Maple AI Consultants

AI consulting case studies by Joel & Nanz Inc.

Hospitality • 5 Properties

Case Study 18: Hotel Chain

Marketing-optimized case study for Canadian SMBs.

$118,000
Annual Savings
425%
ROI
3.1 months
Payback
Hospitality • 5 Properties
Industry Focus

Executive Summary

Dynamic Pricing: ML model adjusts room rates in real-time based on demand forecasts, local events, competitor pricing, weather, and booking patterns. Updates rates hourly across all OTAs.

Challenge

Solution

  • Dynamic Pricing: ML model adjusts room rates in real-time based on demand forecasts, local events, competitor pricing, weather, and booking patterns. Updates rates hourly across all OTAs.
  • Guest Service Chatbot: 24/7 AI assistant handles pre-arrival questions, check-in instructions, amenity information, local recommendations, and service requests via SMS and web.
  • Housekeeping Optimization: AI schedules housekeeping staff based on checkout times, room status, and predicted check-ins. Optimizes room assignment for fastest turnover.

Technical Stack

Key Metrics

14%
RevPAR increased 14% ($187k additional revenue)
68%
Front desk inquiries reduced 68%
31%
Housekeeping efficiency improved 31%
4.2
Guest satisfaction scores improved from 4.2 to 4.7
3
Front desk staffing reduced from 3 per property to 2 ($81k savings)

Implementation Timeline

9 weeks for full deployment across all properties

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