Part 3 - How Does an AI-enabled Service Recommendation System Work in Hotel Business?

The previous article discussed how to conceptualize an AI-based recommendation system. An AI-based recommendation system is just one of the AI-enabled applications that can be built for a Sem-luxury chain of hotels. In this article, we will show how this recommendation system works. 

The recommendation system for a semi-luxury hotel chain operates by dynamically generating personalized suggestions for guests based on their booking details, historical data, preferences, and contextual information like local events or weather. The system continuously learns from guest feedback to improve its recommendations.

When a guest makes a booking, the system collects and preprocesses the guest’s data, updates the guest’s profile with any new information, and analyzes this data to identify preferences and potential interests.

Considering the context of the guest’s stay (such as the time of year and local happenings), it generates personalized recommendations for services, amenities, or activities. These recommendations are then communicated to the guest through various channels. Afterward, the system collects feedback to refine future recommendations, creating a continuous improvement cycle.

Vijay Gonzalez, a guest who has previously stayed at the hotel chain, makes a new booking for a weekend stay during spring break.

The system processes Vijay’s booking details and retrieves his historical data, noting his preferences. The system figures he has a preference for outdoor activities and fine dining.

Vijay’s profile is updated with the new booking details and any changes in preferences (e.g., he now also shows interest in spa services).

The system analyzes Vijay’s updated profile and identifies that guests like him often enjoy guided nature hikes, spa relaxation packages, and dining at the hotel’s rooftop restaurant which offers a spring menu.

The system checks the local event calendar and weather forecast. It finds that the weather is expected to be pleasant, and a local food festival is happening over the weekend.

Based on Vijay’s interests, the system generates personalized recommendations: a guided nature hike, a special spa relaxation package, a reservation at the rooftop restaurant to try the spring menu, and information about the food festival.

These recommendations are emailed to Vijay and published in the hotel’s mobile app, inviting him to book these experiences in advance.

After his stay, Vijay is provided feedback through the app, praising the hike and the dining experiences, which he found perfectly matched his interests.

The system updates Vijay’s profile with his latest feedback and preferences for future stays. It also adjusts its recommendation algorithms based on the feedback to improve suggestions for Vijay and similar guests in the future.

Here’s how the process unfolds behind the scenes when Vijay makes the booking.

Step 1: Data Collection

As soon as a booking is initiated, the system collects initial data about the guest. This could include information from the current booking details (e.g., dates, room type) and any available historical data on past stays, preferences, and feedback if the guest has stayed at the hotel before.

The data is gathered from the hotel’s reservation system, CRM, and possibly from third-party sources if the guest has interacted with the hotel’s social media or review platforms.

Step 2: Data Preprocessing

The collected data is then cleaned and preprocessed. This involves standardizing data formats, filling in missing values where possible, and integrating new booking data with existing guest profiles.

The preprocessed data is now ready for deeper analysis to identify preferences and potential recommendation opportunities.

Step 3: User Profiling / Update

The guest’s profile is updated or created (for new guests) using the latest data. This profile includes preferences, past behaviors, and any specific requests or interests shown during the current booking process.

The updated profile is stored and made accessible to the analysis and recommendation modules.

Step 4: Analysis and Insights

The system analyzes the updated guest profile alongside broader data patterns (e.g., preferences of similar guests, and trends based on time of year). Machine learning models identify preferences and potential interests.

Insights derived from this analysis are used as input for building personalized recommendations.

Step 5: Contextualization

The system considers the current context—like local events, weather forecasts, and special promotions—relevant to the guest’s booking dates.

This contextual data is integrated with the guest’s profile and analysis insights to tailor recommendations further.

Step 6: Recommendation Generation

Using the guest’s updated profile, analysis insights, and contextual information, the recommendation engine generates personalized recommendations. This could include specific room upgrades, special offers, activity suggestions, or dining experiences.

These recommendations are prepared for delivery to the guest.

Step 7: Delivery

The personalized recommendations are communicated to the guest through the chosen channels, such as email, SMS, or directly through the hotel’s booking interface.

The delivery method is selected based on the guest’s preferences or the perceived urgency and relevance of the recommendations.

Step 8: Feedback Collection

After the recommendations are delivered (and possibly after the guest’s stay), feedback on the recommendations and the overall experience is collected.

This feedback is fed back into the system, updating the guest’s profile and informing future recommendations.

Step9: Continuous Improvement Loop

The feedback and any new data collected during the guest’s stay are used to refine the recommendation algorithms, update profiles, and improve the overall accuracy and relevance of future recommendations.

This creates a feedback loop that continually enhances the system’s performance, making recommendations more personalized and effective over time.