Booking Rate, 75% higher than Meta’s benchmark
Cost Per Booking, 20% lower than Meta’s benchmark
A multinational hotel chain with over 7,000 properties globally wanted to drive high-intent travelers to book its hotels located in 10 key DMAs across the South and Midwest, during the fall months. These regions exhibit high levels of seasonal travel activity.
The client initially planned to run the campaigns on Meta and rely entirely on the platform’s pre-built audiences, but recognized the need for additional interest-based data enrichment to more effectively reach high-intent travelers planning to visit these destinations.
By analyzing real-time consumer engagement with popular local attractions, Predactiv created custom audiences for each of the 10 DMAs. These insights uncovered distinct regional interests, enabling more precise and market-specific audience refinement.
The audiences were then layered onto the client’s Meta campaigns across both mobile and desktop, enhancing Meta’s targeting with our proprietary interest and intent data for greater precision and relevance.
Even amid rising competition and media costs, the campaigns significantly outperformed expectations, achieving a 0.20% booking rate (75% higher than Meta’s benchmark) and a $9.94 cost per booking (20% lower than Meta’s benchmarks).
Return on Ad Spend (ROAS), nearly 5x the benchmark
Return on Ad Spend (ROAS), nearly 5x the benchmark
Cost Per Action/Conversion (CPA), 65% lower than the client’s $25 CPA goal and 83% lower than the $50 CPA average across prior campaigns
Cost Per Action/Conversion (CPA), 65% lower than the client’s $25 CPA goal and 83% lower than the $50 CPA average across prior campaigns
Revenue-generating models, enabled by Predactiv data
Lift in models compared to a random data sample
Improvement in model performance by adding Predactiv data to the marketing consultancy’s models
Increase in model usage by the consultancy’s clients, driving significant revenue growth
Revenue-generating models, enabled by Predactiv data
Lift in models compared to a random data sample
Improvement in model performance by adding Predactiv data to the marketing consultancy’s models
Increase in model usage by the consultancy’s clients, driving significant revenue growth
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