Welcome to the Add-on recommendation feature page. This page serves as a comprehensive guide to understanding and implementing this new addition to Maxxton Software.
🚀 Overview
The add-on recommendation model is designed to enhance the booking experience by offering personalized suggestions for add-ons, tailored to each unique reservation. Utilizing machine learning algorithms, this feature analyzes guest data to provide real-time, relevant add-on recommendations during the booking process.
On top of that, add-on recommendations serve as a potential upselling technique through the Template Editor and My Environment.
Table of contents
🕹 Enabling Add-on Recommendations
Reservation Manager
Web Manager
Template Engine
🛠 Implementation
🧐 Why do we recommend item X?
❓Questions
How it Works
The add-on recommendation model uses machine learning to personalize additions to a booking. It analyses and generalises past bookings and contextual information such as booking subjects, dates and length-of-stay, and accommodation information.
During the booking process, these recommendations are delivered in real-time, ensuring that guests receive relevant and up-to-date suggestions. The system continuously learns and adapts based on new bookings, refining its accuracy and personalization over time.
“Cold” Add-ons
Cold add-ons are extra services or products that no guest has ever booked before. These could be new offerings, but also updates of old products, like wine package 2023 getting renewed into wine package 2024. Because there’s no past booking data for these add-ons, it’s harder for the recommendation engine to know what type of guest wants them. We have two solutions for this problem: