Revenue Management: From Spreadsheets to AI Based Systems

Revenue Management, as a discipline, has come a long way since its inception in the 70s. From spreadsheets to today’s sophisticated AI-based systems, this area has embraced significant technological advances that are revolutionizing the hotel industry.

NB: This is an article from XLR8 RMS, one of our Expert Partners

These innovations allow companies to optimize their revenues more precisely, improve the customer experience and remain competitive in a dynamic and ever-changing market.

The History of Revenue Management

Revenue management began as a simple practice: adjusting prices based on demand forecasts. It involved manual calculations and simple tables, and hotels and airlines were early adopters of these techniques, recognizing the potential to maximize their revenue by strategically managing room and seat availability, respectively.

In those early days, Revenue Managers relied heavily on historical data and basic statistical models. While this approach provided some benefits, it was labor-intensive and prone to human error. The need for precision and efficiency led to the creation of more advanced systems. Revenue Managers began to realize that spreadsheets, while practical, couldn’t keep up with the increasing complexity and volume of data needed to make informed decisions. In addition, the manual process of collecting and analyzing data was time-consuming and error-prone, which could result in less accurate decisions that negatively impacted revenue.

Limitations of traditional methods

Traditional revenue management methods have several limitations:

• Data processing: Spreadsheets cannot efficiently manage large amounts of data, making it difficult to analyze multiple variables simultaneously.
• Accuracy: Manual data entry and calculations are prone to errors, leading to inaccurate forecasts and sub-optimal pricing decisions.
• Real-time adjustments: Traditional methods do not have the capacity to make real-time price adjustments based on changes in market conditions.
• Forecasting capabilities: Basic statistical models are insufficient to accurately predict future demand, especially in the face of unexpected events or market changes.

These limitations often result in missed opportunities and reduced profitability, highlighting the need for more advanced solutions.

The advent of RMS’s

The introduction of AI-based systems marked a significant turning point in Revenue Management with the introduction of algorithms, big data analysis and real-time processing capabilities to offer unprecedented accuracy and efficiency.

Read the full article at XLR8 RMS