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Club Quarters: The brand’s winning strategy to boost direct reservations

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Campaign Results

Time period: 3 months



increase in bookings


net incremental revenue

With a long-standing partnership between Club Quarters (CQ) and The Hotels Network (THN), the brand wanted to explore new opportunities to drive incremental bookings at a minimal cost of acquisition.

Both teams identified THN’s AI-machine learning algorithm, Predictive Personalization, and conducted an A/B test to validate the power and cost-effectiveness of the technology.

Test Design


Deciding on the offer

THN offered opportunities to target high-intent users (those most likely to book to up sell and drive more revenue) or target low-intent users (those least likely to book a room) to try to get them to convert. CQ chose to target the low-intent website visitors.


Targeting low-intent users

CQ created a discounted offer to attract website visitors to book. The offer was displayed as a website message that would only be visible to low-intent travelers based on data segmentation and would direct users to the exclusive hidden promotion upon clicking.


A/B test

Club Quarter’s focus was on low-intent guests. They targeted the 30% least likely to book. That means 70% of the site traffic would not see the discounted offer message. Furthermore, of the identified 30% of low-intent website visitors, the test was designed to only show the discount to half of them to determine if the messaging influenced the likelihood that they would book a stay at Club Quarters.


Moving Forward

THN's Predictive Personalization and A/B testing were used to increase low-intent user booking. After three months of implementation, it was observed that THN tools increased ROI, incremental revenue, and saved costs.


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