Today’s hospitality revenue managers operate in an increasingly complex environment shaped by evolving consumer preferences, regional nuances and growing competition from short-term rentals. Given these factors, the need for transformation in revenue management practices has never been more pressing, especially in key markets like the United States, where revenue per available room (RevPAR) growth in 2024 is expected be barely 1.2 percent.
To adapt and thrive in this challenging landscape, the travel industry is demonstrating a strong appetite for technology investments. According to recent researchplanned technology investments are expected to increase by 14% in 2024, with 91% of travel agencies forecasting “moderate to aggressive” growth in their technology spending. These investments highlight a widespread recognition of the need to adopt innovative technologies to ensure revenue management success.
“The rapid pace of technological change, including the adoption of AI and machine learning, requires significant investments in new systems and staff training,” said Ryan Mummert, senior director, responsible of the information and data portfolio at Capgemini. “Hospitality revenue managers must stay up to date with the latest innovations to stay 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, improve demand forecasting, and deliver personalization at scale through guest segmentation.
Insight #1: Take inspiration from airlines to refine your dynamic pricing strategies
“Airlines have long been pioneers in dynamic pricing, adjusting rates based on demand, booking patterns and other factors,” said Lee Taylor, head of hotel sales at Capgemini. “Hotels and other travel agencies can adopt similar strategies to adjust room and package rates in real time, allowing them to better adapt to 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 increasing the price of seats when demand peaks and lowering prices to fill remaining seats during slower periods.
According to Skift ResearchIntegrating AI into airline revenue management is seen as a multi-billion dollar opportunity, with the potential to increase airline profitability by up to 1% today, representing an opportunity to revenue of $30 billion. As data quality improves and legacy systems are phased out, AI-based simulation models could contribute up to 5% to airlines’ bottom lines over the next five years, increasing significantly. would result in more than $100 billion in revenue opportunities by 2030.
Hospitality revenue managers can benefit from adopting similar dynamic pricing models that integrate real-time data from various systems.
“A unified system architecture enables 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 and real-time data flow,” said Taylor. “This eliminates data silos and reduces manual intervention, improving decision-making and operational efficiency. »
According to Taylor, more and more hotel companies are leveraging AI to connect data from these systems to power their dynamic pricing strategies.
“A global hospitality leader uses AI-powered revenue management systems to dynamically adjust room rates based on booking patterns and market trends, maximizing revenue and guest satisfaction “, he said. “Meanwhile, a US vacation property rental company is leveraging AI to predict demand and set dynamic pricing for its listings, helping hosts maximize revenue while offering competitive prices to guests.”
Insight #2: Avoid overbooking and underbooking by forecasting future demand
Beyond dynamic pricing, AI is also advancing predictive analytics for occupancy and revenue optimization across different booking channels, enabling hospitality revenue managers to make more informed decisions .
“AI uses historical data and machine learning models to predict future demand,” Mummert said. “This ability to anticipate booking trends helps revenue managers optimize their inventory and pricing strategies, allowing them to quickly adapt to market changes. By accurately forecasting demand, businesses can ensure they have the right inventory at the right time. This reduces the risk of overbooking or underbooking, leading to a smoother and more reliable booking experience for guests.
According to Mummert, a US multinational hospitality company expects its technology spending to reach $1 billion to $1.2 billion in 2024. To optimize occupancy rates and improve 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 trends, the company’s AI systems can predict fluctuations in demand with remarkable accuracy.
Insight #3: Use AI to Improve Personalization and Drive Repeat Bookings
“AI also facilitates customer segmentation at scale, allowing businesses to personalize marketing and pricing based on individual behaviors and preferences, which can significantly improve the customer experience,” Mummert said.
Advanced revenue management systems can analyze customer data to provide personalized pricing and promotions. This ensures that customers receive offers tailored to their preferences and booking behaviors, providing the advanced level of personalization that customers have come to expect since the pandemic.
Capgemini research highlights the significant impact of personalization on customer loyalty. Their report reveals that 80 percent of customers are attracted by personalized services that are quick and easy to use, emphasizing the demand for instant gratification.
Imagine a hypothetical guest, Sarah, who frequently books weekend getaways and prefers beachfront suites. An AI-based revenue management system at an oceanfront resort detects her booking history and recognizes his preference for these specific stays. The next time she visits the resort’s website, the AI system offers her a personalized discount on beachfront suites for her favorite weekend dates, as well as a promotion on a spa package – something which she enjoyed during her previous stay. By tailoring the offer to Sarah’s preferences and past behaviors, the resort not only increases the likelihood that she will book, but also reinforces her feeling of being valued and understood.
With 24 percent Marketers are already using AI for audience segmentation, it’s time for hospitality revenue managers to follow suit in their customer pricing strategies.
The Future of Hospitality Revenue Management
The deeper integration of AI technologies is expected to transform revenue management, providing unprecedented capabilities in data analysis and real-time decision-making.
“In the hospitality industry, staying ahead in revenue management often depends on using tools that can quickly adapt to changing market conditions,” Taylor said. “Capgemini’s ElevateRM framework provides a tailored solution to meet this need, integrating AI for real-time demand forecasting, dynamic pricing and predictive analytics. Designed to improve both operational efficiency and customer satisfaction, this tool aligns well with the industry’s growing reliance on technology to deliver highly personalized customer experiences.
To explore Capgemini resources and connect with a representative on the ElevateRM framework, Click here.
This content was created in collaboration by Capgemini and branded content studio Skift, SkiftX.