Imagine your city hosts a yearly festival, and you’re determining how to price your rooms for the upcoming year.
NB: This is an article from Cloudbeds, one of our Expert Partners
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Your revenue management system identifies that during the dates of the festival, there’s a spike in reservations and suggests that you increase room rates slightly during the festival period due to higher demand.
However, a deeper investigation incorporating real-time demand feeds reveals that undercutting the competition by 5% (while offering a special bundled package, including tickets and transportation to the festival) precisely influences bookings in a way that maximizes total revenue.
Why the opposing views? The initial suggestion uses a predictive machine learning algorithm designed to find statistical correlations in historical data. The result is a form of inductive reasoning: “When an outcome X happens, variable Y also happens. Therefore, Y likely causes X.”
However, correlation and causation are two different things, and confusing them can lead to misguided conclusions. These types of errors could be prevented with causal AI.
In this article, we explore how causal AI is being adopted across industries to help improve decision-making and its impact on hospitality.
What is causal AI?
Causal AI is a branch of artificial intelligence that uses distinct models and algorithms to uncover true cause-and-effect relationships. It analyzes billions of forward-looking data points to help guide real-time decision-making.
While correlation-based AI makes predictions by detecting patterns in historical data, causal AI understands why something happens, leading to more accurate insights.
Causal AI can also elaborate “what if” scenarios to predict outcomes from different combinations of variables. This is where it surpasses correlation-based AI, which is limited to historical data and struggles to predict novel scenarios caused by new patterns.
Causal AI in hospitality
Causal AI is making strides across industries, and hospitality is no exception. While adoption has been slower, we’re starting to see the power behind causal AI emerge for hotels. For instance, it can help address complex questions that have traditionally been difficult to answer accurately, such as:
- How large a discount is needed to increase the likelihood of winning a particular customer back without unnecessarily cutting into revenue?
- How much do we need to undercut a competitor to increase bookings and maximize revenue rather than blindly matching the lowest rates?
- Why was revenue during the high season lower than expected? What would have happened if we had increased or decreased rates?
- What segments should we target with our upcoming marketing campaign, and what packages should we offer?
There are three areas of a hotel’s operations where causal AI is particularly promising.
