development may range from $30,000 to $500,000, depending on software complexity. The major cost factors include the scope of business operations to be covered by the solution, the geographic reach of the services, and the complexity of analytics features.
According to our research, analytics in the hospitality industry can drive up to 290% three-year ROI with a payback period of seven months. High return is associated with benefits from dynamic price optimization, demand forecasting, and timely updates on operational KPIs.
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|
Basic solution |
Medium-complexity solution |
Advanced solution |
|---|---|---|---|
|
The number of business areas to be analyzed
?
E.g., customer management, revenue management, operations management. |
1 |
2–3 |
3+ |
|
The complexity of the hospitality business
?
The broad spectrum of offering leads to increased data volume and diversity. |
Accommodation with no or limited amenities, e.g., bed and breakfast hotels. |
Mid-scale hotels with a broad range of amenities such as restaurants and fitness centers. |
Comprehensive service offering focused on luxury experience, e.g., all-inclusive resorts. |
|
Geographic reach
?
Possible location-specific cost factors include additional data standardization efforts (e.g., for different currencies) and the need to enable compliance with different regulations. |
Hotels at a single location or a small localized area, e.g., single-property hotels. |
Hotels with multiple locations in a certain country or region, e.g., regional hotel chains, resorts with multiple properties. |
Companies operating across several countries or continents, often representing a well-established brand, e.g., global hotel chains, international resorts. |
|
Integration complexity
?
Different data sources may introduce varying data formats and require custom APIs, especially for legacy systems. |
Integrates with 1–2 systems (e.g., a booking system, an ERP). |
Integrates with up to 5 data sources, including external sources like social media platforms and GDS. |
Integrates with a large number of systems, including multiple booking apps and same-type systems (e.g., PMSs used by different divisions of a company). |
|
Data complexity
|
Structured data (e.g., relational database). |
Structured and semi-structured data (e.g., JSON files). |
Structured, semi-structured, and unstructured data (e.g., DOCX, PDF, JPG, JPEG, MP3, MP4, HTML files). |
|
Data processing frequency
|
Batch (e.g., every 24 hours). |
Batch and real-time. |
Batch and real-time. |
|
Complexity of analytics features
|
KPI calculation, data segmentation, and rule-based forecasting (e.g., customer demand forecasting based on historical data for the same period). |
ML/AI-powered forecasting based on historical and real-time data (e.g., predicting customer demand by combining real-time booking data and info on economic indicators). |
|
|
Reporting and visualization
|
Scheduled and ad hoc report creation via market-available tools like Power BI, Tableau, Looker. |
Scheduled and ad hoc report creation via market-available tools like Power BI, Tableau, Looker. |
|
|
Costs
|
$30,000–$50,000 |
$60,000–$150,000 |
$150,000–$500,000 |
See How ScienceSoft’s Clients Benefit From Hospitality Analytics
Data Analytics Services
Building data analytics and AI solutions since 1989, ScienceSoft has all the necessary expertise to provide full-cycle analytics consulting and software development services to hospitality businesses. Holding ISO 9001 and ISO 27001 certifications, we can guarantee high software quality and complete security of your data.
