Impact of NLP on the Hospitality Industry

As with most industries, the hospitality sector is utilizing natural language processing, helping to enhance the customer experience, to optimize operations and to drive revenue. 

Whilst some of the uses are customer facing, many uses of NLP are behind the scenes, helping hospitality companies to deliver a more efficient service. 

Here are just a few of the ways NLP is impacting the global hospitality industry.

Personalizing the guest experience

AI-powered virtual assistants are one of the most commonly adopted use cases of natural language processing. These often custom built chatbots, commonly seen on websites and applications, use NLP to answer guest questions, enquiries and to provide recommendations. 

Virtual assistants elicit numerous benefits with one of the primary benefits compared to traditional customer service methods is that the chatbots can provide 24/7, instant support, helping to improve the guest experience.

Hilton’s, “Connie”, uses NLP to provide an AI concierge service to customers. Launched in 2016, Connie was powered by IBM’s Watson, named after the founder of IBM. The AI system famously won a round of Jeopardy!, demonstrating the ability of the AI to understand, analyse and answer people’s questions. 

Hilton’s Connie was trained on a database of travel information from the platform WayBlazer. In the words of Rob High, the chief technology officer of Watson, the AI “helps Connie understand and respond naturally to the needs and interests of Hilton’s guests - which is an experience that’s particularly powerful in a hospitality setting, where it can lead to deeper guest engagement”.

Other hospitality industry uses of virtual assistants include the ones deployed on the Marriott Bonvoy App and the booking.com website.

Voice-activated room controls

Many of us use voice-activated devices at home such as Alexa. Hotel chains such as Fairmont and Best Western have rolled out hospitality specific voice assistants, such as the use of Google Nests. 

From the Nest Hub smart display, hotels can tailor their guest experience to address FAQs and commonly requested services. For example, guests can ask “Hey Google, what time does the pool close?” or “Hey Google, ask my hotel for extra towels”. 

Certain hotels have integrated Google Assistant into the check-in process, ensuring guests don’t need to waste time standing in line for the receptionist. This is also helping to break down the language barrier between international guests and the customer service team, ensuring all guests can enjoy a pleasant experience. 

Airbnb automatically translates and responds to guest-host communications in different languages, helping to facilitate international conversations in real time. 

As well as the personalised services, these voice-activated devices can provide standardised services such as reading the news, playing music and finding answers. Voice assistants use NLP-driven speech-to-text/text-to-speech tech to understand and respond to human speech, before prompting tasks based on the voice command.

Elsewhere in the hospitality industry, McDonalds recently ended their trial of artificial intelligence chatbots at their drive-thrus. The trial, which came to a close in June 2024, found that the system proved too inaccurate and expensive in its current format to be rolled out globally. 

However, given the pace of developments within the NLP and wider AI industry, it will not be long before cost-effective, accurate voice assistants for drive-thrus become commonplace.

Forecasting demand

Dynamic pricing was the subject of many headlines in 2024, most notably for the role that dynamic pricing played in Ticketmaster’s selling of Oasis tour tickets. 

Dynamic pricing enables companies to maximize their profits and sales by adjusting prices based on demand and occupancy. 

Within hospitality, hotel room prices fluctuate significantly, often utilizing NLP to analyze social media trends, customer enquiries and reviews to adjust room prices dynamically.

Menu recommendations

Whether you’re dining somewhere new or fancy a change of dish from your favourite restaurant, choosing from the menu can sometimes be a daunting task. 

Menu sentiment analysis, such as the AI assistant used by OpenTable, will make recommendations of restaurants and menus based on the preferences and previous reviews of the user. For example, if the user had previously expressed a dislike of traditional cuisine, they will be recommended towards more modern offerings. 

Sam Cooper, the founder of findarestaurant.co.uk tips this usage of NLP to become most commonplace within the hospitality industry, “Personalisation is a prevalent trend within the contemporary hospitality industry. Operators are keen to find new and novel ways to curate a personal experience for their customers”. 

KFC has been leveraging natural language processing within their ordering process since 2016. The “Du Mi” initiative has the ability to understand customers’ different regional accents and to take orders. Steven Li, of KFC Yum China, explains that  “a machine will remind you of an unused coupon, predict your orders based on your transaction data, offering better services than a human waiter".

Fraud prevention

Reviews are of paramount importance in helping customers decide where to take their custom. Fake reviews seek to manipulate the trust that customers put in the opinions of others. This deception harms genuine businesses that offer a fantastic service, and instead promotes nefarious businesses who seek to cut corners to promote themselves.

Luckily, NLP solutions can be used to detect fraudulent reviews by spotting patterns of suspicious activities. Fake reviews often contain extreme positivity or negativity, far beyond what a genuine reviewer would comment. For example, a review that a restaurant was “Horrible! Worst restaurant ever!” would be detected for displaying excess emotion. 

Depending on the workflow of the system, this type of review would either be automatically hidden, or flagged for human review. 

Similarly, fake reviews can be identified through linguistic pattern analysis. Real reviews are often nuanced and varied, whereas fake ones use generic adjectives, lack specific details or use unnatural phrases. 

Hospitality platforms such as booking.com, Expedia and Airbnb all use NLP to detect inconsistencies between review content and actual booking data in their efforts to identify and remove fake reviews. For example, if a review mentions that a hotel had a poor pool, yet the hotel in question does not have a pool, this will automatically be flagged as being fake.

Final thoughts

Just as hospitality customers are seeking personalisation, your hospitality business should be seeking a custom NLP solution. 

Off the shelf, turnkey examples are excellent examples of what NLP can do, but lack the contextual nuance to be of specific value for your business. 

On the other hand, custom NLP solutions can be trained to offer specific, personalized experiences for your guests. Not sure how to train your own natural language processing tool? 

That’s where we come in. By creating tailor-made tools, NetGeist creates tools that tackle textual challenges by automating, processing, and summarizing information. 

Let us simplify your textual tasks with a unique solution, tailored specifically just for you.