An Interesting Application for Hotel AI Is Guest Segment Suggestions

By Larry and Adam Mogelonsky | February 24, 2025

Hotels depend on segmentation, but why stop at just three (leisure, corporate, group)? With advancements in data streaming, APIs, data warehousing and AI-driven analytics, hotels can now achieve an unprecedented level of granularity in their KYC (know your customer) efforts. Understanding guest behaviors – where they book, why they choose a property and how they spend – has evolved from broad demographic insights to hyper-specific ‘micro-segmentation’ that can drive personalized marketing, optimized revenue strategies, and enhanced guest experiences.

That’s all great and many industry pundits have already extolled ad nauseum the benefits of AI. What does that look like moment to moment, interface to interface and tool to tool? Without writing an entire book on the subject matter, this article focus on one long-term benefit from augmented data streaming and warehousing that can allow hoteliers to test inborn assumptions about guest segmentation to then allow for neutral, machine-driven micro-segmentation and all the sales, marketing and guest service efforts that may result from those insights.

Right now, with good use of APIs, disparate hotel technologies— PMS, CRM, RMS, GEMS and so on – can now be synchronized to create a unified guest profile. While the PMS was historically the central hub, the CRM is increasingly taking center stage due to its ability to aggregate first-party data across properties and integrate above-property insights.

Yet, API connectivity comes with its own set of challenges. Every new system added to the tech stack requires IT resources to integrate, maintain, and troubleshoot these connections, making large-scale implementations resource intensive. This is where robotic process automation (RPA) has emerged as a game-changer, replacing manual double-entry work by acting as an intermediary that bridges gaps between non-integrated systems.

Once guest data is aggregated, cleansed, and structured, the true power of machine learning (ML) can be harnessed. ML excels at identifying hidden patterns within vast datasets, revealing guest behaviors and predictive insights that human analysis could never uncover. The more data the system processes, the sharper and more accurate its predictive models become.

The most immediate and lucrative application of ML in hospitality has been within revenue management. Today’s advanced RMS platforms ingest massive data sets—including market trends, competitor pricing, and historical booking data—to generate real-time pricing recommendations. But ML’s capabilities extend far beyond pricing strategy; it can also redefine guest segmentation by analyzing behavioral, transactional, and psychographic data to uncover new audience clusters.

Rather than relying solely on traditional segments—leisure, corporate, group—AI-driven recommendations can identify microsegments that might otherwise go unnoticed. For instance, it could reveal that weekday business travelers who book direct are more likely to engage with upselling offers than those booking through an OTA. Or that wellness-focused guests are more inclined to extend their stays when presented with customized spa or fitness packages. The possibilities for revenue, marketing, and sales teams are limitless.

As previously stated about inborn segment bias based on classical hotel training, AI doesn’t just reinforce existing assumptions; it challenges them. Many of us in hospitality have spent years working within conventional guest segmentations, making decisions based on experience and industry norms. ML without such biases, providing a fresh, data-driven perspective on who your true high-value guests are and how to attract more of them.

This technology presents an opportunity to refine everything from digital ad spend to package offerings, optimizing guest acquisition and retention strategies in ways previously unimaginable. However, before diving into AI-driven segmentation, hotels must first ensure that their foundational systems are integrated, their data is structured, and their teams are prepared to adapt to AI-driven insights.

Perhaps the most thought-provoking challenge ahead isn’t just technological; it’s cultural. What happens when AI identifies microsegments that contradict long-standing assumptions held by your teams? The willingness to embrace these insights and pivot accordingly could be the key to staying ahead in an increasingly data-driven industry. One thing is certain: as AI continues to evolve, the future of hotel segmentation will be more precise, dynamic and profitable than ever before.