Hospitality

Optimizing upgrade menus with multivariate demand modeling

Built a revenue management engine that prices and orders upgrade bundles based on simultaneous choice modeling.

Revenue uplift

+11%

Optimization latency

< 2s

Upgrade bundles evaluated

5K+

Overview

Hospitality leaders wanted to personalize upgrade menus while understanding how offers interact with one another.

We combined behavioral modeling and combinatorial optimization to balance customer delight with inventory constraints.

Traditional single-choice models ignored the fact that guests select multiple upgrades at once.
Manual merchandising could not react to dynamic inventory or demand fluctuations.
Analysts lacked a scalable way to quantify willingness to pay for bundled experiences.

Multivariate demand estimation
Modeled simultaneous selections to capture substitution and complementarity effects across upgrade options.
Behavioral segmentation
Segmented guests by loyalty, trip purpose, and booking context to tailor upgrade recommendations.
Real-time optimization services
Deployed APIs that evaluate thousands of upgrade bundles per request while respecting inventory limits and business rules.

Increased upgrade attachment rates through context-aware pricing and assortments.
Surfaced interpretable willingness-to-pay insights that guide product and marketing decisions.
Enabled enterprise-scale deployment with latency suitable for booking-path and pre-arrival touchpoints.

Capturing cross-item effects is essential when customers can buy multiple add-ons.
Segmentation and optimization must operate together to personalize complex menus.
API-first architectures translate sophisticated models into operational workflows.

Let's discuss how we can help you achieve similar results with a tailored approach for your organization.