Building a real-time analytics system to monitor the speed of processing incoming requests in chats. The solution allowed the support team to significantly improve customer service metrics (SLA).
Challenge:
The client did not have an objective tool to track customer waiting time for a manager's response. This led to a "drop" in communication speed, customers left the chats, and management received delayed reports that did not reflect the actual performance of the department.
My solution:
I developed the architecture for automated data collection and processing through the Make (Integromat) platform:
API Integration: Set up a continuous data transmission channel from the client's chat platform. Each new activity in the chat becomes a point for real-time analysis.
Mathematical Logic: Implemented scripts for automatically calculating the time from the moment a customer reaches out to the first response from a manager.
Multi-level Alert/Warning System: Configured threshold logic. If the waiting time approaches critical levels, the system automatically sends a warning, allowing the manager to intervene before the customer loses patience.
Performance Dashboard: All data is aggregated into reports that allow assessing the effectiveness of each manager individually and the department as a whole.
Technology Stack:
Make (Integromat)
Webhooks & API Integration
Google Sheets (Data processing & Storage)
Telegram/Email Notification System
Result:
Measurability of service: Management received transparent metrics of response speed (SLA) in real-time.
Increased response speed: Thanks to the alert system, the team began to respond to requests faster, minimizing customer churn.
Objective assessment: The system provided a basis for fair employee motivation based on their actual performance.
Challenge:
The client did not have an objective tool to track customer waiting time for a manager's response. This led to a "drop" in communication speed, customers left the chats, and management received delayed reports that did not reflect the actual performance of the department.
My solution:
I developed the architecture for automated data collection and processing through the Make (Integromat) platform:
API Integration: Set up a continuous data transmission channel from the client's chat platform. Each new activity in the chat becomes a point for real-time analysis.
Mathematical Logic: Implemented scripts for automatically calculating the time from the moment a customer reaches out to the first response from a manager.
Multi-level Alert/Warning System: Configured threshold logic. If the waiting time approaches critical levels, the system automatically sends a warning, allowing the manager to intervene before the customer loses patience.
Performance Dashboard: All data is aggregated into reports that allow assessing the effectiveness of each manager individually and the department as a whole.
Technology Stack:
Make (Integromat)
Webhooks & API Integration
Google Sheets (Data processing & Storage)
Telegram/Email Notification System
Result:
Measurability of service: Management received transparent metrics of response speed (SLA) in real-time.
Increased response speed: Thanks to the alert system, the team began to respond to requests faster, minimizing customer churn.
Objective assessment: The system provided a basis for fair employee motivation based on their actual performance.