By Larry and Adam Mogelonsky
Customer segmentation has been critical from Day 1 for the modern hotel industry. More recently, with numerous data sets being merged due to technological advances, brands are starting to get granular with their KYC (know your customer) in terms of understanding where guests are coming from, why they’re selecting your property, and what their buying.
Layering on AI tools on top of this data can now allow hotels to do far more within this burgeoning field of micro-segmentation.
To start, we must grasp how all this data is coming together. In contrast, with the dawn of application programming interfaces (APIs), disparate systems used by different operations could be strung together by structuring data field imports into a centralized storehouse. The PMS has always been a likely candidate for this nexus.
Common friction points for working with APIs has been that IT professionals need the spare time to set up each interface and maintain all established connections with each subsequent software update. With each new system added to the tech stack, this quickly becomes resource-intensive. A specific type of AI called robotic processing automation (RPA) has already proven itself by acting as a robot that can directly replace double entry work that has to be done manually because two systems haven’t been integrated to talk directly to each other.
Once you have all this data imported, cleaned and structured into proper data fields, you now have an enormous treasure trove of numbers. While this database is far too vast for a pair of hotelier eyes to pick out patterns, the AI specialty of machine learning (ML) is designed precisely for that task. The more data you give it, the more patterns it can potentially find and the more accurate its predictions will be.
The key to ML is that it can produce a predictive model to optimize for desired future outcomes. Then, once that model is tested out in the field, the best AIs can use the new data as feedback to improve their own modelling algorithm, further enhancing their predictive power to better optimize for a stated objective.
Hotels have already seen lucrative applications for ML in the Revenue-Management System (RMS), where massive data sets comprising external and internal inputs are computed into an algorithm that can recommend pricing for rooms revenue, occupancy or total revenue per guest stay.
It’s this notion of recommendations that brings us to the concept of having ML interpret not only how to adjust nightly rates or what response to provide for a website chatbot, but also to look at the multitude of guest profile data and then come back with its own set of micro-segments for your revenue, sales and marketing teams to interpret and pivot their planning accordingly.
Currently, all of us are operating under a given set of established business assumptions based on how we were trained and our experience working in hotels. We see the world in terms of leisure, corporate and groups, and many of us have become locked into these guest segments. Recommendation engines based on ML don’t have those same limitations and thus can provide a fresh set of eyes on what your real segments are.
Larry and Adam Mogelonsky are partners of Hotel Mogel Consulting Limited. You can reach Larry at [email protected] or Adam at [email protected]
