A common struggle for hotels is connecting with potential guests from the moment they arrive on the website. This was the case of The Thief, a member of Nordic Hotels & Resorts, a modern hideaway located in downtown Oslo that prides itself on delivering a unique experience to travelers. To further enhance the guest experience, The Thief partnered with THN to personalize its website and boost their direct reservations. 

Following the success of this collaboration, The Thief decided to explore new opportunities to drive incremental bookings at a minimal cost of acquisition. That's when they discovered Predictive Personalization.

A Bit More on Predictive Personalization

The product follows a two-step process that leverages machine learning and website campaigns to optimize the user experience and increase hotel revenue simultaneously. First, a predictive algorithm assigns a value score to each user in real-time based on their likelihood of completing a reservation. Then, the tool automatically personalizes the user experience by delivering the most suitable offers and content depending on the value score.

A/B Test Implementation

To activate Predictive Personalization, The Thief only needed to set the value score for low-intent users. THN's Campaign Manager took care of everything else, from creating the Smart Note message to controlling the campaign performance. An A/B test was set up to measure the effectiveness of the campaign, splitting low-intent users into two groups. Group A was shown the exclusive offer while it remained hidden for Group B. The Smart Note displayed a 15% off secret sale to encourage low-intent users to book.

After running the low-intent campaign for one month, The Thief achieved impressive results. The hyper-targeted 15% off discount generated additional reservations from users who most likely would not have booked otherwise. The promotional costs were significantly reduced as the algorithm identified those visitors with a high likelihood to book, and the discount wasn't shown to them.

Message displayed on the booking engine to low intent users

The campaign achieved a 24.79% uplift in conversion rate for low-intent users who saw the offer (Group A) versus the control group with no offer (Group B) throughout the month the A/B test was active. Only 30% of users that saw the campaign used the promo code when making a booking, meaning that 70% of the low-intent bookings were influenced by the campaign but booked using the public rates, resulting in saved promotional spend.

The Results

The Thief’s one month A/B test with Predictive Personalization achieved €26,000 savings in promotional spend, €30,545 in revenue from low-intent users, and 40 bookings were influenced by the campaign, resulting in significant revenue growth for the hotel.

The Thief's partnership with THN to implement Predictive Personalization was a resounding success. By leveraging this innovative technology, the hotel was able to create a hyper-targeted campaign that generated additional revenue, reduced promotional costs, and achieved a 24.79% uplift in conversion rate for low-intent users. The results of the A/B test demonstrate the power of Predictive Personalization and highlight the potential for hotels to achieve significant results by leveraging machine learning technology to optimize the user experience and increase revenue.

Want to know more? Read the entire case study here!