Skip to content
The Algorithm
Case StudiesTravel & Tourism
Travel & Tourism
16 / 25

A Multi-Property Tourism Management System for Seasonal Operations — Built for the Chaos of Peak Season

Key Outcome
22%
revenue per available room increase via dynamic pricing
Team
12 engineers
Timeline
12 weeks
Industry
Travel & Tourism
01The Situation

A tourism operator managing multiple properties across a popular US destination region — lodges, guided tours, equipment rentals, and seasonal staff across locations that each had their own booking systems, their own pricing, and their own scheduling. Peak season meant 300% staffing surge, equipment moving between locations daily, and pricing that changed based on demand, weather, and availability.

02What Changed

Peak season. A fully booked holiday weekend. Two properties had double-sold the same block of rooms through different booking channels. Equipment reserved at Property A was physically at Property C. Three seasonal guides were scheduled at two locations simultaneously. The GM spent the weekend on the phone fixing problems instead of serving guests.

03Why The Algorithm

They needed a unified system that treated all properties as one operation — single source of truth for inventory, staff, equipment, and pricing.

04What We Built

Multi-property tourism management platform. Centralized inventory management — rooms, guides, equipment — across all properties with real-time availability. Dynamic pricing engine responding to demand signals, season, day-of-week, and manual overrides. Workforce scheduling across properties with shift management, skill matching, and overtime tracking for seasonal staff. Equipment logistics — tracking physical location, maintenance status, and reservation allocation. Unified booking engine feeding all third-party channels from a single availability source to eliminate double-booking.

05 — The Result

Zero double-bookings in the first peak season on the new platform. Revenue per available room increased 22% through dynamic pricing. Seasonal staff onboarding time reduced from 2 weeks to 3 days because the system guided them through operations rather than requiring institutional knowledge.

Facing a Similar Situation?

The first call is with a senior engineer.

Tell us the industry, the regulatory environment, and what needs to be built. We'll tell you if we've done it before, what it should cost, and how long it takes.

Talk to an EngineerAll Case Studies
Related Services, Platforms & Engagements
Service
Enterprise Modernization
Service
Data Engineering & Analytics
Platform
BookB — Appointment Management
Related Case Study
How We Built the Most Sophisticated Fishing Intelligence Platform in Alaska
Engage Us