In today’s wide realm of revenue technology and analytics, there’s been a “new” algorithmic process called machine learning garnering a lot of air time in recent revenue management discussions. Interesting enough, machine learning is actually not new to the hospitality industry at all – advanced revenue management technology has been incorporating this process, where appropriate, into their analytics for quite some time. SAS Institute, IDeaS’ parent company, defines machine learning as “a method of data analysis that automates analytical model building.” Basically, this process enables computers to find hidden insights without being explicitly programmed where to look (think: web searches or targeted marketing advertising.) IDeaS’ advanced revenue management systems have long been using the process of machine learning, in conjunction with statistical methods, to produce cutting-edge forecasting and decision optimization. However, machine learning doesn’t come without its complexities: the learning methods it needs to consider, its relationship with both data mining and statistics, and the types of problems its application works well for. Get the full story at IDeaS