Customer segmentation has been critical from day one for the modern hotel industry. More recently, with numerous data sets being merged due to technological advances, brands are starting to get quite granular with their KYC (know your customer) in terms of understanding where guests are coming from (channels and geography), why they are selecting your property (leisure, corporate, conference and so on) and what their buying (packages, upselling offers, onsite ancillary spend or others).
Layering on artificial intelligence tools on top of all this data can now allow hotels to do far more than even five years ago within this burgeoning field of micro-segmentation, but this requires some understanding of how AI works (as well as a lot of techie acronyms).
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. Still, increasingly it’s the customer relationship management (CRM) because of this system’s propensity to position all data around unified guest profiles and to incorporate above-property inputs.
Common friction points for working with APIs has been that an IT professional needs the spare time to set up each interface, and then maintain all those established connections with each subsequent software update. With each new system added to the tech stack, this quickly becomes resource-intensive. Here, 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.
And it gets far better. Once you have all this data imported, cleaned (to remove duplicates) 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. You give it the data; it finds the patterns, however, hidden they may be to the human overseer. The more data you give it, the more patterns it can potentially find and the more accurate its predictions will be.
Besides looking at vast amounts of data and then giving insights into that data, 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 then use the new data as feedback to improve their own modeling algorithm, further enhancing their predictive power to better optimize for a stated objective.
Where hotels have already seen the most lucrative applications for ML is in the revenue management system (RMS), with massive data sets comprising external and internal inputs are computed into an algorithm that can then recommend to the revenue director what pricing will optimize for rooms revenue, occupancy or now total revenue per guest stay.
It’s this whole 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 microsegments for your revenue, sales and marketing teams to interpret and pivot their planning accordingly.
As of now, 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.
Perhaps this latest technology will help your hotel find an edge over the competition or allow you to deploy the advertising budget more effectively. Maybe it will give you suggestions on what packages will work better for attracting leisure guests or what types of groups are most winnable for your meetings and events business. Just as AI helps us to rethink business assumptions, neither of us would dare assume to know what such a tool would find buried within your hotel’s data.
Our advice is to first chart a path for connecting all your systems, and only then investigate these more advanced ML tools. At the same time, you will also have to confront the cultural, more existential scenario of what happens when the AI finds microsegments that contradict those that your teams are working off. We live in exciting times!
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