Using machine learning to segment hotel guests for a global hospitality brand.
Global Hospitality Brand
  • ClientConfidential
  • UrlNA
Market Intelligence

The Challenge

A global hospitality brand approached Polyculture to improve its promotion strategy and raise its room rates without risking its occupancy and stable revenue.

The Solution

Polyculture developed a comprehensive revenue & promotion strategy that was rooted in studying the current identity & behavior of the Brand’s existing guests. Leveraging the latest analytical techniques in machine learning, Polyculture developed a statistically-robust guest segmentation based on over 27 variables. The analysis uncovered 6 core guest segments and gave the client valuable information on each segment’s behavior – ranging from their propensity to spend on F&B to booking lead times.

The Result

The analysis yielded insights that enabled the client to optimize its package & promotion strategy by targeting guests with long-lead times while preserving high-spenders who were prone to book last minute and willing to pay premium rates.

To protect the privacy of our clients and their customers, Polyculture has randomized all data in any samples of its work and rendered it purely illustrative.