Project context
Introducing a global quality management system for customer interactions
Key areas
Quality management, monitoring criteria, voice recording, AI-supported monitoring, feedback processes, international scaling
The role of grünlicht
Conception, project management, process design, stakeholder management, training and rollout support
Starting situation
A globally operating specialist tool manufacturer aimed to systematically improve the quality of its customer service interactions. Previously, there were no uniform standards for conversation management, written communication, or monitoring within the international service teams.
The aim was to implement a scalable quality management system that works at all global sites, takes cultural differences into account, and is supported by technical solutions.
The main challenges were:
- Different quality standards in the regions
- Lack of organisational structures for quality assurance
- High demand for transparency and measurable criteria for customer dialogues
- Integration of new technologies such as voice recording and AI-powered monitoring
- Our Approach
grünlicht developed a holistic quality management system for all customer service interactions – from strategy to international roll-out.
The central building blocks were:
- Organisational Development
Establishing clear roles and responsibilities for quality management and quality assessors - Process design
Development of unified QM processes, including documentation, feedback, and escalation routes - Monitoring criteria:
Definition of evaluation matrices for telephony, chat, email and social media with a focus on professionalism, service orientation and customer experience - Technical Framework Conditions:
Introduction of voice recording systems and integration of AI-powered monitoring for telephony and written correspondence - Consequence and Development Model
Developing a fair and transparent employee appraisal and development model - Training courses
Training and certification of Quality Assessors worldwide - Change Management:
Communication and engagement of all stakeholders, as well as regular workshops with management and employees.
Measures in detail
Voice recording systems have been implemented in the project to make relevant conversations available for quality analysis. Complementary to this, AI-powered analyses have been introduced to select samples more efficiently and to make patterns in telephone and chat interactions visible.
Standards and guidelines for email and messaging have been developed for written communication. Additionally, a reporting structure with dashboards for metrics such as customer satisfaction, error rates, and process adherence has been created.
The new quality management system was initially piloted in Europe and subsequently extended to North and South America, as well as China. In each region, feedback mechanisms have been established to provide agents and managers with structured feedback and coaching recommendations.
Regular quality circles ensured that criteria were further developed, cultural specificities were taken into account, and market requirements were incorporated.
Results
- Uniform quality standards:
Binding standards for customer interactions have been established for all locations - Transparent evaluation
Customer dialogues can be transparently and fairly evaluated across all channels. - Increased customer satisfaction:
The quality of service has demonstrably improved and is evident in both internal and external surveys. - Efficiency gains through AI
AI-powered analyses reduced manual review efforts and made patterns more quickly identifiable. - Sustainable learning culture
Agents receive structured feedback and individual development impulses - Global Scale
The system has been successfully rolled out in Europe, North and South America, and China. - Greater acceptance
Employees appreciate the fairness and transparency of the appraisal model
Key Learnings
- Quality management requires clear structures, defined processes, and technological support.
- Global standards must be culturally adaptable without losing their uniformity.
- AI-powered monitoring tools increase efficiency, but only achieve their full effect through human evaluation and good feedback processes.
- A consequence and development model not only increases quality, but also trust and acceptance.