Case study · Hospitality
Hospitality Property Management Automation
Centralized booking sync, occupancy-based scheduling, and personalized guest messaging across 12 properties — double-bookings eliminated, staffing costs down 22%.
Client — Boutique hotel group, 12 properties
Stack — PMS API · TWILIO · N8N · HUBSPOT · AIRTABLE
/ Headline result
0
DOUBLE-BOOKING INCIDENTS
−22%
STAFFING COSTS
+37%
DIRECT BOOKINGS
4.2 → 4.8
GUEST SATISFACTION SCORE
/ 01 · The challenge
Twelve properties ran on a patchwork of systems that did not talk to each other. Bookings from OTAs, the website, and phone reservations were reconciled by hand, and double-bookings were a recurring, guest-facing embarrassment.
Staff scheduling was built on gut-feel occupancy guesses — overstaffed slow weeks, understaffed busy ones — and guest communication varied wildly by property, depending on who was at the desk.
/ 02 · The build
We implemented a centralized property management automation layer. Bookings synchronize across all channels and all twelve properties in real time, making double-bookings structurally impossible rather than merely less likely.
Staff schedules are generated from occupancy forecasts, matching labor to actual demand. Guest communications — pre-arrival details, in-stay touchpoints, post-stay follow-ups with direct-booking incentives — are personalized and automatic across the whole journey.
/ 03 · The results
Double-booking incidents went to zero. Staffing costs fell 22% as schedules tracked real occupancy instead of habit, with no decline in service coverage.
Direct bookings rose 37%, driven by the personalized post-stay marketing — revenue that previously paid OTA commissions now stays in house. Guest satisfaction climbed from 4.2 to 4.8 out of 5, with consistency across properties doing much of the lifting.
“
The automation system has revolutionized how we manage our properties. Our staff can focus on providing exceptional service instead of wrestling with systems, and our guests have noticed the difference.
Operations Director
Where we go from here
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