Today’s hospitality revenue managers are navigating an increasingly complex environment, one shaped by shifting consumer preferences, regional nuances, and mounting competition from short-term rentals. With these factors at play, the need for transformation in revenue management practices has never been more pressing, particularly in key markets like the U.S., where revenue per available room (RevPAR) growth in 2024 is projected to be just 1.2 percent.
To adapt and thrive in this challenging landscape, the travel sector is showing a robust appetite for technology investment. According to recent research, planned technology investments are set to rise by 14 percent in 2024, with 91 percent of travel companies anticipating “moderate to aggressive” growth in their tech spending. These investments underscore a widespread recognition of the need to embrace innovative technologies to ensure revenue management success.
“The rapid pace of technological change, including the adoption of AI and machine learning, requires significant investment in new systems and training for staff,” said Ryan Mummert, senior principal, insights and data portfolio leader, at Capgemini. “Hospitality revenue managers must stay updated with the latest innovations to remain competitive, which can be resource intensive.”
Here are three insights to help hospitality revenue managers better understand how to leverage AI to optimize pricing strategies, enhance demand forecasting, and deliver personalization at scale through customer segmentation.
Insight #1: Take a Cue from Airlines to Refine Dynamic Pricing Strategies
“Airlines have long been pioneers in dynamic pricing, adjusting fares based on demand, booking patterns, and other factors,” said Lee Taylor, head of hospitality sales, at Capgemini. “Hotels and other travel companies can adopt similar strategies to adjust room rates and package prices in real time, allowing them to better match market conditions and optimize revenue.”
Airlines have indeed led the way in dynamic pricing, using sophisticated algorithms to adjust fares based on real-time demand, booking patterns, and competitive conditions. This approach allows them to maximize revenue by pricing seats higher when demand peaks and lowering prices to fill remaining seats during slower periods.
According to Skift Research, the integration of AI into airline revenue management is seen as a multi-billion-dollar opportunity, potentially boosting airline profitability by up to 1 percent today, representing a $30 billion revenue opportunity. As data quality improves and legacy systems are phased out, AI-based simulation models could contribute up to 5 percent to airlines’ bottom line within the next five years, translating to over $100 billion in revenue opportunities by 2030.
Hospitality revenue managers can benefit by adopting similar dynamic pricing models that integrate real-time data from a variety of systems.
“A unified system architecture allows seamless integration of revenue management systems (RMS) with other critical business tools such as enterprise resource planning (ERP) and customer relationship management (CRM) platforms, ensuring accurate, real-time data flow,” Taylor said. “This eliminates data silos and reduces manual interventions, improving decision-making and operational efficiency.”
According to Taylor, more hospitality companies are leveraging AI to connect data from these systems to power their dynamic pricing strategies.
“A leading global hospitality company uses AI-powered revenue management systems to dynamically adjust room rates based on booking patterns and market trends, optimizing revenue and guest satisfaction,” he said. “Meanwhile, an American vacation property rentals company leverages AI to predict demand and set dynamic pricing for its listings, helping hosts maximize their earnings while offering competitive prices to guests.”
Insight #2: Avoid Overbooking and Underbooking by Forecasting Future Demand
Beyond dynamic pricing, AI is also driving advancements in predictive analytics for occupancy and revenue optimization across different booking channels, allowing hospitality revenue managers to make more informed decisions.
“AI uses historical data and machine learning models to forecast future demand,” Mummert said. “This ability to anticipate booking trends helps revenue managers optimize inventory and pricing strategies, ensuring they can adjust to market changes quickly. By accurately forecasting demand, businesses can ensure that they have the right inventory available at the right time. This reduces the chances of overbooking or underbooking, leading to a smoother and more reliable booking experience for customers.”
According to Mummert, an American multinational hospitality company expects its tech spending to reach $1 billion to $1.2 billion in 2024. To optimize occupancy rates and enhance profitability, the company leverages advanced AI-driven demand forecasting across its extensive global portfolio. By analyzing large volumes of historical booking data, real-time market trends, and seasonal patterns, the company’s AI systems can predict demand fluctuations with remarkable accuracy.
Insight #3: Use AI to Boost Personalization and Drive Repeat Bookings
“AI also facilitates customer segmentation at scale, enabling companies to personalize marketing and pricing based on individual behaviors and preferences, which can significantly enhance the guest experience,” Mummert said.
Advanced revenue management systems can analyze customer data to offer personalized pricing and promotions. This ensures that customers receive offers tailored to their preferences and booking behaviors, delivering the advanced level of personalization customers have grown to expect since the pandemic.
Capgemini’s research underscores the significant impact of personalization on customer loyalty. Their report reveals that 80 percent of customers are drawn to fast, easy-to-use personalized services, emphasizing the demand for instant gratification.
Imagine a hypothetical guest, Sarah, who frequently books weekend getaways and prefers oceanfront suites. An AI-powered revenue management system at a beachfront resort detects her booking history and recognizes her preference for these specific stays. When Sarah next visits the resort’s website, the AI system offers her a personalized discount on oceanfront suites for her preferred weekend dates, along with a promotion for a spa package — something she enjoyed during her previous stay. By tailoring the offer to Sarah’s preferences and past behaviors, the resort not only increases the likelihood of her booking but also enhances her sense of being valued and understood.
With 24 percent of marketers already using AI for audience segmentation, it’s time for hospitality revenue managers to follow suit with their customer pricing strategies.
The Future of Hospitality Revenue Management
The deeper integration of AI technologies is set to transform revenue management, offering unprecedented capabilities in data analysis and real-time decision-making.
“In the hospitality industry, staying ahead in revenue management often hinges on leveraging tools that can swiftly adapt to changing market conditions,” Taylor said. “Capgemini’s ElevateRM framework provides a solution tailored to meet this need, integrating AI for real-time demand forecasting, dynamic pricing, and predictive analytics. Designed to enhance both operational efficiency and customer satisfaction, this tool aligns well with the industry’s growing reliance on technology to deliver highly personalized guest experiences.”
To explore Capgemini’s resources and connect with a representative about the ElevateRM framework, click here.
This content was created collaboratively by Capgemini and Skift’s branded content studio, SkiftX.
