Artificial Intelligence (AI) can be of extreme benefit to the guest experience and when implemented correctly, can speed you on your path to digital transformation. In fact, Servion has predicted that, “AI will power 95% of customer interaction by 2025.” But how do you get started with AI-enhanced guest experience? In this article, we will explore three types of AI driven technologies that can assist with guest experience and recommendations for considering them in a hospitality environment.


Chatbots are a form of conversational AI that is used to simplify human interaction with computers. Many businesses are using chatbots for a first point of contact when customers need help. Have you been to a website lately where a chat window pops up to ask if you need some assistance? It’s likely that that chat window is being managed by a chatbot. Chatbots can automatically answer common inquiries using your language. Chatbots can provide answers to frequently asked questions and manage routine service requests. They are used to help customers navigate websites and direct people to the relevant point of contact.

For hotels, chatbots can be used to answer a myriad of guest requests, such as wake-up calls, calling for transport and laundry service. They can help enhance guest communications, being instantaneous, always on and available 24/7. With multi-lingual capabilities, chatbots can also help manage international guest requests smoothly while putting the guest at ease. Chatbots can free up your staff from the time-consuming task of receiving and responding to routine guest requests and have them focus on situations where human interaction makes a big difference to the guest experience.

Sentiment Analysis (Visual, Voice and Text)

Your guests interact with your brand via many channels – phone, email, chat, guest surveys, review sites and social and digital, as well as in person at your properties. Each of these touchpoints provides the opportunity to gain insights about your guest, what they want and how they feel about your product and services. Sentiment analysis uses natural language processing, one of the foundations of AI. Natural language processing combines machine learning, AI and linguistics and can automate and quantify guest feedback across all guest interactions, regardless of channel.

Natural language processing is important because of the large volumes of text data. It is already possible for a machine to analyze more language-based data than humans can. When you capture insights across multiple channels you can identify opportunities to improve guest experience and prioritize which investments will deliver the most value to your business. These insights also give you the ability to make more effective decisions about guest strategy and can help drive 1:1 guest experiences.

Sentiment analysis can enable you to analyze written communications to determine emotion and intent. Then you can route guest communications, identify guest satisfaction triggers and even demonstrate the connection between positive sentiment and higher lifetime values and guest retention.

Personalized Recommendations

If you have ever shopped with some of the large online retailers, you have probably experienced a recommendation system in action. Recommendation systems provide personalized recommendations by analyzing your historical buying behavior and making recommendations in real-time. Recommendation systems take one of two approaches. The customer-centric approach looks for similarities in the behavior or characteristics between users or guests and recommends products that other similar users have bought. A product-centric approach looks for products that are associated with each other and is helpful when it is the customer’s first time shopping with you. In this you don’t have any past behavior, and you don’t know anything about them or their characteristics, other than what they are showing interest in at that moment. When this occurs, the product most associated with what the customer is interested in is recommended.

AI can accelerate the effectiveness of recommendation engines. Machine learning algorithms that combine both customer-based and product-based recommendations together can help overcome challenges that occur because there is too much behavior data to process in real-time, the guest is too new or there is just not enough information. These enhancements improve the response and conversion rates for the hospitality company as well as delivering a more cohesive guest experience.

Recommendations for AI-Enhanced Guest Experiences:

•Be transparent. Set expectations with guests upfront that they are dealing with a non-human.

•Respect the opt-out. Recognize that human intervention will be either required or desired occasionally.

•Ask for feedback. Let guests provide feedback on recommendations that you are surfacing – a simple thumbs up or thumbs down will do.

Natalie Osborn is senior industry consultant for SAS Institute’s Hospitality and Travel practice, and an 18+ year veteran of hospitality and hospitality technology solutions development, specializing in analytics and revenue management. Prior to joining SAS, Natalie was the director, product marketing for Minneapolis-based IDeaS Revenue Solutions, where she worked from 2000 to 2011. She is a frequent contributor to industry publications, speaker at industry conferences and is co-author of the SAS and Cornell Center for Hospitality Research blog, “The Analytic Hospitality Executive.”



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