AI and Predictive Analytics for Revenue Management

Revenue from hospitality and tourism

Worldwide, hospitality and tourism has become a major industry, employing millions of people and accounting for about 10% of global GDP in 2019. Following a decline of $4.9tn in 2020 due to COVID-19 restrictions, the sector increased by $1tn in 2021. In 2021, the total number of jobs in the sector worldwide was 289 million, an increase over the 2020 value but still far less than before the pandemic. Although the global pandemic greatly impacted revenue from the sector, it is expected to make a recovery within the next few years. As this happens, the hospitality industry is poised to take control of new measures to improve revenue management using predictive analytics. Predictive analytics can function in: 

i. Optimization of available products for sale

ii. Suggestions for which products to make available in particular locations or to a particular set of customers 

iii. Determination of pricing with respect to demand and other competitors

iv. Development of strategies to target customers based on behaviors and trends

All these combine to ensure effective marketing and maximum customer satisfaction, thus creating a sustainable flow of revenue to the industry. According to McKinsey, using AI in the domain of travel alone can be worth at least $600bn. Predictive analysis is particularly helping in achieving these goals because of the vast amounts of data available for use in the industry. 

The Role of Artificial Intelligence 

Considering the large amounts of data to be analysed and used to make predictions, artificial intelligence is well suited for revenue management. In contrast to traditional methods of revenue management, AI powered methods are expected to provide dynamic pricing and consistently make adjustments to achieve the highest revenue possible. It has been predicted that by 2022, global annual spending on AI by retailers will reach $7.3bn and that by then, AI use will deliver most revenue growth. Already, many hotel chains and shopping malls have begun to tap into this opportunity by using various programs and methods:

Tools input data on various factors such as seasonal variation in product demand and customer habit can automatically adjust prices to reflect changing demand.

Retail artificial intelligence can be used to identify and track retailers who sell below recommended prices, leading to reduction in revenue.

Implementation of review of budget data that tracks performance of the business during holidays and special events to allow optimal preparation for customers.

Strategization of pricing methods based on previous performance of products, current demand as well as future predictions in the market. Two methods, namely segmentation and clustering are employed. Under segmentation, customers are organized according to demographics and this is used to tailor products to consumers. Under clustering, which segments based on hidden relations between several variables.

Identification and elimination of unfavorable discounts, promos etc based on analysis of cost against profit.

Prediction of possible risks to revenue such as fluctuations in demand or cost of products.

Observation of social media traffic to determine the most popular destinations, products and desired deals among the public with which potential customers can be targeted.

Despite these numerous opportunities, concerns have been shown concerning the possible vulnerabilities in automated revenue management. Automation increases the vulnerability to hackers and system failures which could be especially damaging in an area such as revenue management. However, if these concerns are weighed against the opportunity to adequately and accurately analyze data and make predictions to grow revenue, there is no doubt that AI in predictive analytics will go a long way in revolutionizing the industry. 


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