By Adam and Larry Mogelonsky
Hotels accumulate a lot of data, all of which can be used to uncover hidden patterns for better efficiencies and throughout improvements. Understanding the intricacies of machine learning (ML) and its applications is essential for forward-thinking hoteliers. A key concept to grasp is ‘data maturation’, which refers to the necessary period for ML to process and analyze vast amounts of training data. This process allows computers to test variables and identify patterns, ultimately developing algorithms and models that drive business goals.
Time is a critical factor in this equation. While machines can process data at incredible speeds, there is a significant difference between human and machine learning rates that can impact our expectations of AI deployment. Humans learn through causal inference—understanding cause and effect relationships intuitively. For example, if you see a burnt-down house, you instinctively deduce that a fire occurred. In contrast, machines learn through correlation. Without extensive data points, a computer cannot confidently determine if a fire caused the house to burn down or if another factor was involved.
To achieve high confidence levels in their predictions, machines need to analyze numerous variables across countless instances, developing and refining probabilistic models. This process mirrors human maturation; just as a child does not become an adult overnight, ML models require time and data to mature.
Two key actions are implied by this:
- Deeper Data Connections. The more data fields associated with a given observation, the better the AI can identify hidden patterns and build accurate models. This highlights the importance of integrating various systems through APIs, CDPs and other unified platforms, making these tools a top priority in a hotel’s commercial strategy.
- Multivariate Testing. In addition to comprehensive data interfaces, machines need extensive observations to improve accuracy. This involves A/B testing and examining customer interactions with the hotel’s measurable aspects, such as the website or mobile app. Over time, these interactions help the machine refine its models.
The necessity for a long-term data maturation plan is evident when considering how it can impact prearrival upselling revenues. Take, for example, a hotel using Nor1’s upselling platform. Suppose the hotel sends a confirmation email upon reservation and again seven days before arrival, offering three upgrade options: adding breakfast (F&B), upgrading to a suite (rooms), or purchasing a spa voucher (wellness). Initially, these offers are presented equally across different positions for different customers. Customer #1 might see F&B first, rooms second, and wellness third, while Customer #2 sees rooms first, wellness second and F&B third.
If Customer #1 chooses to add breakfast, is it because breakfast was the top option, or was it genuinely preferred? Similarly, if Customer #2 also chooses breakfast when it appears last, is this a strong enough indicator of preference, or is the sample size too small? Numerous variables could influence these decisions, from the visual appeal of the offer’s photography to its relative cost, or even the time of day when the customers interact with the upselling platform.
At this early stage, the data is insufficient to draw definitive conclusions. The solution is to continuously test and refine the offers based on accumulating customer interactions. Since guests won’t uniformly visit the website or open prearrival emails as soon as the ML tool is implemented, it will take time for observations to accumulate and for the model to mature. Therefore, starting the data accumulation process as soon as possible is crucial.
While ML offers transformative potential for the hospitality industry, realizing its benefits requires a commitment to data maturation. By integrating deeper data connections and engaging in continuous multivariate testing, hoteliers can refine their AI models over time. This long-term approach not only enhances operational efficiency and guest satisfaction but also positions hotels to better capitalize on the opportunities presented by advanced ML applications. Embracing this strategy now ensures that your property remains at the forefront of innovation in an increasingly competitive market.
