A general introduction to Machine Learning
Machine Learning is a subset of Artificial Intelligence in which computers can develop and improve algorithms by learning from both old and new data. Within machine learning, there are distinctions between Supervised Learning, Unsupervised Learning, and Reinforcement Learning. However, for a foundational understanding, these distinctions are not initially relevant. In essence, rather than receiving explicit instructions to perform a specific task, systems can use machine learning to recognize patterns and relationships in data that stem from a wide variety of data sources, in large quantities, and at high speeds (Big Data). This signifies that we could not speak of Artificial Intelligence without Machine Learning. The ability to discern patterns in data and extract knowledge from it is crucial for the creation of intelligent, autonomously operating systems.
Conversely, AI provides context and comprehension to machine learning in order to achieve human-like intelligence and simulate human-like decision-making. AI utilizes the insights gained from machine learning to accomplish complex tasks such as image recognition, language understanding, autonomous driving, or tailored price recommendations for the hospitalityl industry.
How ARIS Learns and Operates
In the previous blog posts, several data sources and their impact on hotel demand and pricing have already been explained. Naturally, not every data feature is relevant for every hotel. Hence, ARIS was developed as a two-stage model. The first step involves assessing each of the over 1000 data features. One can envision it as someone in the background observing over the past years whether there's an effect on pickup when, for example, it rains or the sun shines. If this is not the case, these data features are temporarily excluded from the subsequent steps, which involve price calculations. However, this process is regularly repeated to ensure no potential changes are missed.
In the second step, ARIS continuously monitors the data features where it has identified a correlation (statistical cause-and-effect relationships). For example, if ARIS found a connection between pickup for weekends and wind strength in the initial analysis, the system examines the weather data for the upcoming weeks and evaluates changes on an hourly basis.
Suggested reading: Discover how wind strength can influence hotel pickup in Volume 2.
In the final step, the system ranks all data features according to their relevance for hotel pricing. Features that exert a stronger influence are consequently given higher weights and carry more significance in the price determination. This ranking is presented to the user in the Hotellistat InsightFeed, aiming to establish an understanding of ARIS's pricing recommendations and to allow the hotel to learn which factors are particularly crucial for their property. Additionally, this transparency fosters trust in the system, as unlike many other Revenue Management Systems, it becomes clear how the price recommendations are calculated and generated.
Enriching the System through Personal Experience
There is also the option to "self-train" the system and provide it with hotel-specific insights. For instance, the preferred market position can be set, or whether the system should generate higher occupancy or rates. In this process, the hotel can decide whether to establish a minimum or maximum rate, or if price recommendations should be entirely data-driven. Additionally, the system learns from manual price adjustments made by the hotel.
Furthermore, the hotel can choose to embed its own strategy within Hotellistat. ARIS then operates based on an "if-then" scenario. For example, if the wind conditions are favorable, the hotel may want to increase its rate by an additional 10%. As ARIS receives weather data, the system adjusts hotel prices accordingly once the wind conditions are reported as favorable.
Conclusion
Thanks to machine learning, ARIS can identify patterns and correlations within extensive hotel and market data, enabling it to autonomously make optimal pricing decisions and demand forecasts. As a Hotel Revenue Management System, ARIS can simulate human-like intelligence in pricing determination and setting price and restriction strategies. The ability to rapidly analyze new data provides a significant speed advantage. Furthermore, Hotellistat collects and observes far more data than a human ever could, allowing it to conduct situational analyses and decision-making not only faster but also more accurately.
