CASE STUDY
An Online Hotel Booking Platform Boosts Conversions with Our Python-based, ML Solution
Industry | Hotel
Technology | MLOps
Location | US
The client is a US-based, online hotel booking service provider with operations in over 70 countries and providing room booking services for an easy, flexible traveling experience.
The client’s team involves technical personnel, trip planners for personal and commercial purposes, and creators responsible for providing a seamless booking and traveling experience for every visitor as per their needs and preferences.
The client is a US-based, online hotel booking service provider with operations in over 70 countries and providing room booking services for an easy, flexible traveling experience.
The client’s team involves technical personnel, trip planners for personal and commercial purposes, and creators responsible for providing a seamless booking and traveling experience for every visitor as per their needs and preferences.
Challenges
How We Did It
Key Outcomes
50%
Decrease in the website’s bounce rate
60%
of visitors who navigated to the website also booked a hotel room
Increased Conversions
3-star hotels were more popular, which resulted in increased conversions from a particular group of customers
Working with the client was a great learning experience for our Royal Cyber and its data science team. We started by understanding the client’s industry, nature of business, and core points that need a practical solution through MLOps. The case tempted the team to use Kubeflow on AWS and create an end-to-end, first-time implementation solution that extracted data and patterns that better acknowledged online visitors and onsite behavior and allowed the client to make better decisions accordingly.
A Python-based
ML solution
Audience
Case Study
Learn how Royal Cyber helped an online hotel booking platform understand visitors' needs, preferences, and patterns and increased their conversions through an ML algorithm.
