TRANSFORMING SERVICE QUALITY FOR A LEADING US HOSPITALITY BRAND
AI-DRIVEN QA AUTOMATION THAT IMPROVED CONSISTENCY, SPEED & GUEST SATISFACTION
- Background
- Problem
- Solutions
- Accomplishments
A major hospitality group in the United States, operating multiple properties across several states, was experiencing rapid growth in guest interactions.
Customer support operations included reservations, front-desk coordination, loyalty programs, and guest-relations handling across voice, chat, and email.
With rising occupancy and distributed support teams (both in-house and outsourced), maintaining consistent service quality became increasingly difficult.
The company partnered with Amanstra to design an AI-powered quality monitoring and coaching system that could scale with nationwide operations.
Manual QA processes were reviewing less than 5% of total interactions, leaving major gaps in quality oversight.
Service delivery varied significantly across locations, with 20–30% inconsistency in guest-experience scores between properties.
Feedback loops to agents were slow — often taking 5–7 days, limiting the impact of coaching.
Leadership lacked real-time visibility into guest-agent interactions, making it difficult to compare performance across properties or identify recurring service failures.
- Implemented Amanstra’s AI-driven QA engine to automatically evaluate thousands of voice, chat, and email interactions across all US properties.
- Deployed standardized scoring frameworks and coaching protocols to ensure uniform service evaluation nationwide.
- Launched centralized performance dashboards that provided property-level analytics, agent trends, error categories, and benchmarking insights.
- Automated feedback workflows ensured every agent received structured coaching within 24 hours, accelerating improvement cycles.
Increased QA coverage from 5% to nearly 30%, dramatically improving visibility across all guest interactions.
Reduced QA variability by 40–45%, standardizing guest experience across all locations and partner teams.
Cut feedback turnaround time from 5–7 days to under 24 hours, increasing coaching effectiveness and agent compliance.
Improved guest satisfaction metrics, with complaint recurrence dropping by 20% after deploying AI-led quality monitoring and coaching.
