Case Study

Club Quarters' Winning Strategy to Boost Direct Reservations

Fiona Gillen

The Hotels Network · October 19, 2022

Club Quarters (CQ) turned to The Hotels Network (THN), with whom they have had a long-standing partnership, to leverage targeting and personalization capabilities, and price compare widgets. When that proved to be successful, CQ and THN explored various new opportunities to drive incremental bookings at a minimal cost of acquisition. Both teams identified THN’s AI-machine learning algorithm, Predictive Personalization. The Hotels Network team conducted A/B tests to validate the power and cost-effectiveness of the technology.

Why it matters: Club Quarters is a well-known and highly regarded hotel brand to business travelers for over 30 years; however, it has been lesser known to the non-business traveler. As business travel declined during the pandemic, Club Quarters' business model pivoted to attract leisure travelers who were visiting their destination cities. Part of the strategy was focused on website conversion, merchandising and personalization. During the pandemic, this group of people largely had to be reached digitally and then converted on their website.

Predictive Personalization harnesses machine learning techniques to predict user behavior and effortlessly optimize campaigns for each and every user.

Top 3 Core Objectives: Club Quarters was looking drive incremental bookings at a minimal cost of acquisition. They launched the AI-Machine learning campaign to:

Win low-intent bookings: Of the 30% of low-intent website visitors, half were shown a discount to determine if the messaging influenced the likelihood that they would book a stay at Club Quarters.
Acquisition at minimal cost: By evaluating the number of bookings made using the promo code, Club Quarters determined the cost-effectiveness of the technology.
Generate revenue: Through the A/B test, Club Quarters determined if utilizing THN's AI-Machine learning increased earnings by targeting low-intent bookers on their websites.

"THN has been the perfect partner in helping us expand our business model. The setup was simple, we saw a clear boost in both direct bookings and revenue, and now the tool is continuing to help us while allowing more time to focus on new strategy efforts."

Kristin Collett

Global Director of Digital Marketing

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👍 Global Director of Digital Marketing Kristin Collett said that utilizing AI-Machine learning produces results and frees up time to continue growing their direct channel strategy.:

"THN has been the perfect partner in helping us expand our business model. The setup was simple, we saw a clear boost in both direct bookings and revenue, and now the tool is continuing to help us while allowing more time to focus on new strategy efforts."

⚖️ The selection process: During their research process, Kristin Collett also researched Hotelchamp, and ultimately decided The Hotels Network was the best fit for them.

Global Director of Digital Marketing Kristin Collett said, about their decision: "We've had a long-standing relationship with THN using their personalization features and price comparison widget. The success of these tools made us confident in exploring new opportunities like THN's AI-Machine learning, which we haven’t found offered by any other provider, to further boost bookings on our website."

📈 The results: The test results were compelling for high ROI, incremental revenue, and saved costs. The ROI of the Predictive Personalization solution was well over 100%. Based on the data collected, Club Quarters decided to make the program available to all 16 hotels in the United States and the United Kingdom.

Club Quarters saw a 17.2% increase in bookings of people who saw the message as part of the test versus those who did not in the control group.
Only around 15% of users who saw the campaign used the promo code with their reservation, meaning those users had been influenced by the campaign but booked using the public rate without discount.
The net incremental revenue driven by the test of this solution was $41,746.