Hello everyone, I’m Youssef Farag, a Data Scientist and Machine Learning specialist at Hotellistat. With a deep passion for the field and over 6 years of experience working on various AI projects, I’m excited to guide you through a new series of blog posts about machine learning and AI. I’ll be sharing internal insights about AI, how we approach “machine learning” within the Hotellistat team, and how we manage your sensitive information with care.
What is Artificial intelligence (AI)?
In this blog post, we will be covering what machine learning is, why it is beneficial for the hotel industry, and why it is taking the world by storm– offering solutions that other systems can’t even begin to match.
Artificial intelligence (AI) gives machines the power to automate various tasks and predict future scenarios. Machine learning (ML), a subset of AI, allows machines to act as "students," learning from real-life data and situations.
Machine learning is, quite simply, exciting! It has proven valuable across a broad range of fields, from healthcare—where it aids in detecting diseases and health conditions—to transportation, powering the advancements in self-driving cars. ML has delivered solutions that once felt futuristic, as though they were centuries away. And yet, it’s still in its infancy, only gaining real momentum between 2010 and 2012. With AI proving itself in many fields, what makes it so promising in hospitality? Can it only add value to image-based/Robotics problems, or is there more?
The truth is, that AI has been rapidly adopted in the hospitality sector, bringing transformative shifts to the industry. Hotels are increasingly relying on this "new genie" to streamline operations, automate pricing, and predict demand—just a few of the benefits we’ll explore in this post. But first, let’s dive into what machine learning is and why it’s causing such a global stir.
What is Machine Learning?
We have previously mentioned that machine learning is the process of a machine, learning from real-life information and data, similar to how a student is taught by a teacher, but how does this work?
Machine learning works by collecting and analyzing data, mathematically extracting information, and identifying trends and patterns to provide insights and make predictions based on new, unseen information. The process of extracting relationships between input variables and target output (predictions) is called training, and the saved state of the process is what we call a trained machine learning model. Over time, as the model processes more data, it becomes more accurate and thus provides better predictions with higher accuracy for future outcomes.
Machine learning algorithms vary widely, ranging from algorithms capable of learning from real-life simulations (reinforcement learning algorithms) to others that can extract relationships and clusters within data (unsupervised learning). Lastly, there are algorithms capable of learning previous trends to make accurate predictions toward specific goals (supervised learning). Each of these categories encompasses a wide range of specialized fields and algorithms, each with unique tasks and advantages over the others.
One subset we frequently work with at Hotellistat is time series forecasting (TSF). TSF is a method for predicting future values based on historical events over time. In a time series, each data point is indexed by time, enabling the model to capture temporal patterns, cycles, and seasonality within the data. The main goal is to identify these historical patterns, learn from them, and use this insight to predict future events.
This makes time series forecasting an ideal solution for various problems in the hospitality industry, such as demand forecasting and optimal price prediction. Common techniques include statistical methods like ARIMA, and other more data and machine learning-centric methods, such as Long Short-Term Memory Networks (LSTMs). LSTMs are well suited for handling sequences of data with long-term dependencies, where events happening even more than one year ago could still hold value for future predictions. This capability makes LSTMs a core method in various sections of our training pipelines.
Another method we utilize in our system for time series forecasting is boosting. Although boosting methods are not typically associated with direct applications in time series forecasting (TSF), they excel at capturing complex non-linear relationships between input features, especially when dealing with a large number of features. For example, if your hotel experiences a higher number of bookings during weekends with events, favorable weather and below-average market rates, boosting methods can detect such patterns and adjust predictions accordingly. Given that our pipeline includes 700+ features, employing boosting methods is essential. In upcoming blog posts, we will explore boosting methods and LSTMs in greater depth.
Why Machine Learning?
Now that we’ve explored what machine learning is, the next question looms large: Why machine learning?
The answer actually lies in the transformative power machine learning holds, and here is why:
- Utilizing all of your data: ML algorithms make it easy to analyze vast amounts of data, whether it spans two years or just a few months, uncovering patterns and insights that humans might miss or take years to find.
- Effectively evaluating other information sources: When implemented effectively, ML can consider over 700+ features – ranging from weather, events, search information, reviews, to holidays — automatically selecting the features most relevant to your hotel.
- Automation: Instead of requiring a specialized team to monitor your data daily to uncover new patterns, ML can do it for you automatically, easily scaling to handle millions of data points—something traditional systems or manual methods struggle to achieve.
- Consistency: Machine learning models are immune to emotional influence or bias. They analyze data objectively, providing consistent results based solely on input data.
- Adaptability: ML models can continually improve and refine themselves over time. The more they process, the more accurate they become, growing increasingly powerful without any manual intervention.
In conclusion, artificial intelligence is undeniably a powerful tool with immense potential to revolutionize the hospitality industry—whether through automating routine tasks, enhancing revenue management, or providing crucial insights. In Youssef's next blog post, he’ll walk you through the entire machine-learning journey leading to the final price recommendation, breaking down each step along the way.
In the end, the main engine of hospitality will forever be the human touch, defining the warmth and authenticity of the industry, but this does not diminish the positive impact AI could have, elevating almost all the industry's sectors. In this age where data is omnipresent, it is always great to have an assistant along the road, giving you that competitive edge.
