UseCasesFor.ai

AI Use Cases

A collection of over 250 uses for artificial intelligence

A continually updated list exploring how different types of AI are used across various industries and AI disciplines,including generative AI use cases, banking AI use cases, AI use cases in healthcare, AI use cases in government, AI use cases in insurance, and more

Sign up

to receive a PDF containing all the use cases and stay updated with the latest AI trends and news (you can always unsubscribe)

Dynamic pricing of hotel rooms and other amenities based on demand forecasts

Introduction

The conventional way of pricing hotels has been that of static pricing wherein prices are usually constant for long periods. But with the integration of Generative Artificial Intelligence especially through machine learning and predictive analytics, this is changing at a very fast rate. This has made it possible to implement dynamic pricing systems which are variable and responsive to the dynamic nature of the market. This change is most evident in the accommodation sector where hotels are in a constant search for ways to enhance their occupancy levels and RevPAR. The implementation of AI in dynamic pricing has brought about a revolution in the way hotel room prices are set. Hotels in the past had only previous occupancy data, seasonal patterns and competitors’ prices to determine their charges. While this approach served a certain purpose, it often resulted in losses during high demand periods or over pricing during the slow season through unnecessary promotions. However, this is not the case with AI which gives a better and detailed analysis of the future demand trends. Dynamic pricing of hotels can therefore be defined as the process of varying the prices of hotel rooms in relation to certain factors such as availability of rooms, time of the year, events and other factors which may affect the demand for hotel rooms. This capability for adaptation provides the hotels to better coordinate their prices with the demands of the market and hence improve on their revenue and profitability. There are numerous advantages of AI in dynamic pricing. The first and probably the most important benefit is the enhancement of the predictive capabilities. The AI algorithms can process large sets of data on occupancy rates, weather, air ticket prices, and search queries to produce better demand estimates. Through having better forecasts, hotels can make proper decisions on their pricing strategies and avoid underpricing their rooms in peak seasons while at the same time avoiding overpricing in off seasons. Furthermore, the AI-enabled dynamic pricing systems can make the process easier and more efficient for both the hotels and the guests. It is not necessary for hotels to set the prices manually and this is a rather time-consuming and a inaccurate process. AI helps in changing the prices effectively and efficiently thereby freeing up the time of the staff for other important activities. On the part of the guest, price dynamic pricing ensures that the guest is only offered prices that are relevant to the search query and preferences that the guest has specified, to enhance the guest experience and possibly increase the conversion rate. Besides, AI can also help in enhancing other aspects of the hotel such as the pricing strategies. For example, it is possible to optimize inventory management because AI can estimate when the rooms will be empty and vary the prices of house rates accordingly. This approach not only enables the avoidance of deep discounts but also avoids overpricing in the high season. Also, AI can be used to improve the quality of service by suggesting services or activities that may be of interest to the customer depending on the duration of their stay or their interests. Through the use of chatbots, hotels can provide assistance to the guests by answering basic questions and solving basic issues that the guests may have, thereby enhancing the customers’ experience. However, there are some challenges that can be seen with the adoption of AI in dynamic pricing. The first issue is the issue of data privacy since hotels have to deal with guests’ information. It is therefore important that hotels balance the use of guest data in a way that will be in compliance with data protection laws such as the GDPR. There is also a possibility of ‘over- reliance’ on the AI algorithms which may result into setting wrong prices if the data used is inaccurate or if the system fails to consider other key market variables. Another problem is the issue of interoperability with current PMS and reservation systems which may be costly and complicated. This means that without full integration, the AI dynamic pricing solutions cannot automatically reset the prices of the available rooms.

Challenges

One of the biggest issues that the tourism industry currently deals with is the variability of the demand which is dependent on the season, rivals, and the general state of the economy. This is quite challenging since these changes cannot be forecasted easily for the purpose of dynamic pricing. Also, there is a huge amount of data that needs to be processed and analyzed for the purpose of achieving precise prediction and all these present challenges in processing and analysis. The manual data handling is error prone and may be very time consuming thereby causing the company to miss opportunities. Last but not the least, it becomes a difficult task to manage between the price optimization and customer satisfaction. If prices are high the consumers may be driven away by other companies while if prices are low then the organization may be losing out on a lot of revenue.

AI Solutions

Here Gen AI comes into play to solve the aforementioned problems. In this way, hotels can use AI to understand the past trends, competitors’ prices, online comments, social media activity, and other factors that affect demand. It is possible for AI algorithms to analyze a massive amount of data within a short time and with high level of accuracy which results to accurate forecasts. Also, by implementing AI in the dynamic pricing process, a lot of manual work can be eliminated and less mistakes made. In order to achieve this, AI can be used to set different prices based on customer segmentation in the pricing model so that prices are optimized without alienating customers. Some real-life examples of AI in dynamic pricing include the new revenue management system that Marriott has adopted called ‘One Yield’ and IDeaS Revenue Solutions’ sophisticated pricing software for Accor Hotels.

Benefits

There are a lot of benefits of using Gen AI for dynamic pricing in the tourism industry. It helps in realising the maximum revenue by analysing the demand and setting the prices effectively. With AI, it is possible to meet the ever changing market conditions and still come up with ways of being relevant. It also cuts on costs in the labor aspect by automation and improves decision making through the use of data. In addition, integrating customer segmentation into pricing strategies can result to improved customer satisfaction and hence increased customer loyalty.

Return on Investment

It can be seen that there is a high ROI when AI is applied in dynamic pricing strategies. For example, Marriott’s ‘One Yield’ system helped the company to generate an additional $200 million in revenue during the initial year of its operation. Similarly, Accor Hotels enhanced its revenue per available room (RevPAR) by 5% after integrating IDeaS Revenue Solutions’ sophisticated pricing software. Although, it is important to note that implementation of such AI systems may require a large capital investment, the returns that are obtained in terms of increased revenue, reduced labor costs and improved customer satisfaction make it a worthwhile investment.

Choose Topic