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.​